---
title: "Appendix M: Bibliography – The Life Sciences AI Handbook"
source: "https://lifesciencesaihandbook.com/bibliography.html"
author: "Bryan Tegomoh, MD, MPH"
last_updated: "2026-11-04"
doi: "10.5281/zenodo.22073135"
license: CC BY 4.0
generated-by: handbook-markdown-export
---

# Appendix M: Bibliography

Sources are listed in chapter order. Follow **Cited in** to return to the source’s context. Recommended reading is listed separately.

Entries retain the citation label when publication details are incomplete. Inclusion in this bibliography does not establish that a source supports every claim made about it.

Search author, title or journal Chapter All chapters Welcome Start Here Preface Executive Summary How to Read This Handbook Scope and Limitations AI for the Life Sciences History of AI in the Life Sciences Biological Data Infrastructure Knowledge Graphs and Literature AI Foundation Models for Biology Evaluation Principles for Life Sciences AI Protein Structure Prediction Protein Design and Engineering Antibody and Biologic Design Nucleic Acid and Genome Models Variant Effect Prediction Single-Cell Foundation Models Spatial Omics and Tissue Models Cell Painting and Image-Based Phenotyping Histopathology AI Microscopy and Cryo-EM AI Perturbation Prediction and Virtual Cells Microbiome and Multi-Omics AI Systems Biology and Multiscale Modeling Neuroscience AI and Brain Foundation Models Aging and Longevity Biology AI Plant, Crop, and Agricultural AI Environmental and Ecological AI Virtual Organisms and Digital Biology Target Identification and Prioritization Small Molecule Generation and ADMET Chemical Biology and Target Engagement Drug Repurposing and Combination Therapy mRNA, RNA, and Vaccine Design Cell and Gene Therapy AI Diagnostics and Biomarker Translation Clinical Trial AI for Translational Research Real-World Evidence and Biomarker AI Translational Evidence and Failure Modes Self-Driving Laboratories Robotic Lab Automation and Cloud Labs Synthetic Biology Design Tools AI for Biomanufacturing Agentic Science Workflows Toolkit for AI-Augmented Bio Research Practical Evaluation Workflows and Worksheets Benchmarks for Bio AI Reproducibility and Open Science Information Hazards in Capability Research Workforce, Compute, and Institutional Readiness Emerging Frontiers in AI for the Life Sciences Advisory Reader Feedback Support the Life Sciences AI Handbook References Changelog Quick Reference: All Chapter Summaries Case Studies Glossary Model and Dataset Index Course Syllabus Template Teaching with the Life Sciences AI Handbook How to Cite This Handbook Editions and Versions Guidance for AI Agents License

## Search results

- [100 Days Mission](https://cepi.net/100-days-mission)Cited in: [mRNA, RNA, and Vaccine Design](therapeutics/vaccines.html#sec-vaccines-countermeasure-readiness)
- [AI-Orchestrated De Novo Discovery of a Therapeutic Anti-IgE VHH in 16 Days](https://divergencelabs.com/publications/ige-preprint.pdf)Abramovitz et al. 2026. Divergence Labs PreprintpreprintCited in: [Agentic Science Workflows](automation/agentic-workflows.html#agentic-science-workflows-demonstrated)
- [Accurate structure prediction of biomolecular interactions with AlphaFold 3](https://doi.org/10.1038/s41586-024-07487-w)Josh Abramson; Jonas Adler; Jack Dunger; et al. 2024. Naturejournal articleCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways); [Scope and Limitations](scope-limitations.html#sec-scope-sources); [AI for the Life Sciences](foundations/ai-for-life-sciences.html#sec-ai-for-life-sciences-introduction); [History of AI in the Life Sciences (passage 1)](foundations/history.html#sec-history-timeline); [History of AI in the Life Sciences (passage 2)](foundations/history.html#sec-history-generative); [Foundation Models for Biology](foundations/foundation-models.html#sec-fm-structures); [Evaluation Principles for Life Sciences AI](foundations/evaluation-principles.html#sec-evaluation-calibration); [Protein Structure Prediction (passage 1)](molecular/protein-structure.html); [Protein Structure Prediction (passage 2)](molecular/protein-structure.html#sec-protein-structure-introduction); [Protein Structure Prediction (passage 3)](molecular/protein-structure.html#complex-and-interaction-prediction); [Protein Structure Prediction (passage 4)](molecular/protein-structure.html#sec-protein-structure-alphafold3-open-access); [Protein Structure Prediction (passage 5)](molecular/protein-structure.html#sec-protein-structure-common-questions); [Protein Design and Engineering](molecular/protein-design.html#sec-protein-design-rfantibody); [Antibody and Biologic Design (passage 1)](molecular/antibodies.html#antibody-biologic-design-introduction); [Antibody and Biologic Design (passage 2)](molecular/antibodies.html#antibody-biologic-design-demonstrated); [mRNA, RNA, and Vaccine Design (passage 1)](therapeutics/vaccines.html#mrna-rna-vaccine-design-introduction); [mRNA, RNA, and Vaccine Design (passage 2)](therapeutics/vaccines.html#mrna-rna-vaccine-design-demonstrated); [Toolkit for AI-Augmented Bio Research](practical/toolkit.html#sec-toolkit-inventory); [Information Hazards in Capability Research](governance/information-hazards.html#information-hazards-demonstrated); [Emerging Frontiers in AI for the Life Sciences](future/emerging.html#sec-emerging-multimodal); [Quick Reference: All Chapter Summaries (passage 1)](appendices/tldr-compilation.html#summary-protein-structure); [Quick Reference: All Chapter Summaries (passage 2)](appendices/tldr-compilation.html#summary-reproducibility); [Case Studies](appendices/case-studies.html#sec-case-alphafold3-access); [Glossary (passage 1)](appendices/glossary.html#alphafold); [Glossary (passage 2)](appendices/glossary.html#pae); [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-structure)Recommended in: [References](references.html#sec-references-structure)
- [Addendum: Accurate structure prediction of biomolecular interactions with AlphaFold 3](https://doi.org/10.1038/s41586-024-08416-7)Josh Abramson; Jonas Adler; Jack Dunger; et al. 2024. Naturejournal articleCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways); [AI for the Life Sciences](foundations/ai-for-life-sciences.html#sec-ai-for-life-sciences-introduction); [History of AI in the Life Sciences](foundations/history.html#sec-history-generative); [Protein Structure Prediction](molecular/protein-structure.html#sec-protein-structure-alphafold3-open-access); [Toolkit for AI-Augmented Bio Research](practical/toolkit.html#sec-toolkit-inventory); [Quick Reference: All Chapter Summaries (passage 1)](appendices/tldr-compilation.html#summary-protein-structure); [Quick Reference: All Chapter Summaries (passage 2)](appendices/tldr-compilation.html#summary-reproducibility); [Case Studies](appendices/case-studies.html#sec-case-alphafold3-access); [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-structure)
- [AI and the Future of Medical Countermeasures to Protect Against Biological Threats](https://doi.org/10.1093/ofid/ofag108)Amesh A Adalja; Jaspreet Pannu; Thomas V Inglesby. 2026. Open Forum Infectious Diseasesjournal articleCited in: [mRNA, RNA, and Vaccine Design](therapeutics/vaccines.html#sec-vaccines-countermeasure-readiness)
- [A Multiplexed Single-Cell CRISPR Screening Platform Enables Systematic Dissection of the Unfolded Protein Response](https://doi.org/10.1016/j.cell.2016.11.048)Britt Adamson; Thomas M. Norman; Marco Jost; et al. 2016. Celljournal articleCited in: [Perturbation Prediction and Virtual Cells](cells/virtual-cells.html#perturbation-virtual-cells-introduction)Recommended in: [References](references.html#sec-references-perturbation)
- [Will AI ever win its own Nobel? Some predict a prize-worthy science discovery soon](https://doi.org/10.1038/d41586-025-03223-0)Jenna Ahart. 2025. Naturenews featureCited in: [Self-Driving Laboratories](automation/self-driving-labs.html#sec-sdl-nobel-turing); [References](references.html#sec-references-automation)
- [Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines](https://doi.org/10.1038/s41592-025-02772-6)Constantin Ahlmann-Eltze; Wolfgang Huber; Simon Anders. 2025. Nature Methodsjournal articleCited in: [Preface](preface.html#sec-preface-why); [Executive Summary](executive-summary.html#sec-executive-summary-takeaways); [Foundation Models for Biology](foundations/foundation-models.html#sec-fm-cells); [Single-Cell Foundation Models (passage 1)](cells/single-cell-models.html); [Single-Cell Foundation Models (passage 2)](cells/single-cell-models.html#scfm-introduction); [Perturbation Prediction and Virtual Cells (passage 1)](cells/virtual-cells.html); [Perturbation Prediction and Virtual Cells (passage 2)](cells/virtual-cells.html#perturbation-virtual-cells-introduction); [Perturbation Prediction and Virtual Cells (passage 3)](cells/virtual-cells.html#perturbation-virtual-cells-demonstrated); [Translational Evidence and Failure Modes (passage 1)](therapeutics/translation-failures.html#translational-evidence-failure-modes-introduction); [Translational Evidence and Failure Modes (passage 2)](therapeutics/translation-failures.html#translational-evidence-failure-modes-demonstrated); [Quick Reference: All Chapter Summaries (passage 1)](appendices/tldr-compilation.html#summary-single-cell-models); [Quick Reference: All Chapter Summaries (passage 2)](appendices/tldr-compilation.html#summary-virtual-cells); [Quick Reference: All Chapter Summaries (passage 3)](appendices/tldr-compilation.html#summary-translation-failures); [Case Studies (passage 1)](appendices/case-studies.html#sec-case-single-cell-perturbation); [Case Studies (passage 2)](appendices/case-studies.html#sec-case-single-cell-foundation-model); [Glossary](appendices/glossary.html#perturbation-prediction); [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-critical-evaluations); [Teaching with the Life Sciences AI Handbook](teaching.html#sec-teaching-starter-papers)Recommended in: [References](references.html#sec-references-cells)
- [Ahmad et al., 2022, preprint](https://arxiv.org/abs/2209.01712)Cited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-generative)Recommended in: [References](references.html#sec-references-small-molecules)
- [SCENIC: single-cell regulatory network inference and clustering](https://doi.org/10.1038/nmeth.4463)Sara Aibar; Carmen Bravo González-Blas; Thomas Moerman; et al. 2017. Nature Methodsjournal articleCited in: [Systems Biology and Multiscale Modeling (passage 1)](cells/systems-biology.html#systems-biology-introduction); [Systems Biology and Multiscale Modeling (passage 2)](cells/systems-biology.html#systems-biology-grns); [Systems Biology and Multiscale Modeling (passage 3)](cells/systems-biology.html#systems-biology-models-datasets-benchmarks)
- [Aiforia, 2026](https://www.aiforia.com/)Cited in: [Histopathology AI](cells/histopathology.html#histopathology-industry)Recommended in: [References](references.html#sec-references-imaging)
- [Enhancing randomized clinical trials with digital twins](https://doi.org/10.1038/s41540-025-00592-0)Hossein Akbarialiabad; Amirmohammad Pasdar; Dédée F. Murrell; et al. 2025. npj Systems Biology and Applicationsjournal articleCited in: [Virtual Organisms and Digital Biology](organisms/virtual-organisms.html#virtual-organisms-pbpk-qsp)Recommended in: [References](references.html#sec-references-systems-biology)
- [A structural biology community assessment of AlphaFold2 applications](https://doi.org/10.1038/s41594-022-00849-w)Mehmet Akdel; Douglas E. V. Pires; Eduard Porta Pardo; et al. 2022. Nature Structural & Molecular Biologyjournal articleCited in: [History of AI in the Life Sciences](foundations/history.html#sec-history-generative); [Protein Structure Prediction](molecular/protein-structure.html#plddt-predicted-local-distance-difference-test)
- [Automated synthetic cell-based screening for designed proteins with emergent functions](https://doi.org/10.1038/s41467-026-76787-8)Kareem Al Nahas; Béla P. Frohn; Aleksandra Šakanović; et al. 2026. Nature Communicationsjournal articleCited in: [Synthetic Biology Design Tools](automation/synbio-tools.html#synthetic-biology-design-tools-demonstrated)
- [Protein generation with evolutionary diffusion: sequence is all you need](https://doi.org/10.1101/2023.09.11.556673)Sarah Alamdari; Nitya Thakkar; Rianne van den Berg; et al. 2023preprintCited in: [Protein Design and Engineering](molecular/protein-design.html#sec-protein-design-generative); [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-design)Recommended in: [References](references.html#sec-references-design)
- [CellVoyager: AI CompBio agent generates new insights by autonomously analyzing biological data](https://doi.org/10.1038/s41592-026-03029-6)Samuel Alber; Bowen Chen; Eric Sun; et al. 2026. Nature Methodsjournal articleCited in: [Agentic Science Workflows (passage 1)](automation/agentic-workflows.html#agentic-science-workflows-introduction); [Agentic Science Workflows (passage 2)](automation/agentic-workflows.html#agentic-science-workflows-demonstrated); [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-agentic-workflows); [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-automation)Recommended in: [References](references.html#sec-references-automation)
- [Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning](https://doi.org/10.1038/nbt.3300)Babak Alipanahi; Andrew Delong; Matthew T Weirauch; et al. 2015. Nature Biotechnologyjournal articleCited in: [History of AI in the Life Sciences (passage 1)](foundations/history.html#sec-history-timeline); [History of AI in the Life Sciences (passage 2)](foundations/history.html#sec-history-deep-learning)
- [AlphaFold Protein Structure Database, 2026](https://alphafold.ebi.ac.uk/about)Cited in: [Biological Data Infrastructure](foundations/data-infrastructure.html#data-infrastructure-demonstrated); [Reproducibility and Open Science](governance/reproducibility.html#reproducibility-open-science-demonstrated); [Case Studies](appendices/case-studies.html#sec-case-structure-prediction); [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-infrastructure)Recommended in: [References](references.html#sec-references-infrastructure)
- [Basic local alignment search tool](https://doi.org/10.1016/S0022-2836%2805%2980360-2)Stephen F. Altschul; Warren Gish; Webb Miller; et al. 1990. Journal of Molecular Biologyjournal articleCited in: [History of AI in the Life Sciences (passage 1)](foundations/history.html#sec-history-timeline); [History of AI in the Life Sciences (passage 2)](foundations/history.html#sec-history-algorithmic); [Toolkit for AI-Augmented Bio Research](practical/toolkit.html#sec-toolkit-inventory)
- [Claude discovers a novel enzyme system](https://www.anthropic.com/news/claude-discovers-novel-enzyme-system)Anthropic. 2026. AnthropicwebpageCited in: [Agentic Science Workflows](automation/agentic-workflows.html#agentic-science-workflows-demonstrated)
- [Early progress on accelerating drug development with Claude](https://www.anthropic.com/research/early-progress-drug-development)Anthropic. 2026. Anthropic Researchcompany research postCited in: [Agentic Science Workflows](automation/agentic-workflows.html#agentic-science-workflows-demonstrated)
- [System Card: Claude Opus 5.5](https://www-cdn.anthropic.com/fc1b44717c85dc068bc6ba5024219938094694bd/Claude%20Opus%205.5%20System%20Card.pdf)Anthropic. 2026. AnthropicSystem cardCited in: [Benchmarks for Bio AI](governance/benchmarks.html#sec-benchmarks-dangerous-capability)
- [Anthropic, 2026](https://anthropic.com/responsible-scaling-policy/rsp-v3-0)Cited in: [Foundation Models for Biology](foundations/foundation-models.html#fm-practice-notes)
- [Anthropic, 2026](https://www.anthropic.com/news/model-hardware-standard-research-preview)Cited in: [Self-Driving Laboratories](automation/self-driving-labs.html#sec-sdl-chemistry)
- [Anthropic, 2026, system card](https://www.anthropic.com/claude-opus-5-system-card)Cited in: [Benchmarks for Bio AI](governance/benchmarks.html#sec-benchmarks-dangerous-capability)
- [Anthropic, August 2026](https://www.anthropic.com/research/Claude-accelerates-protein-design)Cited in: [Protein Design and Engineering](molecular/protein-design.html#sec-protein-design-generative)Recommended in: [References (passage 1)](references.html#sec-references-design); [References (passage 2)](references.html#sec-references-frontier-labs)
- [Anthropic, Claude Fable and Mythos 5.1 overview, 2026](https://www.anthropic.com/claude-fable-and-mythos-5-1)Cited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#sec-ai-for-life-sciences-introduction)
- [Anthropic, June 2026](https://www.anthropic.com/news/claude-science-ai-workbench)Cited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#sec-ai-for-life-sciences-introduction); [Toolkit for AI-Augmented Bio Research](practical/toolkit.html#sec-toolkit-inventory); [Workforce, Compute, and Institutional Readiness](governance/workforce-compute.html#workforce-compute-readiness-demonstrated)Recommended in: [References](references.html#sec-references-frontier-labs)
- [Anthropic, Life Sciences Verification Program, 2026](https://www.anthropic.com/news/life-sciences-verification-program)Cited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#sec-ai-for-life-sciences-introduction); [Toolkit for AI-Augmented Bio Research](practical/toolkit.html#sec-toolkit-inventory)
- [Anthropic, October 2025](https://www.anthropic.com/news/claude-for-life-sciences)Cited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#sec-ai-for-life-sciences-introduction); [Workforce, Compute, and Institutional Readiness](governance/workforce-compute.html#workforce-compute-readiness-demonstrated)Recommended in: [References](references.html#sec-references-frontier-labs)
- [Search-and-replace genome editing without double-strand breaks or donor DNA](https://doi.org/10.1038/s41586-019-1711-4)Andrew V. Anzalone; Peyton B. Randolph; Jessie R. Davis; et al. 2019. Naturejournal articleCited in: [Cell and Gene Therapy AI](therapeutics/cell-gene-therapy.html#sec-cell-gene-therapy-models-datasets); [Glossary](appendices/glossary.html#prime-editing)
- [ApexGO](https://github.com/Yimeng-Zeng/ApexGO)Cited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-antimicrobial)
- [Field high-throughput phenotyping: the new crop breeding frontier](https://doi.org/10.1016/j.tplants.2013.09.008)José Luis Araus; Jill E. Cairns. 2014. Trends in Plant Sciencejournal articleCited in: [Plant, Crop, and Agricultural AI (passage 1)](organisms/agriculture-ai.html#agriculture-ai-measurement)Recommended in: [Plant, Crop, and Agricultural AI (passage 2)](organisms/agriculture-ai.html#agriculture-ai-further-reading)
- [Standards for distribution models in biodiversity assessments](https://doi.org/10.1126/sciadv.aat4858)Miguel B. Araújo; Robert P. Anderson; A. Márcia Barbosa; et al. 2019. Science Advancesjournal articleCited in: [Environmental and Ecological AI (passage 1)](organisms/environmental-ecology-ai.html#environmental-ecology-demonstrated)Recommended in: [Environmental and Ecological AI (passage 2)](organisms/environmental-ecology-ai.html#environmental-ecology-further-reading)
- [Arc Institute, 2025](https://arcinstitute.org/news/virtual-cell-challenge-2025-wrap-up)Cited in: [Perturbation Prediction and Virtual Cells (passage 1)](cells/virtual-cells.html); [Perturbation Prediction and Virtual Cells (passage 2)](cells/virtual-cells.html#perturbation-virtual-cells-demonstrated); [Benchmarks for Bio AI](governance/benchmarks.html#benchmarks-introduction); [Emerging Frontiers in AI for the Life Sciences](future/emerging.html#sec-emerging-virtual-cells)Recommended in: [References](references.html#sec-references-perturbation)
- [Segment Anything for Microscopy](https://doi.org/10.1038/s41592-024-02580-4)Anwai Archit; Luca Freckmann; Sushmita Nair; et al. 2025. Nature Methodsjournal articleCited in: [Microscopy and Cryo-EM AI](cells/microscopy-cryo-em.html#microscopy-cryo-em-segmentation); [Glossary](appendices/glossary.html#segment-anything-microscopy); [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-imaging)Recommended in: [References](references.html#sec-references-imaging)
- [Evaluating batch correction methods for image-based cell profiling](https://doi.org/10.1038/s41467-024-50613-5)John Arevalo; Ellen Su; Jessica D. Ewald; et al. 2024. Nature Communicationsjournal articleCited in: [Cell Painting and Image-Based Phenotyping (passage 1)](cells/cell-painting.html#cell-painting-image-phenotyping-batch); [Cell Painting and Image-Based Phenotyping (passage 2)](cells/cell-painting.html#cell-painting-image-phenotyping-demonstrated)
- [MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data](https://doi.org/10.1186/s13059-020-02015-1)Ricard Argelaguet; Damien Arnol; Danila Bredikhin; et al. 2020. Genome Biologyjournal articleCited in: [Microbiome and Multi-Omics AI](cells/multiomics.html#microbiome-multiomics-ai-demonstrated)
- [ARPA-H IGoR, 2026](https://arpa-h.gov/explore-funding/programs/igor)Cited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways); [Scope and Limitations](scope-limitations.html#sec-scope-out-of-scope); [Robotic Lab Automation and Cloud Labs](automation/cloud-labs.html#robotic-lab-automation-cloud-labs-introduction); [Agentic Science Workflows (passage 1)](automation/agentic-workflows.html#agentic-science-workflows-introduction); [Agentic Science Workflows (passage 2)](automation/agentic-workflows.html#agentic-science-workflows-demonstrated); [Reproducibility and Open Science](governance/reproducibility.html#reproducibility-open-science-introduction); [Information Hazards in Capability Research](governance/information-hazards.html#information-hazards-introduction); [Workforce, Compute, and Institutional Readiness](governance/workforce-compute.html#workforce-compute-readiness-introduction); [Quick Reference: All Chapter Summaries (passage 1)](appendices/tldr-compilation.html#summary-cloud-labs); [Quick Reference: All Chapter Summaries (passage 2)](appendices/tldr-compilation.html#summary-workforce-compute); [Case Studies](appendices/case-studies.html#sec-case-agentic-research-planning); [Glossary](appendices/glossary.html#igor); [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-programs)Recommended in: [References](references.html#sec-references-programs)
- [ARPA-H programs page](https://arpa-h.gov/explore-funding/programs)Cited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#public-programs)
- [ARPA-H, 2026](https://arpa-h.gov/news-and-events/arpa-h-announces-awards-advance-ai-rare-disease-diagnosis-and-treatment)Cited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#public-programs)
- [The promise and peril of chemical probes](https://doi.org/10.1038/nchembio.1867)Cheryl H Arrowsmith; James E Audia; Christopher Austin; et al. 2015. Nature Chemical Biologyjournal articleCited in: [Chemical Biology and Target Engagement](therapeutics/chemical-biology.html#sec-chemical-biology-introduction)
- [OpenEvidence clinical question-answering platform: systematic review of early evaluations](https://doi.org/10.1038/s41746-026-03077-4)Yaara Artsi; Vera Sorin; Benjamin S. Glicksberg; et al. 2026. npj Digital Medicinejournal articleCited in: [Knowledge Graphs and Literature AI](foundations/knowledge-and-literature.html#knowledge-literature-rag)
- [Effective gene expression prediction from sequence by integrating long-range interactions](https://doi.org/10.1038/s41592-021-01252-x)Žiga Avsec; Vikram Agarwal; Daniel Visentin; et al. 2021. Nature Methodsjournal articleCited in: [Nucleic Acid and Genome Models (passage 1)](molecular/genome-models.html#nucleic-acid-genome-models-regulatory-spine); [Nucleic Acid and Genome Models (passage 2)](molecular/genome-models.html#nucleic-acid-genome-models-demonstrated); [Variant Effect Prediction](molecular/variant-effects.html#sec-variant-effects-noncoding); [Glossary](appendices/glossary.html#enformer); [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-genome)Recommended in: [References](references.html#sec-references-genome)
- [Advancing regulatory variant effect prediction with AlphaGenome](https://doi.org/10.1038/s41586-025-10014-0)Žiga Avsec; Natasha Latysheva; Jun Cheng; et al. 2026. Naturejournal articleCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways); [Nucleic Acid and Genome Models (passage 1)](molecular/genome-models.html#nucleic-acid-genome-models-introduction); [Nucleic Acid and Genome Models (passage 2)](molecular/genome-models.html#nucleic-acid-genome-models-regulatory-spine); [Nucleic Acid and Genome Models (passage 3)](molecular/genome-models.html#nucleic-acid-genome-models-demonstrated); [Variant Effect Prediction](molecular/variant-effects.html#sec-variant-effects-noncoding); [mRNA, RNA, and Vaccine Design](therapeutics/vaccines.html#mrna-rna-vaccine-design-introduction); [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-genome-models); [Glossary](appendices/glossary.html#alphagenome); [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-genome)Recommended in: [References](references.html#sec-references-genome)
- [Azabou et al., 2023](https://poyo-brain.github.io/)Cited in: [Neuroscience AI and Brain Foundation Models (passage 1)](organisms/neuroscience-ai.html#neuroscience-ai-latent-dynamics); [Neuroscience AI and Brain Foundation Models (passage 2)](organisms/neuroscience-ai.html#neuroscience-ai-cross-modal-foundation); [Neuroscience AI and Brain Foundation Models (passage 3)](organisms/neuroscience-ai.html#neuroscience-ai-models-datasets-benchmarks)
- [Gene regulatory network inference in the era of single-cell multi-omics](https://doi.org/10.1038/s41576-023-00618-5)Pau Badia-i-Mompel; Lorna Wessels; Sophia Müller-Dott; et al. 2023. Nature Reviews Geneticsjournal articleCited in: [Systems Biology and Multiscale Modeling](cells/systems-biology.html#systems-biology-introduction); [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-systems-biology)Recommended in: [References](references.html#sec-references-systems-biology)
- [Accurate prediction of protein structures and interactions using a three-track neural network](https://doi.org/10.1126/science.abj8754)Minkyung Baek; Frank DiMaio; Ivan Anishchenko; et al. 2021. Sciencejournal articleCited in: [History of AI in the Life Sciences](foundations/history.html#sec-history-generative); [Protein Structure Prediction](molecular/protein-structure.html#single-chain-prediction-alphafold-2-and-successors); [Glossary](appendices/glossary.html#rosettafold); [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-structure)Recommended in: [References](references.html#sec-references-structure)
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- [Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods](https://doi.org/10.34133/2020/3521852)Etienne David; Simon Madec; Pouria Sadeghi-Tehran; et al. 2020. Plant Phenomicsjournal articleCited in: [Plant, Crop, and Agricultural AI (passage 1)](organisms/agriculture-ai.html#agriculture-ai-demonstrated); [Plant, Crop, and Agricultural AI (passage 2)](organisms/agriculture-ai.html#agriculture-ai-models-datasets-benchmarks)Recommended in: [Plant, Crop, and Agricultural AI (passage 3)](organisms/agriculture-ai.html#agriculture-ai-further-reading)
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- [Pretraining a foundation model for small-molecule natural products](https://doi.org/10.1038/s42256-026-01226-8)Yuheng Ding; Bo Qiang; Shaoning Li; et al. 2026. Nature Machine Intelligencejournal articleCited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-generative)Recommended in: [References](references.html#sec-references-small-molecules)
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- [ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning](https://doi.org/10.1109/TPAMI.2021.3095381)Ahmed Elnaggar; Michael Heinzinger; Christian Dallago; et al. 2022. IEEE Transactions on Pattern Analysis and Machine Intelligencejournal articleCited in: [Foundation Models for Biology](foundations/foundation-models.html#sec-fm-proteins); [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-protein-lm)Recommended in: [References](references.html#sec-references-structure)
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- [FDA, 2026](https://www.fda.gov/industry/fda-actions-accelerate-and-modernize-early-and-late-stage-clinical-development)Cited in: [Clinical Trial AI for Translational Research](therapeutics/clinical-trials.html#clinical-trial-latency-ai-era)
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- [FDA, 2026](https://www.fda.gov/regulatory-information/search-fda-guidance-documents/safety-assessment-genome-editing-human-gene-therapy-products-using-next-generation-sequencing)Cited in: [Cell and Gene Therapy AI (passage 1)](therapeutics/cell-gene-therapy.html#sec-cell-gene-therapy-introduction); [Cell and Gene Therapy AI (passage 2)](therapeutics/cell-gene-therapy.html#sec-cell-gene-therapy-models-datasets); [Cell and Gene Therapy AI (passage 3)](therapeutics/cell-gene-therapy.html#cell-gene-therapy-beyond-current-capabilities)
- [FDA, 2026](https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development)Cited in: [Scope and Limitations](scope-limitations.html#sec-scope-out-of-scope); [Target Identification and Prioritization](therapeutics/targets.html#target-identification-demonstrated); [Clinical Trial AI for Translational Research (passage 1)](therapeutics/clinical-trials.html#clinical-trial-ai-introduction); [Clinical Trial AI for Translational Research (passage 2)](therapeutics/clinical-trials.html#clinical-trial-ai-demonstrated); [Workforce, Compute, and Institutional Readiness](governance/workforce-compute.html#workforce-compute-readiness-introduction); [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-clinical-trials); [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-programs)Recommended in: [References](references.html#sec-references-programs)
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### Preface

#### References

- [Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines](https://doi.org/10.1038/s41592-025-02772-6)Constantin Ahlmann-Eltze; Wolfgang Huber; Simon Anders. 2025. Nature Methodsjournal articleCited in: [Preface](preface.html#sec-preface-why)
- [Deeper evaluation of a single-cell foundation model](https://doi.org/10.1038/s42256-024-00949-w)Rebecca Boiarsky; Nalini M. Singh; Alejandro Buendia; et al. 2024. Nature Machine Intelligencejournal articleCited in: [Preface](preface.html#sec-preface-why)
- [PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences](https://doi.org/10.1039/D3SC04185A)Martin Buttenschoen; Garrett M. Morris; Charlotte M. Deane. 2024. Chemical Sciencejournal articleCited in: [Preface](preface.html#sec-preface-why)

### Executive Summary

#### References

- [Accurate structure prediction of biomolecular interactions with AlphaFold 3](https://doi.org/10.1038/s41586-024-07487-w)Josh Abramson; Jonas Adler; Jack Dunger; et al. 2024. Naturejournal articleCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [Addendum: Accurate structure prediction of biomolecular interactions with AlphaFold 3](https://doi.org/10.1038/s41586-024-08416-7)Josh Abramson; Jonas Adler; Jack Dunger; et al. 2024. Naturejournal articleCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines](https://doi.org/10.1038/s41592-025-02772-6)Constantin Ahlmann-Eltze; Wolfgang Huber; Simon Anders. 2025. Nature Methodsjournal articleCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [ARPA-H IGoR, 2026](https://arpa-h.gov/explore-funding/programs/igor)Cited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [Advancing regulatory variant effect prediction with AlphaGenome](https://doi.org/10.1038/s41586-025-10014-0)Žiga Avsec; Natasha Latysheva; Jun Cheng; et al. 2026. Naturejournal articleCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [Atomically accurate de novo design of antibodies with RFdiffusion](https://doi.org/10.1038/s41586-025-09721-5)Nathaniel R. Bennett; Joseph L. Watson; Robert J. Ragotte; et al. 2025. Naturejournal articleCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [Deeper evaluation of a single-cell foundation model](https://doi.org/10.1038/s42256-024-00949-w)Rebecca Boiarsky; Nalini M. Singh; Alejandro Buendia; et al. 2024. Nature Machine Intelligencejournal articleCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [Autonomous chemical research with large language models](https://doi.org/10.1038/s41586-023-06792-0)Daniil A. Boiko; Robert MacKnight; Ben Kline; et al. 2023. Naturejournal articleCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [Genome modelling and design across all domains of life with Evo 2](https://doi.org/10.1038/s41586-026-10176-5)Garyk Brixi; Matthew G. Durrant; Jerome Ku; et al. 2026. Naturejournal articleCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [Chai-1: Decoding the molecular interactions of life](https://doi.org/10.1101/2024.10.10.615955)Chai Discovery; Jacques Boitreaud; Jack Dent; et al. 2024preprintCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [ClinicalTrials.gov, NCT07687459](https://clinicaltrials.gov/study/NCT07687459)Cited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [scGPT: toward building a foundation model for single-cell multi-omics using generative AI](https://doi.org/10.1038/s41592-024-02201-0)Haotian Cui; Chloe Wang; Hassaan Maan; et al. 2024. Nature Methodsjournal articleCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [Nucleotide Transformer: building and evaluating robust foundation models for human genomics](https://doi.org/10.1038/s41592-024-02523-z)Hugo Dalla-Torre; Liam Gonzalez; Javier Mendoza-Revilla; et al. 2024. Nature Methodsjournal articleCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [Robust deep learning–based protein sequence design using ProteinMPNN](https://doi.org/10.1126/science.add2187)J. Dauparas; I. Anishchenko; N. Bennett; et al. 2022. Sciencejournal articleCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [Atomic context-conditioned protein sequence design using LigandMPNN](https://doi.org/10.1038/s41592-025-02626-1)Justas Dauparas; Gyu Rie Lee; Robert Pecoraro; et al. 2025. Nature Methodsjournal articleCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [Orthrus: toward evolutionary and functional RNA foundation models](https://doi.org/10.1038/s41592-026-03064-3)Philip Fradkin; Ruian “Ian” Shi; Taykhoom Dalal; et al. 2026. Nature Methodsjournal articleCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [A foundation model of transcription across human cell types](https://doi.org/10.1038/s41586-024-08391-z)Xi Fu; Shentong Mo; Alejandro Buendia; et al. 2025. Naturejournal articleCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [A multi-agent system for automating scientific discovery](https://doi.org/10.1038/s41586-026-10652-y)Ali E. Ghareeb; Benjamin Chang; Ludovico Mitchener; et al. 2026. Naturejournal articleCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [Google DeepMind, AlphaFold 3 Model Parameters Terms of Use](https://github.com/google-deepmind/alphafold3/blob/main/WEIGHTS_TERMS_OF_USE.md)Cited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [Google DeepMind, AlphaFold 3 repository README](https://github.com/google-deepmind/alphafold3/blob/main/README.md)Cited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [Accelerating scientific discovery with Co-Scientist](https://doi.org/10.1038/s41586-026-10644-y)Juraj Gottweis; Wei-Hung Weng; Alexander Daryin; et al. 2026. Naturejournal articleCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [Large-scale foundation model on single-cell transcriptomics](https://doi.org/10.1038/s41592-024-02305-7)Minsheng Hao; Jing Gong; Xin Zeng; et al. 2024. Nature Methodsjournal articleCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [Simulating 500 million years of evolution with a language model](https://doi.org/10.1126/science.ads0018)Thomas Hayes; Roshan Rao; Halil Akin; et al. 2025. Sciencejournal articleCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [Illuminating protein space with a programmable generative model](https://doi.org/10.1038/s41586-023-06728-8)John B. Ingraham; Max Baranov; Zak Costello; et al. 2023. Naturejournal articleCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [Highly accurate protein structure prediction with AlphaFold](https://doi.org/10.1038/s41586-021-03819-2)John Jumper; Richard Evans; Alexander Pritzel; et al. 2021. Naturejournal articleCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [Deep learning perturbation models can outperform baselines on calibrated metrics](https://doi.org/10.1038/s41587-026-03307-w)Miller et al. 2026. Nature Biotechnologyjournal articleCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [Sequence modeling and design from molecular to genome scale with Evo](https://doi.org/10.1126/science.ado9336)Eric Nguyen; Michael Poli; Matthew G. Durrant; et al. 2024. Sciencejournal articleCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [NIH Common Fund](https://commonfund.nih.gov/bridge2ai)Cited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [NIH NOT-OD-26-101](https://grants.nih.gov/grants/guide/notice-files/NOT-OD-26-101.html)Cited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [ProGen2: Exploring the boundaries of protein language models](https://doi.org/10.1016/j.cels.2023.10.002)Erik Nijkamp; Jeffrey A. Ruffolo; Eli N. Weinstein; et al. 2023. Cell Systemsjournal articleCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria](https://doi.org/10.1016/j.ajhg.2022.10.013)Vikas Pejaver; Alicia B. Byrne; Bing-Jian Feng; et al. 2022. The American Journal of Human Geneticsjournal articleCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology](https://doi.org/10.1038/gim.2015.30)Sue Richards; Nazneen Aziz; Sherri Bale; et al. 2015. Genetics in Medicinejournal articleCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodies](https://doi.org/10.1038/s41586-025-09442-9)Kyle Swanson; Wesley Wu; Nash L. Bulaong; et al. 2025. Naturejournal articleCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [An autonomous laboratory for the accelerated synthesis of inorganic materials](https://doi.org/10.1038/s41586-023-06734-w)Nathan J. Szymanski; Bernardus Rendy; Yuxing Fei; et al. 2023. Naturejournal articleCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [Nicheformer: a foundation model for single-cell and spatial omics](https://doi.org/10.1038/s41592-025-02814-z)Alejandro Tejada-Lapuerta; Anna C. Schaar; Robert Gutgesell; et al. 2025. Nature Methodsjournal articleCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [Transfer learning enables predictions in network biology](https://doi.org/10.1038/s41586-023-06139-9)Christina V. Theodoris; Ling Xiao; Anant Chopra; et al. 2023. Naturejournal articleCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [USG Policy for Stopping High-Risk Life Sciences Research](https://www.whitehouse.gov/wp-content/uploads/2026/07/USG-Policy-for-Stopping-High-Risk-Life-Sciences-Research_July-2026.pdf)Cited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [DOME: recommendations for supervised machine learning validation in biology](https://doi.org/10.1038/s41592-021-01205-4)Ian Walsh; Dmytro Fishman; Dario Garcia-Gasulla; et al. 2021. Nature Methodsjournal articleCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [De novo design of protein structure and function with RFdiffusion](https://doi.org/10.1038/s41586-023-06415-8)Joseph L. Watson; David Juergens; Nathaniel R. Bennett; et al. 2023. Naturejournal articleCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [Boltz-1 Democratizing Biomolecular Interaction Modeling](https://doi.org/10.1101/2024.11.19.624167)Jeremy Wohlwend; Gabriele Corso; Saro Passaro; et al. 2024preprintCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial](https://doi.org/10.1038/s41591-025-03743-2)Zuojun Xu; Feng Ren; Ping Wang; et al. 2025. Nature Medicinejournal articleCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data](https://doi.org/10.1038/s42256-022-00534-z)Fan Yang; Wenchuan Wang; Fang Wang; et al. 2022. Nature Machine Intelligencejournal articleCited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)
- [Zambaldi et al., 2024, preprint](https://arxiv.org/abs/2409.08022)Cited in: [Executive Summary](executive-summary.html#sec-executive-summary-takeaways)

### How to Read This Handbook

#### References

- [PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences](https://doi.org/10.1039/D3SC04185A)Martin Buttenschoen; Garrett M. Morris; Charlotte M. Deane. 2024. Chemical Sciencejournal articleCited in: [How to Read This Handbook](how-to-read.html#sec-how-to-read-tiers)
- [Kryshtafovych et al., 2024](https://pmc.ncbi.nlm.nih.gov/articles/PMC10843301/)Cited in: [How to Read This Handbook](how-to-read.html#sec-how-to-read-tiers)
- [A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models](https://doi.org/10.1038/s41587-024-02143-0)Feng Ren; Alex Aliper; Jian Chen; et al. 2024. Nature Biotechnologyjournal articleCited in: [How to Read This Handbook](how-to-read.html#sec-how-to-read-drug-discovery)
- [DOME: recommendations for supervised machine learning validation in biology](https://doi.org/10.1038/s41592-021-01205-4)Ian Walsh; Dmytro Fishman; Dario Garcia-Gasulla; et al. 2021. Nature Methodsjournal articleCited in: [How to Read This Handbook](how-to-read.html#sec-how-to-read-tiers)
- [A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial](https://doi.org/10.1038/s41591-025-03743-2)Zuojun Xu; Feng Ren; Ping Wang; et al. 2025. Nature Medicinejournal articleCited in: [How to Read This Handbook](how-to-read.html#sec-how-to-read-drug-discovery)

### Scope and Limitations

#### References

- [Accurate structure prediction of biomolecular interactions with AlphaFold 3](https://doi.org/10.1038/s41586-024-07487-w)Josh Abramson; Jonas Adler; Jack Dunger; et al. 2024. Naturejournal articleCited in: [Scope and Limitations](scope-limitations.html#sec-scope-sources)
- [ARPA-H IGoR, 2026](https://arpa-h.gov/explore-funding/programs/igor)Cited in: [Scope and Limitations](scope-limitations.html#sec-scope-out-of-scope)
- [Autonomous chemical research with large language models](https://doi.org/10.1038/s41586-023-06792-0)Daniil A. Boiko; Robert MacKnight; Ben Kline; et al. 2023. Naturejournal articleCited in: [Scope and Limitations](scope-limitations.html#sec-scope-in-scope)
- [PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences](https://doi.org/10.1039/D3SC04185A)Martin Buttenschoen; Garrett M. Morris; Charlotte M. Deane. 2024. Chemical Sciencejournal articleCited in: [Scope and Limitations](scope-limitations.html#sec-scope-in-scope)
- [EMA, 2024](https://www.ema.europa.eu/en/use-artificial-intelligence-ai-medicinal-product-lifecycle)Cited in: [Scope and Limitations](scope-limitations.html#sec-scope-out-of-scope)
- [FDA, 2026](https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development)Cited in: [Scope and Limitations](scope-limitations.html#sec-scope-out-of-scope)
- [Challenges in High-Throughput Inorganic Materials Prediction and Autonomous Synthesis](https://doi.org/10.1103/PRXEnergy.3.011002)Josh Leeman; Yuhan Liu; Joseph Stiles; et al. 2024. PRX Energyjournal articleCited in: [Scope and Limitations](scope-limitations.html#sec-scope-claim-calibration)
- [The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodies](https://doi.org/10.1038/s41586-025-09442-9)Kyle Swanson; Wesley Wu; Nash L. Bulaong; et al. 2025. Naturejournal articleCited in: [Scope and Limitations](scope-limitations.html#sec-scope-in-scope)
- [An autonomous laboratory for the accelerated synthesis of inorganic materials](https://doi.org/10.1038/s41586-023-06734-w)Nathan J. Szymanski; Bernardus Rendy; Yuxing Fei; et al. 2023. Naturejournal articleCited in: [Scope and Limitations](scope-limitations.html#sec-scope-claim-calibration)
- [DOME: recommendations for supervised machine learning validation in biology](https://doi.org/10.1038/s41592-021-01205-4)Ian Walsh; Dmytro Fishman; Dario Garcia-Gasulla; et al. 2021. Nature Methodsjournal articleCited in: [Scope and Limitations](scope-limitations.html#sec-scope-in-scope)
- [Zambaldi et al., 2024, preprint](https://arxiv.org/abs/2409.08022)Cited in: [Scope and Limitations](scope-limitations.html#sec-scope-sources)

## Part I: Foundations

### AI for the Life Sciences

#### References

- [Accurate structure prediction of biomolecular interactions with AlphaFold 3](https://doi.org/10.1038/s41586-024-07487-w)Josh Abramson; Jonas Adler; Jack Dunger; et al. 2024. Naturejournal articleCited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#sec-ai-for-life-sciences-introduction)
- [Addendum: Accurate structure prediction of biomolecular interactions with AlphaFold 3](https://doi.org/10.1038/s41586-024-08416-7)Josh Abramson; Jonas Adler; Jack Dunger; et al. 2024. Naturejournal articleCited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#sec-ai-for-life-sciences-introduction)
- [Anthropic, Claude Fable and Mythos 5.1 overview, 2026](https://www.anthropic.com/claude-fable-and-mythos-5-1)Cited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#sec-ai-for-life-sciences-introduction)
- [Anthropic, June 2026](https://www.anthropic.com/news/claude-science-ai-workbench)Cited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#sec-ai-for-life-sciences-introduction)
- [Anthropic, Life Sciences Verification Program, 2026](https://www.anthropic.com/news/life-sciences-verification-program)Cited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#sec-ai-for-life-sciences-introduction)
- [Anthropic, October 2025](https://www.anthropic.com/news/claude-for-life-sciences)Cited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#sec-ai-for-life-sciences-introduction)
- [ARPA-H programs page](https://arpa-h.gov/explore-funding/programs)Cited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#public-programs)
- [ARPA-H, 2026](https://arpa-h.gov/news-and-events/arpa-h-announces-awards-advance-ai-rare-disease-diagnosis-and-treatment)Cited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#public-programs)
- [Autonomous chemical research with large language models](https://doi.org/10.1038/s41586-023-06792-0)Daniil A. Boiko; Robert MacKnight; Ben Kline; et al. 2023. Naturejournal articleCited in: [AI for the Life Sciences (passage 1)](foundations/ai-for-life-sciences.html); [AI for the Life Sciences (passage 2)](foundations/ai-for-life-sciences.html#sec-ai-for-life-sciences-reference-standard)
- [Bridge2AI Consortium](https://bridge2ai.org)Cited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#public-programs)
- [Genome modelling and design across all domains of life with Evo 2](https://doi.org/10.1038/s41586-026-10176-5)Garyk Brixi; Matthew G. Durrant; Jerome Ku; et al. 2026. Naturejournal articleCited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#non-profit-and-foundation-programs)
- [ClinicalTrials.gov, NCT07687459](https://clinicaltrials.gov/study/NCT07687459)Cited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#drug-discovery-companies-using-ai)
- [scGPT: toward building a foundation model for single-cell multi-omics using generative AI](https://doi.org/10.1038/s41592-024-02201-0)Haotian Cui; Chloe Wang; Hassaan Maan; et al. 2024. Nature Methodsjournal articleCited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#sec-ai-for-life-sciences-reference-standard)
- [CZ CELLxGENE Discover](https://cellxgene.cziscience.com/)Cited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#non-profit-and-foundation-programs)
- [CZI](https://virtualcellmodels.cziscience.com/)Cited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#non-profit-and-foundation-programs)
- [AlphaFold 3 Source Code License (Apache License 2.0, June 2026)](https://github.com/google-deepmind/alphafold3/blob/main/LICENSE)Google DeepMind. 2026. GitHubdocumentationCited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#sec-ai-for-life-sciences-introduction)
- [Google DeepMind, AlphaFold 3 Model Parameters Terms of Use, November 2024 release](https://github.com/google-deepmind/alphafold3/blob/v3.0.0/WEIGHTS_TERMS_OF_USE.md)Cited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#sec-ai-for-life-sciences-introduction)
- [Simulating 500 million years of evolution with a language model](https://doi.org/10.1126/science.ads0018)Thomas Hayes; Roshan Rao; Halil Akin; et al. 2025. Sciencejournal articleCited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#frontier-labs)
- [HHS press release, May 2026](https://www.hhs.gov/press-room/arpa-h-launches-intelligent-generator-of-research-igor-program.html)Cited in: [AI for the Life Sciences (passage 1)](foundations/ai-for-life-sciences.html#sec-ai-for-life-sciences-introduction); [AI for the Life Sciences (passage 2)](foundations/ai-for-life-sciences.html#public-programs)
- [Isomorphic Labs, January 2024](https://www.isomorphiclabs.com/articles/isomorphic-labs-kicks-off-2024-with-two-pharmaceutical-collaborations)Cited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#frontier-labs)
- [Highly accurate protein structure prediction with AlphaFold](https://doi.org/10.1038/s41586-021-03819-2)John Jumper; Richard Evans; Alexander Pritzel; et al. 2021. Naturejournal articleCited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#sec-ai-for-life-sciences-reference-standard)
- [Evolutionary-scale prediction of atomic-level protein structure with a language model](https://doi.org/10.1126/science.ade2574)Zeming Lin; Halil Akin; Roshan Rao; et al. 2023. Sciencejournal articleCited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#frontier-labs)
- [NCI CRDC](https://datascience.nih.gov/data-scholars-projects/supporting-cancer-knowledge-extraction-through-the-cancer-research-data-commons)Cited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#public-programs)
- [Sequence modeling and design from molecular to genome scale with Evo](https://doi.org/10.1126/science.ado9336)Eric Nguyen; Michael Poli; Matthew G. Durrant; et al. 2024. Sciencejournal articleCited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#non-profit-and-foundation-programs)
- [NIH Common Fund](https://commonfund.nih.gov/bridge2ai)Cited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#public-programs)
- [NIH Common Fund, January 2026](https://commonfund.nih.gov/bridge2ai/news/nih-common-fund-bridge2ai-program-advances-next-stage)Cited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#public-programs)
- [Nobel Prize, 2024](https://www.nobelprize.org/prizes/chemistry/2024/summary/)Cited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#sec-ai-for-life-sciences-introduction)
- [Introducing GPT-Rosalind for life sciences research](https://openai.com/index/introducing-gpt-rosalind/)OpenAI. 2026. OpenAIvendor announcementCited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#sec-ai-for-life-sciences-introduction)
- [Introducing new capabilities to GPT-Rosalind](https://openai.com/index/introducing-new-capabilities-to-gpt-rosalind/)OpenAI. 2026. OpenAIvendor announcementCited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#sec-ai-for-life-sciences-introduction)
- [Meet Rosalind Workbench: Empowering every scientist to be their own research team](https://developers.openai.com/blog/rosalind-workbench)OpenAI Rosalind team. 2026. OpenAI Developersvendor announcementCited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#sec-ai-for-life-sciences-introduction)
- [OpenAI, April 2024](https://openai.com/index/moderna/)Cited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#frontier-labs)
- [OpenAI, June 2024](https://openai.com/index/color-health/)Cited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#frontier-labs)
- [OpenAI, September 2024](https://openai.com/index/learning-to-reason-with-llms/)Cited in: [AI for the Life Sciences (passage 1)](foundations/ai-for-life-sciences.html#sec-ai-for-life-sciences-introduction); [AI for the Life Sciences (passage 2)](foundations/ai-for-life-sciences.html#frontier-labs)
- [RAPID program](https://arpa-h.gov/explore-funding/programs/rapid)Cited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#public-programs)
- [Recursion pipeline](https://www.recursion.com/pipeline)Cited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#drug-discovery-companies-using-ai)
- [A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models](https://doi.org/10.1038/s41587-024-02143-0)Feng Ren; Alex Aliper; Jian Chen; et al. 2024. Nature Biotechnologyjournal articleCited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#drug-discovery-companies-using-ai)
- [Transfer learning enables predictions in network biology](https://doi.org/10.1038/s41586-023-06139-9)Christina V. Theodoris; Ling Xiao; Anant Chopra; et al. 2023. Naturejournal articleCited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#sec-ai-for-life-sciences-reference-standard)
- [AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences](https://doi.org/10.1093/nar/gkad1011)Mihaly Varadi; Damian Bertoni; Paulyna Magana; et al. 2023. Nucleic Acids Researchjournal articleCited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html)
- [De novo design of protein structure and function with RFdiffusion](https://doi.org/10.1038/s41586-023-06415-8)Joseph L. Watson; David Juergens; Nathaniel R. Bennett; et al. 2023. Naturejournal articleCited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html)
- [A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial](https://doi.org/10.1038/s41591-025-03743-2)Zuojun Xu; Feng Ren; Ping Wang; et al. 2025. Nature Medicinejournal articleCited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#drug-discovery-companies-using-ai)
- [Zambaldi et al., 2024, preprint](https://arxiv.org/abs/2409.08022)Cited in: [AI for the Life Sciences](foundations/ai-for-life-sciences.html#frontier-labs)

### History of AI in the Life Sciences

#### References

- [Accurate structure prediction of biomolecular interactions with AlphaFold 3](https://doi.org/10.1038/s41586-024-07487-w)Josh Abramson; Jonas Adler; Jack Dunger; et al. 2024. Naturejournal articleCited in: [History of AI in the Life Sciences (passage 1)](foundations/history.html#sec-history-timeline); [History of AI in the Life Sciences (passage 2)](foundations/history.html#sec-history-generative)
- [Addendum: Accurate structure prediction of biomolecular interactions with AlphaFold 3](https://doi.org/10.1038/s41586-024-08416-7)Josh Abramson; Jonas Adler; Jack Dunger; et al. 2024. Naturejournal articleCited in: [History of AI in the Life Sciences](foundations/history.html#sec-history-generative)
- [A structural biology community assessment of AlphaFold2 applications](https://doi.org/10.1038/s41594-022-00849-w)Mehmet Akdel; Douglas E. V. Pires; Eduard Porta Pardo; et al. 2022. Nature Structural & Molecular Biologyjournal articleCited in: [History of AI in the Life Sciences](foundations/history.html#sec-history-generative)
- [Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning](https://doi.org/10.1038/nbt.3300)Babak Alipanahi; Andrew Delong; Matthew T Weirauch; et al. 2015. Nature Biotechnologyjournal articleCited in: [History of AI in the Life Sciences (passage 1)](foundations/history.html#sec-history-timeline); [History of AI in the Life Sciences (passage 2)](foundations/history.html#sec-history-deep-learning)
- [Basic local alignment search tool](https://doi.org/10.1016/S0022-2836%2805%2980360-2)Stephen F. Altschul; Warren Gish; Webb Miller; et al. 1990. Journal of Molecular Biologyjournal articleCited in: [History of AI in the Life Sciences (passage 1)](foundations/history.html#sec-history-timeline); [History of AI in the Life Sciences (passage 2)](foundations/history.html#sec-history-algorithmic)
- [Accurate prediction of protein structures and interactions using a three-track neural network](https://doi.org/10.1126/science.abj8754)Minkyung Baek; Frank DiMaio; Ivan Anishchenko; et al. 2021. Sciencejournal articleCited in: [History of AI in the Life Sciences](foundations/history.html#sec-history-generative)
- [Prediction of complete gene structures in human genomic DNA](https://doi.org/10.1006/jmbi.1997.0951)Chris Burge; Samuel Karlin. 1997. Journal of Molecular Biologyjournal articleCited in: [History of AI in the Life Sciences (passage 1)](foundations/history.html#sec-history-timeline); [History of AI in the Life Sciences (passage 2)](foundations/history.html#sec-history-statistical)
- [Chai-1: Decoding the molecular interactions of life](https://doi.org/10.1101/2024.10.10.615955)Chai Discovery; Jacques Boitreaud; Jack Dent; et al. 2024preprintCited in: [History of AI in the Life Sciences](foundations/history.html#sec-history-generative)
- [Accurate proteome-wide missense variant effect prediction with AlphaMissense](https://doi.org/10.1126/science.adg7492)Jun Cheng; Guido Novati; Joshua Pan; et al. 2023. Sciencejournal articleCited in: [History of AI in the Life Sciences](foundations/history.html#sec-history-generative)
- [Profile hidden Markov models.](https://doi.org/10.1093/bioinformatics/14.9.755)S R Eddy. 1998. Bioinformaticsjournal articleCited in: [History of AI in the Life Sciences (passage 1)](foundations/history.html#sec-history-timeline); [History of AI in the Life Sciences (passage 2)](foundations/history.html#sec-history-statistical)
- [AlphaFold 3 Source Code License (Apache License 2.0, June 2026)](https://github.com/google-deepmind/alphafold3/blob/main/LICENSE)Google DeepMind. 2026. GitHubdocumentationCited in: [History of AI in the Life Sciences](foundations/history.html#sec-history-generative)
- [Google DeepMind, AlphaFold 3 Model Parameters Terms of Use, November 2024 release](https://github.com/google-deepmind/alphafold3/blob/v3.0.0/WEIGHTS_TERMS_OF_USE.md)Cited in: [History of AI in the Life Sciences](foundations/history.html#sec-history-generative)
- [Amino acid substitution matrices from protein blocks.](https://doi.org/10.1073/pnas.89.22.10915)S Henikoff; J G Henikoff. 1992. Proceedings of the National Academy of Sciencesjournal articleCited in: [History of AI in the Life Sciences](foundations/history.html#sec-history-algorithmic)
- [Highly accurate protein structure prediction with AlphaFold](https://doi.org/10.1038/s41586-021-03819-2)John Jumper; Richard Evans; Alexander Pritzel; et al. 2021. Naturejournal articleCited in: [History of AI in the Life Sciences (passage 1)](foundations/history.html#sec-history-introduction); [History of AI in the Life Sciences (passage 2)](foundations/history.html#sec-history-timeline); [History of AI in the Life Sciences (passage 3)](foundations/history.html#sec-history-generative)
- [Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks](https://doi.org/10.1101/gr.200535.115)David R. Kelley; Jasper Snoek; John L. Rinn. 2016. Genome Researchjournal articleCited in: [History of AI in the Life Sciences](foundations/history.html#sec-history-deep-learning)
- [Sequential regulatory activity prediction across chromosomes with convolutional neural networks](https://doi.org/10.1101/gr.227819.117)David R. Kelley; Yakir A. Reshef; Maxwell Bileschi; et al. 2018. Genome Researchjournal articleCited in: [History of AI in the Life Sciences](foundations/history.html#sec-history-deep-learning)
- [Evolutionary-scale prediction of atomic-level protein structure with a language model](https://doi.org/10.1126/science.ade2574)Zeming Lin; Halil Akin; Roshan Rao; et al. 2023. Sciencejournal articleCited in: [History of AI in the Life Sciences](foundations/history.html#sec-history-generative)
- [A general method applicable to the search for similarities in the amino acid sequence of two proteins](https://doi.org/10.1016/0022-2836%2870%2990057-4)Saul B. Needleman; Christian D. Wunsch. 1970. Journal of Molecular Biologyjournal articleCited in: [History of AI in the Life Sciences (passage 1)](foundations/history.html#sec-history-timeline); [History of AI in the Life Sciences (passage 2)](foundations/history.html#sec-history-algorithmic)
- [Nobel Prize, 2024](https://www.nobelprize.org/prizes/chemistry/2024/summary/)Cited in: [History of AI in the Life Sciences (passage 1)](foundations/history.html#sec-history-timeline); [History of AI in the Life Sciences (passage 2)](foundations/history.html#sec-history-generative)
- [A universal SNP and small-indel variant caller using deep neural networks](https://doi.org/10.1038/nbt.4235)Ryan Poplin; Pi-Chuan Chang; David Alexander; et al. 2018. Nature Biotechnologyjournal articleCited in: [History of AI in the Life Sciences (passage 1)](foundations/history.html#sec-history-timeline); [History of AI in the Life Sciences (passage 2)](foundations/history.html#sec-history-deep-learning)
- [DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences](https://doi.org/10.1093/nar/gkw226)Daniel Quang; Xiaohui Xie. 2016. Nucleic Acids Researchjournal articleCited in: [History of AI in the Life Sciences](foundations/history.html#sec-history-deep-learning)
- [Improved protein structure prediction using potentials from deep learning](https://doi.org/10.1038/s41586-019-1923-7)Andrew W. Senior; Richard Evans; John Jumper; et al. 2020. Naturejournal articleCited in: [History of AI in the Life Sciences (passage 1)](foundations/history.html#sec-history-timeline); [History of AI in the Life Sciences (passage 2)](foundations/history.html#sec-history-deep-learning)
- [Identification of common molecular subsequences](https://doi.org/10.1016/0022-2836%2881%2990087-5)T.F. Smith; M.S. Waterman. 1981. Journal of Molecular Biologyjournal articleCited in: [History of AI in the Life Sciences (passage 1)](foundations/history.html#sec-history-timeline); [History of AI in the Life Sciences (passage 2)](foundations/history.html#sec-history-algorithmic)
- [Transfer learning enables predictions in network biology](https://doi.org/10.1038/s41586-023-06139-9)Christina V. Theodoris; Ling Xiao; Anant Chopra; et al. 2023. Naturejournal articleCited in: [History of AI in the Life Sciences](foundations/history.html#sec-history-foundation-models)
- [AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences](https://doi.org/10.1093/nar/gkad1011)Mihaly Varadi; Damian Bertoni; Paulyna Magana; et al. 2023. Nucleic Acids Researchjournal articleCited in: [History of AI in the Life Sciences](foundations/history.html#sec-history-generative)
- [De novo design of protein structure and function with RFdiffusion](https://doi.org/10.1038/s41586-023-06415-8)Joseph L. Watson; David Juergens; Nathaniel R. Bennett; et al. 2023. Naturejournal articleCited in: [History of AI in the Life Sciences (passage 1)](foundations/history.html#sec-history-timeline); [History of AI in the Life Sciences (passage 2)](foundations/history.html#sec-history-generative)
- [Boltz-1 Democratizing Biomolecular Interaction Modeling](https://doi.org/10.1101/2024.11.19.624167)Jeremy Wohlwend; Gabriele Corso; Saro Passaro; et al. 2024preprintCited in: [History of AI in the Life Sciences](foundations/history.html#sec-history-generative)
- [Predicting effects of noncoding variants with deep learning–based sequence model](https://doi.org/10.1038/nmeth.3547)Jian Zhou; Olga G Troyanskaya. 2015. Nature Methodsjournal articleCited in: [History of AI in the Life Sciences (passage 1)](foundations/history.html#sec-history-timeline); [History of AI in the Life Sciences (passage 2)](foundations/history.html#sec-history-deep-learning)

### Biological Data Infrastructure

#### References

- [AlphaFold Protein Structure Database, 2026](https://alphafold.ebi.ac.uk/about)Cited in: [Biological Data Infrastructure](foundations/data-infrastructure.html#data-infrastructure-demonstrated)
- [The Protein Data Bank](https://doi.org/10.1093/nar/28.1.235)H. M. Berman. 2000. Nucleic Acids Researchjournal articleCited in: [Biological Data Infrastructure](foundations/data-infrastructure.html#data-infrastructure-introduction)
- [International, cross-sector collaboration commits nearly \$2 billion to build foundational data for AI models to predict and treat disease](https://www.prnewswire.com/news-releases/international-cross-sector-collaboration-commits-nearly-2-billion-to-build-foundational-data-for-ai-models-to-predict-and-treat-disease-302901172.html)Biohub. 2026. PR Newswireinstitutional announcementCited in: [Biological Data Infrastructure](foundations/data-infrastructure.html)
- [Virtual Biology Initiative - \$500M for AI-powered biology](https://biohub.org/news/virtual-biology-initiative/)Biohub. 2026. Biohubinstitutional announcementCited in: [Biological Data Infrastructure](foundations/data-infrastructure.html)
- [ESM Metagenomic Atlas, 2026](https://esmatlas.com/about)Cited in: [Biological Data Infrastructure](foundations/data-infrastructure.html#data-infrastructure-demonstrated)
- [Datasheets for datasets](https://doi.org/10.1145/3458723)Timnit Gebru; Jamie Morgenstern; Briana Vecchione; et al. 2021. Communications of the ACMjournal articleCited in: [Biological Data Infrastructure](foundations/data-infrastructure.html#data-infrastructure-fair-ai-ready)
- [Global patterns and gaps in AI policies of national genomics initiatives](https://doi.org/10.1038/s44482-026-00043-5)Hazel et al. 2026. npj Digital Public Healthjournal articleCited in: [Biological Data Infrastructure](foundations/data-infrastructure.html#data-infrastructure-post-training-lineage)
- [Zuckerberg teams with Google, U.S. in push to map cells](https://www.axios.com/2026/10/07/zuckerberg-biohub-ai-biology-data)Ina Fried. 2026. Axiosnews articleCited in: [Biological Data Infrastructure](foundations/data-infrastructure.html)
- [PubChem 2023 update](https://doi.org/10.1093/nar/gkac956)Sunghwan Kim; Jie Chen; Tiejun Cheng; et al. 2022. Nucleic Acids Researchjournal articleCited in: [Biological Data Infrastructure](foundations/data-infrastructure.html#data-infrastructure-demonstrated)
- [Kim et al., 2023](https://pubmed.ncbi.nlm.nih.gov/36305812/)Cited in: [Biological Data Infrastructure](foundations/data-infrastructure.html#data-infrastructure-demonstrated)
- [US government, Google join Zuckerberg-backed Biohub in \$1.8 billion push for AI biology data](https://www.reuters.com/business/healthcare-pharmaceuticals/us-government-google-join-zuckerberg-backed-biohub-18-billion-push-ai-biology-2026-10-07/)Krystal Hu. 2026. Reutersnews articleCited in: [Biological Data Infrastructure](foundations/data-infrastructure.html)
- [Lambert et al., 2025](https://openreview.net/forum?id=i1uGbfHHpH)Cited in: [Biological Data Infrastructure](foundations/data-infrastructure.html#data-infrastructure-post-training-lineage)
- [Lee et al., 2024](https://proceedings.mlr.press/v235/lee24t.html)Cited in: [Biological Data Infrastructure](foundations/data-infrastructure.html#data-infrastructure-post-training-lineage)
- [Democratizing knowledge representation with BioCypher](https://doi.org/10.1038/s41587-023-01848-y)Sebastian Lobentanzer; Patrick Aloy; Jan Baumbach; et al. 2023. Nature Biotechnologyjournal articleCited in: [Biological Data Infrastructure](foundations/data-infrastructure.html#data-infrastructure-measured-computed)
- [NIH joins effort to build SI-ready data for predictive models of human biology  \| National Institutes of Health (NIH)](https://www.nih.gov/news-events/news-releases/nih-joins-effort-build-si-ready-data-predictive-models-human-biology)National Institutes of Health. 2026. National Institutes of Healthinstitutional announcementCited in: [Biological Data Infrastructure](foundations/data-infrastructure.html)
- [NIH Common Fund](https://commonfund.nih.gov/bridge2ai)Cited in: [Biological Data Infrastructure](foundations/data-infrastructure.html)
- [RCSB PDB, 2026](https://www.rcsb.org/)Cited in: [Biological Data Infrastructure](foundations/data-infrastructure.html#data-infrastructure-introduction)
- [The Human Cell Atlas](https://doi.org/10.7554/eLife.27041)Aviv Regev; Sarah A Teichmann; Eric S Lander; et al. 2017. eLifejournal articleCited in: [Biological Data Infrastructure](foundations/data-infrastructure.html#data-infrastructure-fair-ai-ready)
- [AI models collapse when trained on recursively generated data](https://doi.org/10.1038/s41586-024-07566-y)Ilia Shumailov; Zakhar Shumaylov; Yiren Zhao; et al. 2024. Naturejournal articleCited in: [Biological Data Infrastructure](foundations/data-infrastructure.html#data-infrastructure-post-training-lineage)
- [Tabula Sapiens Consortium, 2022](https://doi.org/10.1126/science.abl4896)Cited in: [Biological Data Infrastructure](foundations/data-infrastructure.html#data-infrastructure-demonstrated)
- [UniProt Consortium, 2025](https://doi.org/10.1093/nar/gkae1010)Cited in: [Biological Data Infrastructure](foundations/data-infrastructure.html#data-infrastructure-demonstrated)
- [The FAIR Guiding Principles for scientific data management and stewardship](https://doi.org/10.1038/sdata.2016.18)Mark D. Wilkinson; Michel Dumontier; IJsbrand Jan Aalbersberg; et al. 2016. Scientific Datajournal articleCited in: [Biological Data Infrastructure](foundations/data-infrastructure.html#data-infrastructure-introduction)
- [wwPDB, 2026](https://www.wwpdb.org/?lang=en)Cited in: [Biological Data Infrastructure](foundations/data-infrastructure.html#data-infrastructure-introduction)
- [The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods](https://doi.org/10.1093/nar/gkad1004)Barbara Zdrazil; Eloy Felix; Fiona Hunter; et al. 2023. Nucleic Acids Researchjournal articleCited in: [Biological Data Infrastructure](foundations/data-infrastructure.html#data-infrastructure-demonstrated)
- [Zdrazil et al., 2024](https://pubmed.ncbi.nlm.nih.gov/37933841/)Cited in: [Biological Data Infrastructure](foundations/data-infrastructure.html#data-infrastructure-demonstrated)

### Knowledge Graphs and Literature AI

#### References

- [OpenEvidence clinical question-answering platform: systematic review of early evaluations](https://doi.org/10.1038/s41746-026-03077-4)Yaara Artsi; Vera Sorin; Benjamin S. Glicksberg; et al. 2026. npj Digital Medicinejournal articleCited in: [Knowledge Graphs and Literature AI](foundations/knowledge-and-literature.html#knowledge-literature-rag)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://doi.org/10.18653/v1/D19-1371)Iz Beltagy; Kyle Lo; Arman Cohan. 2019. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)proceedings articleCited in: [Knowledge Graphs and Literature AI](foundations/knowledge-and-literature.html#knowledge-literature-language-models)
- [The Unified Medical Language System (UMLS): integrating biomedical terminology](https://doi.org/10.1093/nar/gkh061)O. Bodenreider. 2004. Nucleic Acids Researchjournal articleCited in: [Knowledge Graphs and Literature AI](foundations/knowledge-and-literature.html#knowledge-literature-language-models)
- [Causaly, 2026](https://www.causaly.com/)Cited in: [Knowledge Graphs and Literature AI](foundations/knowledge-and-literature.html#knowledge-literature-rag)
- [Building a knowledge graph to enable precision medicine](https://doi.org/10.1038/s41597-023-01960-3)Payal Chandak; Kexin Huang; Marinka Zitnik. 2023. Scientific Datajournal articleCited in: [Knowledge Graphs and Literature AI](foundations/knowledge-and-literature.html#knowledge-literature-graphs)
- [Edwards, Ars Technica, November 2022](https://arstechnica.com/information-technology/2022/11/after-controversy-meta-pulls-demo-of-ai-model-that-writes-scientific-papers/)Cited in: [Knowledge Graphs and Literature AI](foundations/knowledge-and-literature.html#knowledge-literature-citation-grounding)
- [Elicit, 2026](https://elicit.com/)Cited in: [Knowledge Graphs and Literature AI](foundations/knowledge-and-literature.html#knowledge-literature-rag)
- [AI acknowledgments in math arXiv preprints (research assistance, established authors)](https://epoch.ai/data/arxiv?view=graph&useCase=research_assistance&onlyEstablished=true)Epoch AI. 2026. Epoch AIdatasetCited in: [Knowledge Graphs and Literature AI](foundations/knowledge-and-literature.html#knowledge-literature-rag)
- [arXiv AI acknowledgments data documentation](https://epoch.ai/data/arxiv-documentation)Epoch AI. 2026. Epoch AIreportCited in: [Knowledge Graphs and Literature AI](foundations/knowledge-and-literature.html#knowledge-literature-rag)
- [Learning to Fake It: Limited Responses and Fabricated References Provided by ChatGPT for Medical Questions](https://doi.org/10.1016/j.mcpdig.2023.05.004)Jocelyn Gravel; Madeleine D’Amours-Gravel; Esli Osmanlliu. 2023. Mayo Clinic Proceedings: Digital Healthjournal articleCited in: [Knowledge Graphs and Literature AI](foundations/knowledge-and-literature.html#knowledge-literature-rag)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://doi.org/10.1145/3458754)Yu Gu; Robert Tinn; Hao Cheng; et al. 2021. ACM Transactions on Computing for Healthcarejournal articleCited in: [Knowledge Graphs and Literature AI](foundations/knowledge-and-literature.html#knowledge-literature-language-models)
- [Systematic integration of biomedical knowledge prioritizes drugs for repurposing](https://doi.org/10.7554/elife.26726)Daniel Scott Himmelstein; Antoine Lizee; Christine Hessler; et al. 2017. eLifejournal articleCited in: [Knowledge Graphs and Literature AI](foundations/knowledge-and-literature.html#knowledge-literature-graphs)
- [Iris.ai, 2026](https://iris.ai/)Cited in: [Knowledge Graphs and Literature AI](foundations/knowledge-and-literature.html#knowledge-literature-rag)
- [SemMedDB: a PubMed-scale repository of biomedical semantic predications](https://doi.org/10.1093/bioinformatics/bts591)Halil Kilicoglu; Dongwook Shin; Marcelo Fiszman; et al. 2012. Bioinformaticsjournal articleCited in: [Knowledge Graphs and Literature AI](foundations/knowledge-and-literature.html#knowledge-literature-semantic-predications)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://doi.org/10.1093/bioinformatics/btz682)Jinhyuk Lee; Wonjin Yoon; Sungdong Kim; et al. 2019. Bioinformaticsjournal articleCited in: [Knowledge Graphs and Literature AI](foundations/knowledge-and-literature.html#knowledge-literature-language-models)
- [Open Targets Platform: supporting systematic drug–target identification and prioritisation](https://doi.org/10.1093/nar/gkaa1027)David Ochoa; Andrew Hercules; Miguel Carmona; et al. 2020. Nucleic Acids Researchjournal articleCited in: [Knowledge Graphs and Literature AI](foundations/knowledge-and-literature.html#knowledge-literature-graphs)
- [OpenEvidence, 2026](https://www.openevidence.com/)Cited in: [Knowledge Graphs and Literature AI](foundations/knowledge-and-literature.html#knowledge-literature-rag)
- [Taylor et al., 2022, preprint](https://arxiv.org/abs/2211.09085)Cited in: [Knowledge Graphs and Literature AI](foundations/knowledge-and-literature.html#knowledge-literature-citation-grounding)
- [An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition](https://doi.org/10.1186/s12859-015-0564-6)George Tsatsaronis; Georgios Balikas; Prodromos Malakasiotis; et al. 2015. BMC Bioinformaticsjournal articleCited in: [Knowledge Graphs and Literature AI](foundations/knowledge-and-literature.html#knowledge-literature-rag)
- [Fabrication and errors in the bibliographic citations generated by ChatGPT](https://doi.org/10.1038/s41598-023-41032-5)William H. Walters; Esther Isabelle Wilder. 2023. Scientific Reportsjournal articleCited in: [Knowledge Graphs and Literature AI](foundations/knowledge-and-literature.html#knowledge-literature-rag)
- [PubMind: literature-based genetic variant extraction and functional annotation using large language models](https://doi.org/10.1038/s41467-026-76834-4)Peng Wang; Kai Wang. 2026. Nature Communicationsjournal articleCited in: [Knowledge Graphs and Literature AI](foundations/knowledge-and-literature.html#knowledge-literature-language-models)
- [PubTator central: automated concept annotation for biomedical full text articles](https://doi.org/10.1093/nar/gkz389)Chih-Hsuan Wei; Alexis Allot; Robert Leaman; et al. 2019. Nucleic Acids Researchjournal articleCited in: [Knowledge Graphs and Literature AI](foundations/knowledge-and-literature.html#knowledge-literature-language-models)

### Foundation Models for Biology

#### References

- [Accurate structure prediction of biomolecular interactions with AlphaFold 3](https://doi.org/10.1038/s41586-024-07487-w)Josh Abramson; Jonas Adler; Jack Dunger; et al. 2024. Naturejournal articleCited in: [Foundation Models for Biology](foundations/foundation-models.html#sec-fm-structures)
- [Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines](https://doi.org/10.1038/s41592-025-02772-6)Constantin Ahlmann-Eltze; Wolfgang Huber; Simon Anders. 2025. Nature Methodsjournal articleCited in: [Foundation Models for Biology](foundations/foundation-models.html#sec-fm-cells)
- [Anthropic, 2026](https://anthropic.com/responsible-scaling-policy/rsp-v3-0)Cited in: [Foundation Models for Biology](foundations/foundation-models.html#fm-practice-notes)
- [Deeper evaluation of a single-cell foundation model](https://doi.org/10.1038/s42256-024-00949-w)Rebecca Boiarsky; Nalini M. Singh; Alejandro Buendia; et al. 2024. Nature Machine Intelligencejournal articleCited in: [Foundation Models for Biology](foundations/foundation-models.html#sec-fm-cells)
- [Genome modelling and design across all domains of life with Evo 2](https://doi.org/10.1038/s41586-026-10176-5)Garyk Brixi; Matthew G. Durrant; Jerome Ku; et al. 2026. Naturejournal articleCited in: [Foundation Models for Biology (passage 1)](foundations/foundation-models.html#fm-introduction); [Foundation Models for Biology (passage 2)](foundations/foundation-models.html#sec-fm-genomes)
- [scGPT: toward building a foundation model for single-cell multi-omics using generative AI](https://doi.org/10.1038/s41592-024-02201-0)Haotian Cui; Chloe Wang; Hassaan Maan; et al. 2024. Nature Methodsjournal articleCited in: [Foundation Models for Biology (passage 1)](foundations/foundation-models.html#fm-introduction); [Foundation Models for Biology (passage 2)](foundations/foundation-models.html#sec-fm-cells)
- [Nucleotide Transformer: building and evaluating robust foundation models for human genomics](https://doi.org/10.1038/s41592-024-02523-z)Hugo Dalla-Torre; Liam Gonzalez; Javier Mendoza-Revilla; et al. 2024. Nature Methodsjournal articleCited in: [Foundation Models for Biology (passage 1)](foundations/foundation-models.html#fm-introduction); [Foundation Models for Biology (passage 2)](foundations/foundation-models.html#sec-fm-genomes)
- [ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning](https://doi.org/10.1109/TPAMI.2021.3095381)Ahmed Elnaggar; Michael Heinzinger; Christian Dallago; et al. 2022. IEEE Transactions on Pattern Analysis and Machine Intelligencejournal articleCited in: [Foundation Models for Biology](foundations/foundation-models.html#sec-fm-proteins)
- [Orthrus: toward evolutionary and functional RNA foundation models](https://doi.org/10.1038/s41592-026-03064-3)Philip Fradkin; Ruian “Ian” Shi; Taykhoom Dalal; et al. 2026. Nature Methodsjournal articleCited in: [Foundation Models for Biology (passage 1)](foundations/foundation-models.html#fm-introduction); [Foundation Models for Biology (passage 2)](foundations/foundation-models.html#sec-fm-genomes)
- [A foundation model of transcription across human cell types](https://doi.org/10.1038/s41586-024-08391-z)Xi Fu; Shentong Mo; Alejandro Buendia; et al. 2025. Naturejournal articleCited in: [Foundation Models for Biology (passage 1)](foundations/foundation-models.html#fm-introduction); [Foundation Models for Biology (passage 2)](foundations/foundation-models.html#sec-fm-genomes)
- [Google DeepMind, 2026](https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/strengthening-our-frontier-safety-framework/frontier-safety-framework_3-1.pdf)Cited in: [Foundation Models for Biology](foundations/foundation-models.html#fm-practice-notes)
- [Large-scale foundation model on single-cell transcriptomics](https://doi.org/10.1038/s41592-024-02305-7)Minsheng Hao; Jing Gong; Xin Zeng; et al. 2024. Nature Methodsjournal articleCited in: [Foundation Models for Biology (passage 1)](foundations/foundation-models.html#fm-introduction); [Foundation Models for Biology (passage 2)](foundations/foundation-models.html#sec-fm-cells)
- [Simulating 500 million years of evolution with a language model](https://doi.org/10.1126/science.ads0018)Thomas Hayes; Roshan Rao; Halil Akin; et al. 2025. Sciencejournal articleCited in: [Foundation Models for Biology (passage 1)](foundations/foundation-models.html#fm-introduction); [Foundation Models for Biology (passage 2)](foundations/foundation-models.html#sec-fm-proteins)
- [A cell atlas foundation model for scalable search of similar human cells](https://doi.org/10.1038/s41586-024-08411-y)Graham Heimberg; Tony Kuo; Daryle J. DePianto; et al. 2024. Naturejournal articleCited in: [Foundation Models for Biology](foundations/foundation-models.html#sec-fm-cells)
- [DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome](https://doi.org/10.1093/bioinformatics/btab083)Yanrong Ji; Zhihan Zhou; Han Liu; et al. 2021. Bioinformaticsjournal articleCited in: [Foundation Models for Biology](foundations/foundation-models.html#sec-fm-genomes)
- [Highly accurate protein structure prediction with AlphaFold](https://doi.org/10.1038/s41586-021-03819-2)John Jumper; Richard Evans; Alexander Pritzel; et al. 2021. Naturejournal articleCited in: [Foundation Models for Biology](foundations/foundation-models.html#sec-fm-structures)
- [Evolutionary-scale prediction of atomic-level protein structure with a language model](https://doi.org/10.1126/science.ade2574)Zeming Lin; Halil Akin; Roshan Rao; et al. 2023. Sciencejournal articleCited in: [Foundation Models for Biology (passage 1)](foundations/foundation-models.html#fm-introduction); [Foundation Models for Biology (passage 2)](foundations/foundation-models.html#sec-fm-proteins)
- [Sequence modeling and design from molecular to genome scale with Evo](https://doi.org/10.1126/science.ado9336)Eric Nguyen; Michael Poli; Matthew G. Durrant; et al. 2024. Sciencejournal articleCited in: [Foundation Models for Biology (passage 1)](foundations/foundation-models.html#fm-introduction); [Foundation Models for Biology (passage 2)](foundations/foundation-models.html#sec-fm-genomes)
- [ProGen2: Exploring the boundaries of protein language models](https://doi.org/10.1016/j.cels.2023.10.002)Erik Nijkamp; Jeffrey A. Ruffolo; Eli N. Weinstein; et al. 2023. Cell Systemsjournal articleCited in: [Foundation Models for Biology](foundations/foundation-models.html#sec-fm-proteins)
- [OpenAI, 2025](https://openai.com/index/updating-our-preparedness-framework/)Cited in: [Foundation Models for Biology](foundations/foundation-models.html#fm-practice-notes)
- [Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences](https://doi.org/10.1073/pnas.2016239118)Alexander Rives; Joshua Meier; Tom Sercu; et al. 2021. Proceedings of the National Academy of Sciencesjournal articleCited in: [Foundation Models for Biology (passage 1)](foundations/foundation-models.html#fm-introduction); [Foundation Models for Biology (passage 2)](foundations/foundation-models.html#sec-fm-proteins)
- [Nicheformer: a foundation model for single-cell and spatial omics](https://doi.org/10.1038/s41592-025-02814-z)Alejandro Tejada-Lapuerta; Anna C. Schaar; Robert Gutgesell; et al. 2025. Nature Methodsjournal articleCited in: [Foundation Models for Biology (passage 1)](foundations/foundation-models.html#fm-introduction); [Foundation Models for Biology (passage 2)](foundations/foundation-models.html#sec-fm-cells)
- [Transfer learning enables predictions in network biology](https://doi.org/10.1038/s41586-023-06139-9)Christina V. Theodoris; Ling Xiao; Anant Chopra; et al. 2023. Naturejournal articleCited in: [Foundation Models for Biology (passage 1)](foundations/foundation-models.html#fm-introduction); [Foundation Models for Biology (passage 2)](foundations/foundation-models.html#sec-fm-cells)

### Evaluation Principles for Life Sciences AI

#### References

- [Accurate structure prediction of biomolecular interactions with AlphaFold 3](https://doi.org/10.1038/s41586-024-07487-w)Josh Abramson; Jonas Adler; Jack Dunger; et al. 2024. Naturejournal articleCited in: [Evaluation Principles for Life Sciences AI](foundations/evaluation-principles.html#sec-evaluation-calibration)
- [GuacaMol: Benchmarking Models for de Novo Molecular Design](https://doi.org/10.1021/acs.jcim.8b00839)Nathan Brown; Marco Fiscato; Marwin H.S. Segler; et al. 2019. Journal of Chemical Information and Modelingjournal articleCited in: [Evaluation Principles for Life Sciences AI](foundations/evaluation-principles.html#sec-evaluation-validity)
- [Buttenschoen et al., 2024](https://pubs.rsc.org/en-us/content/articlehtml/2024/sc/d3sc04185a)Cited in: [Evaluation Principles for Life Sciences AI](foundations/evaluation-principles.html#sec-evaluation-blind-benchmarks)
- [CAMEO, 2026](https://www.expasy.org/resources/cameo)Cited in: [Evaluation Principles for Life Sciences AI](foundations/evaluation-principles.html#sec-evaluation-blind-benchmarks)
- [Accurate proteome-wide missense variant effect prediction with AlphaMissense](https://doi.org/10.1126/science.adg7492)Jun Cheng; Guido Novati; Joshua Pan; et al. 2023. Sciencejournal articleCited in: [Evaluation Principles for Life Sciences AI](foundations/evaluation-principles.html#sec-evaluation-prospective)
- [Benchmarking biomedical foundation models](https://doi.org/10.1038/s41592-026-03182-y)Julio Saez-Rodriguez; Philipp Sven Lars Schäfer; Nikolas Kalavros; Gustavo Stolovitzky. 2026. Nature Methodsjournal articleCited in: [Evaluation Principles for Life Sciences AI](foundations/evaluation-principles.html#sec-evaluation-blind-benchmarks)
- [Highly accurate protein structure prediction with AlphaFold](https://doi.org/10.1038/s41586-021-03819-2)John Jumper; Richard Evans; Alexander Pritzel; et al. 2021. Naturejournal articleCited in: [Evaluation Principles for Life Sciences AI](foundations/evaluation-principles.html#sec-evaluation-calibration)
- [Kryshtafovych et al., 2024](https://pmc.ncbi.nlm.nih.gov/articles/PMC10843301/)Cited in: [Evaluation Principles for Life Sciences AI](foundations/evaluation-principles.html#sec-evaluation-blind-benchmarks)
- [Deep learning perturbation models can outperform baselines on calibrated metrics](https://doi.org/10.1038/s41587-026-03307-w)Miller et al. 2026. Nature Biotechnologyjournal articleCited in: [Evaluation Principles for Life Sciences AI](foundations/evaluation-principles.html#sec-evaluation-calibration)
- [Miller, 2024, preprint](https://arxiv.org/abs/2411.00640)Cited in: [Evaluation Principles for Life Sciences AI](foundations/evaluation-principles.html#sec-evaluation-statistical-uncertainty)
- [World models for biomedicine](https://doi.org/10.1016/j.cell.2026.08.032)Ayush Noori; Nic Fishman; Ada Fang; Lukas Fesser; Marinka Zitnik. 2026. Celljournal articleCited in: [Evaluation Principles for Life Sciences AI](foundations/evaluation-principles.html#sec-evaluation-claim-evidence-matrix)
- [Intrinsic dataset features drive mutational effect prediction by protein language models](https://doi.org/10.64898/2026.03.08.710389)Luiz C. Vieira; Sophia Lin; Claus O. Wilke. 2026preprintCited in: [Evaluation Principles for Life Sciences AI](foundations/evaluation-principles.html#sec-evaluation-splits)
- [Most Ligand-Based Classification Benchmarks Reward Memorization Rather than Generalization](https://doi.org/10.1021/acs.jcim.7b00403)Izhar Wallach; Abraham Heifets. 2018. Journal of Chemical Information and Modelingjournal articleCited in: [Evaluation Principles for Life Sciences AI](foundations/evaluation-principles.html#sec-evaluation-splits)
- [DOME: recommendations for supervised machine learning validation in biology](https://doi.org/10.1038/s41592-021-01205-4)Ian Walsh; Dmytro Fishman; Dario Garcia-Gasulla; et al. 2021. Nature Methodsjournal articleCited in: [Evaluation Principles for Life Sciences AI](foundations/evaluation-principles.html#sec-evaluation-dome)
- [MoleculeNet: a benchmark for molecular machine learning](https://doi.org/10.1039/C7SC02664A)Zhenqin Wu; Bharath Ramsundar; Evan N. Feinberg; et al. 2018. Chemical Sciencejournal articleCited in: [Evaluation Principles for Life Sciences AI](foundations/evaluation-principles.html#sec-evaluation-splits)

## Part II: Molecular Discovery and Design

### Protein Structure Prediction

#### References

- [Accurate structure prediction of biomolecular interactions with AlphaFold 3](https://doi.org/10.1038/s41586-024-07487-w)Josh Abramson; Jonas Adler; Jack Dunger; et al. 2024. Naturejournal articleCited in: [Protein Structure Prediction (passage 1)](molecular/protein-structure.html); [Protein Structure Prediction (passage 2)](molecular/protein-structure.html#sec-protein-structure-introduction); [Protein Structure Prediction (passage 3)](molecular/protein-structure.html#complex-and-interaction-prediction); [Protein Structure Prediction (passage 4)](molecular/protein-structure.html#sec-protein-structure-alphafold3-open-access); [Protein Structure Prediction (passage 5)](molecular/protein-structure.html#sec-protein-structure-common-questions)
- [Addendum: Accurate structure prediction of biomolecular interactions with AlphaFold 3](https://doi.org/10.1038/s41586-024-08416-7)Josh Abramson; Jonas Adler; Jack Dunger; et al. 2024. Naturejournal articleCited in: [Protein Structure Prediction](molecular/protein-structure.html#sec-protein-structure-alphafold3-open-access)
- [A structural biology community assessment of AlphaFold2 applications](https://doi.org/10.1038/s41594-022-00849-w)Mehmet Akdel; Douglas E. V. Pires; Eduard Porta Pardo; et al. 2022. Nature Structural & Molecular Biologyjournal articleCited in: [Protein Structure Prediction](molecular/protein-structure.html#plddt-predicted-local-distance-difference-test)
- [Accurate prediction of protein structures and interactions using a three-track neural network](https://doi.org/10.1126/science.abj8754)Minkyung Baek; Frank DiMaio; Ivan Anishchenko; et al. 2021. Sciencejournal articleCited in: [Protein Structure Prediction](molecular/protein-structure.html#single-chain-prediction-alphafold-2-and-successors)
- [Improved prediction of protein-protein interactions using AlphaFold2](https://doi.org/10.1038/s41467-022-28865-w)Patrick Bryant; Gabriele Pozzati; Arne Elofsson. 2022. Nature Communicationsjournal articleCited in: [Protein Structure Prediction](molecular/protein-structure.html#complex-and-interaction-prediction)
- [Chai-1: Decoding the molecular interactions of life](https://doi.org/10.1101/2024.10.10.615955)Chai Discovery; Jacques Boitreaud; Jack Dent; et al. 2024preprintCited in: [Protein Structure Prediction (passage 1)](molecular/protein-structure.html); [Protein Structure Prediction (passage 2)](molecular/protein-structure.html#sec-protein-structure-introduction); [Protein Structure Prediction (passage 3)](molecular/protein-structure.html#complex-and-interaction-prediction); [Protein Structure Prediction (passage 4)](molecular/protein-structure.html#sec-protein-structure-alphafold3-open-access); [Protein Structure Prediction (passage 5)](molecular/protein-structure.html#sec-protein-structure-common-questions)
- [Accurate proteome-wide missense variant effect prediction with AlphaMissense](https://doi.org/10.1126/science.adg7492)Jun Cheng; Guido Novati; Joshua Pan; et al. 2023. Sciencejournal articleCited in: [Protein Structure Prediction](molecular/protein-structure.html#sec-protein-structure-downstream-applications)
- [EMBL-EBI AlphaFold training](https://www.ebi.ac.uk/training/online/courses/alphafold/inputs-and-outputs/evaluating-alphafolds-predicted-structures-using-confidence-scores/confidence-scores-in-alphafold-multimer/)Cited in: [Protein Structure Prediction](molecular/protein-structure.html#iptm-interface-predicted-tm-score-complexes-only)
- [Protein complex prediction with AlphaFold-Multimer](https://doi.org/10.1101/2021.10.04.463034)Richard Evans; Michael O’Neill; Alexander Pritzel; et al. 2021preprintCited in: [Protein Structure Prediction](molecular/protein-structure.html#complex-and-interaction-prediction)
- [AlphaFold 3 Source Code License (Apache License 2.0, June 2026)](https://github.com/google-deepmind/alphafold3/blob/main/LICENSE)Google DeepMind. 2026. GitHubdocumentationCited in: [Protein Structure Prediction](molecular/protein-structure.html#sec-protein-structure-alphafold3-open-access)
- [Google DeepMind, AlphaFold 3 Model Parameters Terms of Use, November 2024 release](https://github.com/google-deepmind/alphafold3/blob/v3.0.0/WEIGHTS_TERMS_OF_USE.md)Cited in: [Protein Structure Prediction](molecular/protein-structure.html#sec-protein-structure-alphafold3-open-access)
- [Simulating 500 million years of evolution with a language model](https://doi.org/10.1126/science.ads0018)Thomas Hayes; Roshan Rao; Halil Akin; et al. 2025. Sciencejournal articleCited in: [Protein Structure Prediction](molecular/protein-structure.html#sec-protein-structure-frontier-directions)
- [AlphaFill: enriching AlphaFold models with ligands and cofactors](https://doi.org/10.1038/s41592-022-01685-y)Maarten L. Hekkelman; Ida de Vries; Robbie P. Joosten; et al. 2022. Nature Methodsjournal articleCited in: [Protein Structure Prediction](molecular/protein-structure.html#sec-protein-structure-downstream-applications)
- [Highly accurate protein structure prediction with AlphaFold](https://doi.org/10.1038/s41586-021-03819-2)John Jumper; Richard Evans; Alexander Pritzel; et al. 2021. Naturejournal articleCited in: [Protein Structure Prediction (passage 1)](molecular/protein-structure.html); [Protein Structure Prediction (passage 2)](molecular/protein-structure.html#sec-protein-structure-introduction); [Protein Structure Prediction (passage 3)](molecular/protein-structure.html#single-chain-prediction-alphafold-2-and-successors); [Protein Structure Prediction (passage 4)](molecular/protein-structure.html#sec-protein-structure-common-questions)
- [ESMDynamic: Fast and accurate prediction of protein dynamic contact maps from single sequences](https://doi.org/10.1038/s41467-026-76361-2)Diego E. Kleiman; Jiangyan Feng; Zhengyuan Xue; et al. 2026. Nature Communicationsjournal articleCited in: [Protein Structure Prediction](molecular/protein-structure.html#sec-protein-structure-frontier-directions)
- [Evolutionary-scale prediction of atomic-level protein structure with a language model](https://doi.org/10.1126/science.ade2574)Zeming Lin; Halil Akin; Roshan Rao; et al. 2023. Sciencejournal articleCited in: [Protein Structure Prediction](molecular/protein-structure.html#msa-free-prediction-esmfold-and-omegafold)
- [Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction](https://doi.org/10.1101/2025.06.14.659707)Saro Passaro; Gabriele Corso; Jeremy Wohlwend; et al. 2025preprintCited in: [Protein Structure Prediction (passage 1)](molecular/protein-structure.html#complex-and-interaction-prediction); [Protein Structure Prediction (passage 2)](molecular/protein-structure.html#sec-protein-structure-alphafold3-open-access); [Protein Structure Prediction (passage 3)](molecular/protein-structure.html#sec-protein-structure-frontier-directions)
- [Highly accurate protein structure prediction for the human proteome](https://doi.org/10.1038/s41586-021-03828-1)Kathryn Tunyasuvunakool; Jonas Adler; Zachary Wu; et al. 2021. Naturejournal articleCited in: [Protein Structure Prediction](molecular/protein-structure.html#sec-protein-structure-introduction)
- [AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models](https://doi.org/10.1093/nar/gkab1061)Mihaly Varadi; Stephen Anyango; Mandar Deshpande; et al. 2021. Nucleic Acids Researchjournal articleCited in: [Protein Structure Prediction (passage 1)](molecular/protein-structure.html#sec-protein-structure-introduction); [Protein Structure Prediction (passage 2)](molecular/protein-structure.html#sec-protein-structure-common-questions)
- [AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences](https://doi.org/10.1093/nar/gkad1011)Mihaly Varadi; Damian Bertoni; Paulyna Magana; et al. 2023. Nucleic Acids Researchjournal articleCited in: [Protein Structure Prediction (passage 1)](molecular/protein-structure.html); [Protein Structure Prediction (passage 2)](molecular/protein-structure.html#sec-protein-structure-introduction); [Protein Structure Prediction (passage 3)](molecular/protein-structure.html#sec-protein-structure-common-questions)
- [De novo design of protein structure and function with RFdiffusion](https://doi.org/10.1038/s41586-023-06415-8)Joseph L. Watson; David Juergens; Nathaniel R. Bennett; et al. 2023. Naturejournal articleCited in: [Protein Structure Prediction](molecular/protein-structure.html#sec-protein-structure-downstream-applications)
- [Learning millisecond protein dynamics from what is missing in NMR spectra](https://doi.org/10.1038/s41586-026-10989-4)Hannah K. Wayment-Steele; Gina El Nesr; Ramith Hettiarachchi; et al. 2026. Naturejournal articleCited in: [Protein Structure Prediction](molecular/protein-structure.html#sec-protein-structure-frontier-directions)
- [Boltz-1 Democratizing Biomolecular Interaction Modeling](https://doi.org/10.1101/2024.11.19.624167)Jeremy Wohlwend; Gabriele Corso; Saro Passaro; et al. 2024preprintCited in: [Protein Structure Prediction (passage 1)](molecular/protein-structure.html); [Protein Structure Prediction (passage 2)](molecular/protein-structure.html#sec-protein-structure-introduction); [Protein Structure Prediction (passage 3)](molecular/protein-structure.html#complex-and-interaction-prediction); [Protein Structure Prediction (passage 4)](molecular/protein-structure.html#sec-protein-structure-alphafold3-open-access); [Protein Structure Prediction (passage 5)](molecular/protein-structure.html#sec-protein-structure-common-questions)
- [High-resolution de novo structure prediction from primary sequence](https://doi.org/10.1101/2022.07.21.500999)Ruidong Wu; Fan Ding; Rui Wang; et al. 2022preprintCited in: [Protein Structure Prediction](molecular/protein-structure.html#msa-free-prediction-esmfold-and-omegafold)

### Protein Design and Engineering

#### References

- [Accurate structure prediction of biomolecular interactions with AlphaFold 3](https://doi.org/10.1038/s41586-024-07487-w)Josh Abramson; Jonas Adler; Jack Dunger; et al. 2024. Naturejournal articleCited in: [Protein Design and Engineering](molecular/protein-design.html#sec-protein-design-rfantibody)
- [Protein generation with evolutionary diffusion: sequence is all you need](https://doi.org/10.1101/2023.09.11.556673)Sarah Alamdari; Nitya Thakkar; Rianne van den Berg; et al. 2023preprintCited in: [Protein Design and Engineering](molecular/protein-design.html#sec-protein-design-generative)
- [Anthropic, August 2026](https://www.anthropic.com/research/Claude-accelerates-protein-design)Cited in: [Protein Design and Engineering](molecular/protein-design.html#sec-protein-design-generative)
- [Atomically accurate de novo design of antibodies with RFdiffusion](https://doi.org/10.1038/s41586-025-09721-5)Nathaniel R. Bennett; Joseph L. Watson; Robert J. Ragotte; et al. 2025. Naturejournal articleCited in: [Protein Design and Engineering (passage 1)](molecular/protein-design.html#protein-design-introduction); [Protein Design and Engineering (passage 2)](molecular/protein-design.html#sec-protein-design-rfantibody)
- [AI-enabled discovery and biochemical optimization of minibinders targeting cancer cell-surface proteins](https://doi.org/10.1038/s41467-026-76760-5)Bianca Broske; Benjamin A. McEnroe; Sophie C. Frechen; et al. 2026. Nature Communicationsjournal articleCited in: [Protein Design and Engineering](molecular/protein-design.html#sec-protein-design-active-learning)
- [De novo Design of All-atom Biomolecular Interactions with RFdiffusion3](https://doi.org/10.1101/2025.09.18.676967)Jasper Butcher; Rohith Krishna; Raktim Mitra; et al. 2025preprintCited in: [Protein Design and Engineering](molecular/protein-design.html#sec-protein-design-rfdiffusion)
- [Generative AI designs functional thiolation domains for reprogramming non-ribosomal peptide synthetases](https://doi.org/10.1038/s41467-026-77963-6)Emre F. Bülbül; Seounggun Bang; Kevin George; et al. 2026. Nature Communicationsjournal articleCited in: [Protein Design and Engineering](molecular/protein-design.html#sec-protein-design-generative)
- [Chen et al., 2026](https://doi.org/10.1038/s41586-026-11167-2)Cited in: [Protein Design and Engineering](molecular/protein-design.html#protein-design-theoretical)
- [Choi et al., 2026](https://doi.org/10.1038/s41586-026-11143-w)Cited in: [Protein Design and Engineering](molecular/protein-design.html#protein-design-theoretical)
- [Programmable design of functional proteins from natural language](https://doi.org/10.1038/s41586-026-11205-z)Fengyuan Dai; Shiyang You; Yudian Zhu; et al. 2026. Naturejournal articleCited in: [Protein Design and Engineering](molecular/protein-design.html#sec-protein-design-generative)
- [Robust deep learning–based protein sequence design using ProteinMPNN](https://doi.org/10.1126/science.add2187)J. Dauparas; I. Anishchenko; N. Bennett; et al. 2022. Sciencejournal articleCited in: [Protein Design and Engineering (passage 1)](molecular/protein-design.html#protein-design-introduction); [Protein Design and Engineering (passage 2)](molecular/protein-design.html#sec-protein-design-mpnn)
- [Atomic context-conditioned protein sequence design using LigandMPNN](https://doi.org/10.1038/s41592-025-02626-1)Justas Dauparas; Gyu Rie Lee; Robert Pecoraro; et al. 2025. Nature Methodsjournal articleCited in: [Protein Design and Engineering (passage 1)](molecular/protein-design.html#protein-design-introduction); [Protein Design and Engineering (passage 2)](molecular/protein-design.html#sec-protein-design-mpnn)
- [Baker DMX Barcoding Kit](https://www.addgene.org/kits/baker-dmx-barcoding/)David Baker. 2026. AddgenewebsiteCited in: [Protein Design and Engineering](molecular/protein-design.html#sec-protein-design-generative)
- [Introducing SynthID Bio](https://deepmind.google/blog/introducing-synthid-bio/)Google DeepMind. 2026. Google DeepMind Blogblog postCited in: [Protein Design and Engineering](molecular/protein-design.html#sec-protein-design-mpnn)
- [Protein complex prediction with AlphaFold-Multimer](https://doi.org/10.1101/2021.10.04.463034)Richard Evans; Michael O’Neill; Alexander Pritzel; et al. 2021preprintCited in: [Protein Design and Engineering](molecular/protein-design.html#sec-protein-design-rfantibody)
- [Discovery and design of potent cell surface display elements](https://doi.org/10.1038/s41587-026-03144-x)Zhenhao Fang; Joshua Saskin; Seok-Hoon Lee; et al. 2026. Nature Biotechnologyjournal articleCited in: [Protein Design and Engineering](molecular/protein-design.html#sec-protein-design-active-learning)
- [Zero-shot design of drug-binding proteins via neural iterative selection−expansion](https://doi.org/10.1038/s41586-026-10670-w)Benjamin Fry; Kaia Slaw; Nicholas F. Polizzi. 2026. Naturejournal articleCited in: [Protein Design and Engineering](molecular/protein-design.html#sec-protein-design-small-molecule-binders)
- [Simulating 500 million years of evolution with a language model](https://doi.org/10.1126/science.ads0018)Thomas Hayes; Roshan Rao; Halil Akin; et al. 2025. Sciencejournal articleCited in: [Protein Design and Engineering](molecular/protein-design.html#sec-protein-design-generative)
- [inference code, training code, and model weights](https://github.com/RosettaCommons/foundry/blob/production/models/rfd3/README.md)Cited in: [Protein Design and Engineering](molecular/protein-design.html#sec-protein-design-rfdiffusion)
- [Illuminating protein space with a programmable generative model](https://doi.org/10.1038/s41586-023-06728-8)John B. Ingraham; Max Baranov; Zak Costello; et al. 2023. Naturejournal articleCited in: [Protein Design and Engineering](molecular/protein-design.html#sec-protein-design-generative)
- [Accelerating protein design by scaling experimental characterization](https://doi.org/10.1038/s41467-026-76740-9)Jason Qian; Lukas F. Milles; Basile I. M. Wicky; Robert J. Ragotte; Amir Motmaen; Andrew J. Borst; Rebecca Skotheim; Sebastian Ols; Brian Coventry; Xinting Li; Ryan D. Kibler; Inna Goreshnik; Marc Expòsit; Karin Loré; Lance Stewart; David Baker. 2026. Nature Communicationsjournal articleCited in: [Protein Design and Engineering](molecular/protein-design.html#sec-protein-design-generative)
- [De Novo Computational Design of Retro-Aldol Enzymes](https://doi.org/10.1126/science.1152692)Lin Jiang; Eric A. Althoff; Fernando R. Clemente; et al. 2008. Sciencejournal articleCited in: [Protein Design and Engineering](molecular/protein-design.html#protein-design-theoretical)
- [Rapid in silico directed evolution by a protein language model with EVOLVEpro](https://doi.org/10.1126/science.adr6006)Kaiyi Jiang; Zhaoqing Yan; Matteo Di Bernardo; et al. 2025. Sciencejournal articleCited in: [Protein Design and Engineering](molecular/protein-design.html#sec-protein-design-active-learning)
- [Computational scoring and experimental evaluation of enzymes generated by neural networks](https://doi.org/10.1038/s41587-024-02214-2)Sean R. Johnson; Xiaozhi Fu; Sandra Viknander; et al. 2024. Nature Biotechnologyjournal articleCited in: [Protein Design and Engineering](molecular/protein-design.html#sec-protein-design-active-learning)
- [Validation and analysis of 12,000 AI-driven CAR-T designs in the Bits to Binders competition](https://doi.org/10.1038/s44320-026-00246-1)Clayton W Kosonocky; Alex M Abel; Aaron L Feller; et al. 2026. Molecular Systems Biologyjournal articleCited in: [Protein Design and Engineering](molecular/protein-design.html#sec-protein-design-active-learning)
- [Rosetta3](https://doi.org/10.1016/B978-0-12-381270-4.00019-6)Andrew Leaver-Fay; Michael Tyka; Steven M. Lewis; et al. 2011. Methods in Enzymologybook chapterCited in: [Protein Design and Engineering](molecular/protein-design.html#protein-design-introduction)
- [Design-Evolution Synergy Enables New-to-Nature Enzyme Chemistry](https://doi.org/10.64898/2026.10.01.754751)Zi-Qi Li; Ruijie K Zhang; Pei-Pei Xie; et al. 2026. bioRxivpreprintCited in: [Protein Design and Engineering](molecular/protein-design.html#protein-design-theoretical)
- [Multistate and functional protein design using RoseTTAFold sequence space diffusion](https://doi.org/10.1038/s41587-024-02395-w)Sidney Lyayuga Lisanza; Jacob Merle Gershon; Samuel W. K. Tipps; et al. 2024. Nature Biotechnologyjournal articleCited in: [Protein Design and Engineering](molecular/protein-design.html#sec-protein-design-generative)
- [Efficient generation of epitope-targeted antibodies with Germinal](https://doi.org/10.1038/s41587-026-03187-0)Luis S. Mille-Fragoso; Claudia L. Driscoll; John N. Wang; et al. 2026. Nature Biotechnologyjournal articleCited in: [Protein Design and Engineering](molecular/protein-design.html#sec-protein-design-rfantibody)
- [ProGen2: Exploring the boundaries of protein language models](https://doi.org/10.1016/j.cels.2023.10.002)Erik Nijkamp; Jeffrey A. Ruffolo; Eli N. Weinstein; et al. 2023. Cell Systemsjournal articleCited in: [Protein Design and Engineering](molecular/protein-design.html#sec-protein-design-generative)
- [Nobel Prize, 2024](https://www.nobelprize.org/prizes/chemistry/2024/summary/)Cited in: [Protein Design and Engineering](molecular/protein-design.html#protein-design-introduction)
- [AbLang: an antibody language model for completing antibody sequences](https://doi.org/10.1093/bioadv/vbac046)Tobias H Olsen; Iain H Moal; Charlotte M Deane. 2022. Bioinformatics Advancesjournal articleCited in: [Protein Design and Engineering](molecular/protein-design.html#sec-protein-design-rfantibody)
- [MutexaGPT: an intuition-to-design translator for physics-based enzyme engineering](https://doi.org/10.1038/s43588-026-01049-y)Qianzhen Shao; Yinjie Zhong; Sebastian Stull; Xinchun Ran; Ning Ding; Kieran Nehil-Puleo; Ruizhe Yao; Han Xu; Zhongyue J. Yang. 2026. Nature Computational Sciencejournal articleCited in: [Protein Design and Engineering](molecular/protein-design.html#protein-design-theoretical)
- [Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies](https://doi.org/10.1038/s41467-023-38063-x)Jeffrey A. Ruffolo; Lee-Shin Chu; Sai Pooja Mahajan; et al. 2023. Nature Communicationsjournal articleCited in: [Protein Design and Engineering](molecular/protein-design.html#sec-protein-design-rfantibody)
- [Kemp elimination catalysts by computational enzyme design](https://doi.org/10.1038/nature06879)Daniela Röthlisberger; Olga Khersonsky; Andrew M. Wollacott; et al. 2008. Naturejournal articleCited in: [Protein Design and Engineering](molecular/protein-design.html#protein-design-theoretical)
- [Function-preserving watermarking of AI-generated proteins](https://doi.org/10.1038/s41586-026-10965-y)David Stutz; Alexander I. Cowen-Rivers; Guillermo Ortiz-Jimenez; et al. 2026. Naturejournal articleCited in: [Protein Design and Engineering](molecular/protein-design.html#sec-protein-design-mpnn)
- [Intuitive enzyme design with LLM agents](https://doi.org/10.1038/s43588-026-01052-3)Terra Sztain. 2026. Nature Computational SciencecommentaryCited in: [Protein Design and Engineering](molecular/protein-design.html#protein-design-theoretical)
- [Adaptive model-guided protein evolution with sparse data optimizes compact eukaryotic genome editors](https://doi.org/10.1038/s41587-026-03272-4)Shijie Wan; Jackson Gold; Pranay Vure; et al. 2026. Nature Biotechnologyjournal articleCited in: [Protein Design and Engineering](molecular/protein-design.html#sec-protein-design-active-learning)
- [De novo design of protein structure and function with RFdiffusion](https://doi.org/10.1038/s41586-023-06415-8)Joseph L. Watson; David Juergens; Nathaniel R. Bennett; et al. 2023. Naturejournal articleCited in: [Protein Design and Engineering (passage 1)](molecular/protein-design.html#protein-design-introduction); [Protein Design and Engineering (passage 2)](molecular/protein-design.html#sec-protein-design-rfdiffusion)
- [Aligning protein-generative models to experimental fitness with ProteinDPO](https://doi.org/10.1038/s41592-026-03137-3)Talal Widatalla; Ashir A. Borah; Samuel H. King; et al. 2026. Nature Methodsjournal articleCited in: [Protein Design and Engineering](molecular/protein-design.html#sec-protein-design-mpnn)
- [Designing enzymes for new-to-nature chemistry and non-natural substrates with AlphaProtein Novo](https://doi.org/10.64898/2026.10.01.756017)Zachary Wu; Joshua Abramson; Thomas Frerix; et al. 2026. bioRxivpreprintCited in: [Protein Design and Engineering](molecular/protein-design.html#protein-design-theoretical)
- [Zambaldi et al., 2024, preprint](https://arxiv.org/abs/2409.08022)Cited in: [Protein Design and Engineering](molecular/protein-design.html#sec-protein-design-generative)

### Antibody and Biologic Design

#### References

- [Accurate structure prediction of biomolecular interactions with AlphaFold 3](https://doi.org/10.1038/s41586-024-07487-w)Josh Abramson; Jonas Adler; Jack Dunger; et al. 2024. Naturejournal articleCited in: [Antibody and Biologic Design (passage 1)](molecular/antibodies.html#antibody-biologic-design-introduction); [Antibody and Biologic Design (passage 2)](molecular/antibodies.html#antibody-biologic-design-demonstrated)
- [Atomically accurate de novo design of antibodies with RFdiffusion](https://doi.org/10.1038/s41586-025-09721-5)Nathaniel R. Bennett; Joseph L. Watson; Robert J. Ragotte; et al. 2025. Naturejournal articleCited in: [Antibody and Biologic Design (passage 1)](molecular/antibodies.html#antibody-biologic-design-introduction); [Antibody and Biologic Design (passage 2)](molecular/antibodies.html#antibody-biologic-design-demonstrated)
- [A blinded, prospective benchmark of in silico antibody discovery anchored to experimental affinity and developability](https://doi.org/10.1038/s41587-026-03238-6)M. Frank Erasmus; Daniel Bedinger; Elizabeth Hopkins; et al. 2026. Nature Biotechnologyjournal articleCited in: [Antibody and Biologic Design](molecular/antibodies.html#antibody-biologic-design-demonstrated)
- [Efficient evolution of human antibodies from general protein language models](https://doi.org/10.1038/s41587-023-01763-2)Brian L. Hie; Varun R. Shanker; Duo Xu; et al. 2023. Nature Biotechnologyjournal articleCited in: [Antibody and Biologic Design](molecular/antibodies.html#antibody-biologic-design-demonstrated)
- [A strategy for risk mitigation of antibodies with fast clearance](https://doi.org/10.4161/mabs.22189)Isidro Hötzel; Frank-Peter Theil; Lisa J. Bernstein; et al. 2012. mAbsjournal articleCited in: [Antibody and Biologic Design (passage 1)](molecular/antibodies.html#antibody-biologic-design-developability); [Antibody and Biologic Design (passage 2)](molecular/antibodies.html#antibody-biologic-design-demonstrated)
- [Biophysical properties of the clinical-stage antibody landscape](https://doi.org/10.1073/pnas.1616408114)Tushar Jain; Tingwan Sun; Stéphanie Durand; et al. 2017. Proceedings of the National Academy of Sciencesjournal articleCited in: [Antibody and Biologic Design (passage 1)](molecular/antibodies.html#antibody-biologic-design-developability); [Antibody and Biologic Design (passage 2)](molecular/antibodies.html#antibody-biologic-design-demonstrated)
- [Five computational developability guidelines for therapeutic antibody profiling](https://doi.org/10.1073/pnas.1810576116)Matthew I. J. Raybould; Claire Marks; Konrad Krawczyk; et al. 2019. Proceedings of the National Academy of Sciencesjournal articleCited in: [Antibody and Biologic Design (passage 1)](molecular/antibodies.html#antibody-biologic-design-developability); [Antibody and Biologic Design (passage 2)](molecular/antibodies.html#antibody-biologic-design-demonstrated)
- [Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies](https://doi.org/10.1038/s41467-023-38063-x)Jeffrey A. Ruffolo; Lee-Shin Chu; Sai Pooja Mahajan; et al. 2023. Nature Communicationsjournal articleCited in: [Antibody and Biologic Design](molecular/antibodies.html#antibody-biologic-design-demonstrated)
- [Unsupervised evolution of protein and antibody complexes with a structure-informed language model](https://doi.org/10.1126/science.adk8946)Varun R. Shanker; Theodora U. J. Bruun; Brian L. Hie; et al. 2024. Sciencejournal articleCited in: [Antibody and Biologic Design](molecular/antibodies.html#antibody-biologic-design-demonstrated)
- [De novo design of protein structure and function with RFdiffusion](https://doi.org/10.1038/s41586-023-06415-8)Joseph L. Watson; David Juergens; Nathaniel R. Bennett; et al. 2023. Naturejournal articleCited in: [Antibody and Biologic Design (passage 1)](molecular/antibodies.html#antibody-biologic-design-introduction); [Antibody and Biologic Design (passage 2)](molecular/antibodies.html#antibody-biologic-design-demonstrated)
- [De Novo Design of Progressable Antibodies to Therapeutic Targets](https://www.xaira.com/news/de-novo-design-of-progressable-antibodies-to-therapeutic-targets)Xaira Design Team. 2026. Xaira Therapeuticscompany research postCited in: [Antibody and Biologic Design](molecular/antibodies.html#antibody-biologic-design-demonstrated)

### Nucleic Acid and Genome Models

#### References

- [Effective gene expression prediction from sequence by integrating long-range interactions](https://doi.org/10.1038/s41592-021-01252-x)Žiga Avsec; Vikram Agarwal; Daniel Visentin; et al. 2021. Nature Methodsjournal articleCited in: [Nucleic Acid and Genome Models (passage 1)](molecular/genome-models.html#nucleic-acid-genome-models-regulatory-spine); [Nucleic Acid and Genome Models (passage 2)](molecular/genome-models.html#nucleic-acid-genome-models-demonstrated)
- [Advancing regulatory variant effect prediction with AlphaGenome](https://doi.org/10.1038/s41586-025-10014-0)Žiga Avsec; Natasha Latysheva; Jun Cheng; et al. 2026. Naturejournal articleCited in: [Nucleic Acid and Genome Models (passage 1)](molecular/genome-models.html#nucleic-acid-genome-models-introduction); [Nucleic Acid and Genome Models (passage 2)](molecular/genome-models.html#nucleic-acid-genome-models-regulatory-spine); [Nucleic Acid and Genome Models (passage 3)](molecular/genome-models.html#nucleic-acid-genome-models-demonstrated)
- [Genome modelling and design across all domains of life with Evo 2](https://doi.org/10.1038/s41586-026-10176-5)Garyk Brixi; Matthew G. Durrant; Jerome Ku; et al. 2026. Naturejournal articleCited in: [Nucleic Acid and Genome Models (passage 1)](molecular/genome-models.html#nucleic-acid-genome-models-introduction); [Nucleic Acid and Genome Models (passage 2)](molecular/genome-models.html#nucleic-acid-genome-models-demonstrated)
- [Nucleotide Transformer: building and evaluating robust foundation models for human genomics](https://doi.org/10.1038/s41592-024-02523-z)Hugo Dalla-Torre; Liam Gonzalez; Javier Mendoza-Revilla; et al. 2024. Nature Methodsjournal articleCited in: [Nucleic Acid and Genome Models](molecular/genome-models.html#nucleic-acid-genome-models-introduction)
- [Orthrus: toward evolutionary and functional RNA foundation models](https://doi.org/10.1038/s41592-026-03064-3)Philip Fradkin; Ruian “Ian” Shi; Taykhoom Dalal; et al. 2026. Nature Methodsjournal articleCited in: [Nucleic Acid and Genome Models](molecular/genome-models.html#nucleic-acid-genome-models-demonstrated)
- [A foundation model of transcription across human cell types](https://doi.org/10.1038/s41586-024-08391-z)Xi Fu; Shentong Mo; Alejandro Buendia; et al. 2025. Naturejournal articleCited in: [Nucleic Acid and Genome Models (passage 1)](molecular/genome-models.html#nucleic-acid-genome-models-introduction); [Nucleic Acid and Genome Models (passage 2)](molecular/genome-models.html#nucleic-acid-genome-models-regulatory-spine)
- [Systematic mapping of functional enhancer–promoter connections with CRISPR interference](https://doi.org/10.1126/science.aag2445)Charles P. Fulco; Mathias Munschauer; Rockwell Anyoha; et al. 2016. Sciencejournal articleCited in: [Nucleic Acid and Genome Models](molecular/genome-models.html#nucleic-acid-genome-models-demonstrated)
- [Decoding the sequence determinants of locus-specific DNA methylation across human tissues](https://doi.org/10.1038/s41467-026-76744-5)Junru Jin; Ding Wang; Jianbo Qiao; et al. 2026. Nature Communicationsjournal articleCited in: [Nucleic Acid and Genome Models](molecular/genome-models.html#nucleic-acid-genome-models-demonstrated)
- [Generative design of bacteriophages with genome language models](https://doi.org/10.1126/science.aec2657)Samuel H. King; Claudia L. Driscoll; David B. Li; et al. 2026. Sciencejournal articleCited in: [Nucleic Acid and Genome Models](molecular/genome-models.html#nucleic-acid-genome-models-demonstrated)
- [Semantic design of functional de novo genes from a genomic language model](https://doi.org/10.1038/s41586-025-09749-7)Aditi T. Merchant; Samuel H. King; Eric Nguyen; et al. 2025. Naturejournal articleCited in: [Nucleic Acid and Genome Models (passage 1)](molecular/genome-models.html#nucleic-acid-genome-models-introduction); [Nucleic Acid and Genome Models (passage 2)](molecular/genome-models.html#nucleic-acid-genome-models-demonstrated)
- [Sequence modeling and design from molecular to genome scale with Evo](https://doi.org/10.1126/science.ado9336)Eric Nguyen; Michael Poli; Matthew G. Durrant; et al. 2024. Sciencejournal articleCited in: [Nucleic Acid and Genome Models (passage 1)](molecular/genome-models.html#nucleic-acid-genome-models-introduction); [Nucleic Acid and Genome Models (passage 2)](molecular/genome-models.html#nucleic-acid-genome-models-demonstrated)
- [Phylogeny-agnostic strain-level prediction of phage–host interactions from genomes using machine learning](https://doi.org/10.1038/s41564-026-02482-5)Avery J. C. Noonan; Lucas Moriniere; Edwin O. Rivera-López; et al. 2026. Nature Microbiologyjournal articleCited in: [Nucleic Acid and Genome Models](molecular/genome-models.html#nucleic-acid-genome-models-demonstrated)
- [Accurate RNA 3D structure prediction using a language model-based deep learning approach](https://doi.org/10.1038/s41592-024-02487-0)Tao Shen; Zhihang Hu; Siqi Sun; et al. 2024. Nature Methodsjournal articleCited in: [Nucleic Acid and Genome Models](molecular/genome-models.html#nucleic-acid-genome-models-demonstrated)
- [Direct Identification of Hundreds of Expression-Modulating Variants using a Multiplexed Reporter Assay](https://doi.org/10.1016/j.cell.2016.04.027)Ryan Tewhey; Dylan Kotliar; Daniel S. Park; et al. 2016. Celljournal articleCited in: [Nucleic Acid and Genome Models](molecular/genome-models.html#nucleic-acid-genome-models-demonstrated)
- [De novo design of RNA pseudoknots with deep learning](https://doi.org/10.1126/science.aeg6829)Jill Townley; Wipapat Kladwang; David Baker; et al. 2026. Sciencejournal articleCited in: [Nucleic Acid and Genome Models](molecular/genome-models.html#nucleic-acid-genome-models-demonstrated)
- [A generalizable Hi-C foundation model for chromatin architecture, single-cell and multiomics analysis across species](https://doi.org/10.1038/s41592-026-03097-8)Xiao Wang; Yuanyuan Zhang; Suhita Ray; et al. 2026. Nature Methodsjournal articleCited in: [Nucleic Acid and Genome Models](molecular/genome-models.html#nucleic-acid-genome-models-demonstrated)
- [Predicting effects of noncoding variants with deep learning–based sequence model](https://doi.org/10.1038/nmeth.3547)Jian Zhou; Olga G Troyanskaya. 2015. Nature Methodsjournal articleCited in: [Nucleic Acid and Genome Models (passage 1)](molecular/genome-models.html#nucleic-acid-genome-models-regulatory-spine); [Nucleic Acid and Genome Models (passage 2)](molecular/genome-models.html#nucleic-acid-genome-models-demonstrated)

### Variant Effect Prediction

#### References

- [Effective gene expression prediction from sequence by integrating long-range interactions](https://doi.org/10.1038/s41592-021-01252-x)Žiga Avsec; Vikram Agarwal; Daniel Visentin; et al. 2021. Nature Methodsjournal articleCited in: [Variant Effect Prediction](molecular/variant-effects.html#sec-variant-effects-noncoding)
- [Advancing regulatory variant effect prediction with AlphaGenome](https://doi.org/10.1038/s41586-025-10014-0)Žiga Avsec; Natasha Latysheva; Jun Cheng; et al. 2026. Naturejournal articleCited in: [Variant Effect Prediction](molecular/variant-effects.html#sec-variant-effects-noncoding)
- [A DNA language model based on multispecies alignment predicts the effects of genome-wide variants](https://doi.org/10.1038/s41587-024-02511-w)Gonzalo Benegas; Carlos Albors; Alan J. Aw; et al. 2025. Nature Biotechnologyjournal articleCited in: [Variant Effect Prediction](molecular/variant-effects.html#sec-variant-effects-noncoding)
- [Brnich et al., 2020](https://doi.org/10.1186/s13073-019-0690-2)Cited in: [Variant Effect Prediction](molecular/variant-effects.html#sec-variant-effects-acmg)
- [Accurate proteome-wide missense variant effect prediction with AlphaMissense](https://doi.org/10.1126/science.adg7492)Jun Cheng; Guido Novati; Joshua Pan; et al. 2023. Sciencejournal articleCited in: [Variant Effect Prediction](molecular/variant-effects.html#sec-variant-effects-missense)
- [Nucleotide Transformer: building and evaluating robust foundation models for human genomics](https://doi.org/10.1038/s41592-024-02523-z)Hugo Dalla-Torre; Liam Gonzalez; Javier Mendoza-Revilla; et al. 2024. Nature Methodsjournal articleCited in: [Variant Effect Prediction](molecular/variant-effects.html#sec-variant-effects-noncoding)
- [DeepMind, 2026](https://deepmind.google/blog/alphagenome-atlas-a-predictive-map-of-every-possible-dna-letter-change-in-the-human-genome/)Cited in: [Variant Effect Prediction](molecular/variant-effects.html#sec-variant-effects-noncoding)
- [Accurate classification of BRCA1 variants with saturation genome editing](https://doi.org/10.1038/s41586-018-0461-z)Gregory M. Findlay; Riza M. Daza; Beth Martin; et al. 2018. Naturejournal articleCited in: [Variant Effect Prediction](molecular/variant-effects.html#sec-variant-effects-ensemble)
- [Disease variant prediction with deep generative models of evolutionary data](https://doi.org/10.1038/s41586-021-04043-8)Jonathan Frazer; Pascal Notin; Mafalda Dias; et al. 2021. Naturejournal articleCited in: [Variant Effect Prediction](molecular/variant-effects.html#sec-variant-effects-missense)
- [REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants](https://doi.org/10.1016/j.ajhg.2016.08.016)Nilah M. Ioannidis; Joseph H. Rothstein; Vikas Pejaver; et al. 2016. The American Journal of Human Geneticsjournal articleCited in: [Variant Effect Prediction](molecular/variant-effects.html#sec-variant-effects-ensemble)
- [Predicting Splicing from Primary Sequence with Deep Learning](https://doi.org/10.1016/j.cell.2018.12.015)Kishore Jaganathan; Sofia Kyriazopoulou Panagiotopoulou; Jeremy F. McRae; et al. 2019. Celljournal articleCited in: [Variant Effect Prediction](molecular/variant-effects.html#sec-variant-effects-splicing)
- [The mutational constraint spectrum quantified from variation in 141,456 humans](https://doi.org/10.1038/s41586-020-2308-7)Konrad J. Karczewski; Laurent C. Francioli; Grace Tiao; et al. 2020. Naturejournal articleCited in: [Variant Effect Prediction](molecular/variant-effects.html#sec-variant-effects-ensemble)
- [A general framework for estimating the relative pathogenicity of human genetic variants](https://doi.org/10.1038/ng.2892)Martin Kircher; Daniela M Witten; Preti Jain; et al. 2014. Nature Geneticsjournal articleCited in: [Variant Effect Prediction](molecular/variant-effects.html#sec-variant-effects-ensemble)
- [Meier et al., 2021, NeurIPS](https://proceedings.neurips.cc/paper/2021/hash/f51338d736f95dd42427296047067694-Abstract.html)Cited in: [Variant Effect Prediction](molecular/variant-effects.html#sec-variant-effects-missense)
- [Rare variant effect estimation and polygenic risk prediction](https://doi.org/10.1038/s41588-026-02705-9)Kisung Nam; Minjung Kho; Wei Zhou; et al. 2026. Nature Geneticsjournal articleCited in: [Variant Effect Prediction](molecular/variant-effects.html#sec-variant-effects-acmg)
- [Nature](https://www.nature.com/articles/s41586-026-11005-5)Cited in: [Variant Effect Prediction](molecular/variant-effects.html#sec-variant-effects-noncoding)
- [Nature news](https://www.nature.com/articles/d41586-026-02835-4)Cited in: [Variant Effect Prediction](molecular/variant-effects.html#sec-variant-effects-noncoding)
- [Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria](https://doi.org/10.1016/j.ajhg.2022.10.013)Vikas Pejaver; Alicia B. Byrne; Bing-Jian Feng; et al. 2022. The American Journal of Human Geneticsjournal articleCited in: [Variant Effect Prediction (passage 1)](molecular/variant-effects.html#sec-variant-effects-acmg); [Variant Effect Prediction (passage 2)](molecular/variant-effects.html#variant-effects-theoretical)
- [Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology](https://doi.org/10.1038/gim.2015.30)Sue Richards; Nazneen Aziz; Sherri Bale; et al. 2015. Genetics in Medicinejournal articleCited in: [Variant Effect Prediction (passage 1)](molecular/variant-effects.html#variant-effects-introduction); [Variant Effect Prediction (passage 2)](molecular/variant-effects.html#sec-variant-effects-acmg)
- [Predicting the clinical impact of human mutation with deep neural networks](https://doi.org/10.1038/s41588-018-0167-z)Laksshman Sundaram; Hong Gao; Samskruthi Reddy Padigepati; et al. 2018. Nature Geneticsjournal articleCited in: [Variant Effect Prediction](molecular/variant-effects.html#sec-variant-effects-missense)
- [High-resolution reconstruction of cell-type-specific transcriptional regulatory processes from bulk sequencing samples](https://doi.org/10.1038/s41587-026-03218-w)Li Yao; Sagar R. Shah; Abdullah Ozer; et al. 2026. Nature Biotechnologyjournal articleCited in: [Variant Effect Prediction](molecular/variant-effects.html#sec-variant-effects-noncoding)
- [A disease-specific language model for variant pathogenicity in cardiac and regulatory genomics](https://doi.org/10.1038/s42256-025-01016-8)Huixin Zhan; Jason H. Moore; Zijun Zhang. 2025. Nature Machine Intelligencejournal articleCited in: [Variant Effect Prediction](molecular/variant-effects.html#sec-variant-effects-noncoding)
- [Predicting effects of noncoding variants with deep learning–based sequence model](https://doi.org/10.1038/nmeth.3547)Jian Zhou; Olga G Troyanskaya. 2015. Nature Methodsjournal articleCited in: [Variant Effect Prediction](molecular/variant-effects.html#sec-variant-effects-noncoding)

## Part III: Cells, Tissues, and Systems Biology

### Single-Cell Foundation Models

#### References

- [Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines](https://doi.org/10.1038/s41592-025-02772-6)Constantin Ahlmann-Eltze; Wolfgang Huber; Simon Anders. 2025. Nature Methodsjournal articleCited in: [Single-Cell Foundation Models (passage 1)](cells/single-cell-models.html); [Single-Cell Foundation Models (passage 2)](cells/single-cell-models.html#scfm-introduction)
- [Deeper evaluation of a single-cell foundation model](https://doi.org/10.1038/s42256-024-00949-w)Rebecca Boiarsky; Nalini M. Singh; Alejandro Buendia; et al. 2024. Nature Machine Intelligencejournal articleCited in: [Single-Cell Foundation Models (passage 1)](cells/single-cell-models.html); [Single-Cell Foundation Models (passage 2)](cells/single-cell-models.html#scfm-introduction)
- [scE2TM improves single-cell embedding interpretability and reveals cellular perturbation signatures](https://doi.org/10.1038/s41467-026-76825-5)Hegang Chen; Yuyin Lu; Yifan Zhao; et al. 2026. Nature Communicationsjournal articleCited in: [Single-Cell Foundation Models](cells/single-cell-models.html#sec-scfm-search)
- [scGPT: toward building a foundation model for single-cell multi-omics using generative AI](https://doi.org/10.1038/s41592-024-02201-0)Haotian Cui; Chloe Wang; Hassaan Maan; et al. 2024. Nature Methodsjournal articleCited in: [Single-Cell Foundation Models (passage 1)](cells/single-cell-models.html#scfm-introduction); [Single-Cell Foundation Models (passage 2)](cells/single-cell-models.html#sec-scfm-representation)
- [CZI Cell Science Program et al., 2025](https://doi.org/10.1093/nar/gkae1142)Cited in: [Single-Cell Foundation Models](cells/single-cell-models.html#sec-scfm-data)
- [Bonsai reconstructs tree representations for distortion-free visualization and exploration of high-dimensional data](https://doi.org/10.1038/s41587-026-03220-2)Daan H. de Groot; Sarah X. Morillo Leonardo; Mikhail Pachkov; et al. 2026. Nature Biotechnologyjournal articleCited in: [Single-Cell Foundation Models](cells/single-cell-models.html#sec-scfm-search)
- [Evaluating the role of pretraining dataset size and diversity on single-cell foundation model performance](https://doi.org/10.1038/s41592-026-03120-y)Alan DenAdel; Madeline Hughes; Akshaya Thoutam; et al. 2026. Nature Methodsjournal articleCited in: [Single-Cell Foundation Models](cells/single-cell-models.html#sec-scfm-scaling)
- [Scoring gene importance by interpreting single-cell foundation models](https://doi.org/10.1038/s41587-026-03112-5)Maxwell P. Gold; Miguel Reyes; Nathaniel Diamant; et al. 2026. Nature Biotechnologyjournal articleCited in: [Single-Cell Foundation Models](cells/single-cell-models.html#sec-scfm-search)
- [Large-scale foundation model on single-cell transcriptomics](https://doi.org/10.1038/s41592-024-02305-7)Minsheng Hao; Jing Gong; Xin Zeng; et al. 2024. Nature Methodsjournal articleCited in: [Single-Cell Foundation Models (passage 1)](cells/single-cell-models.html#scfm-introduction); [Single-Cell Foundation Models (passage 2)](cells/single-cell-models.html#sec-scfm-representation)
- [AI-based characterization of Alzheimer's disease phenotypes from population-scale single-cell data](https://doi.org/10.1038/s41591-025-04128-1)Chenfeng He; Athan Z. Li; Kalpana Hanthanan Arachchilage; et al. 2026. Nature Medicinejournal articleCited in: [Single-Cell Foundation Models](cells/single-cell-models.html#sec-scfm-pascode)
- [A cell atlas foundation model for scalable search of similar human cells](https://doi.org/10.1038/s41586-024-08411-y)Graham Heimberg; Tony Kuo; Daryle J. DePianto; et al. 2024. Naturejournal articleCited in: [Single-Cell Foundation Models (passage 1)](cells/single-cell-models.html#sec-scfm-representation); [Single-Cell Foundation Models (passage 2)](cells/single-cell-models.html#sec-scfm-search)
- [CytoVI: deep generative modeling of antibody-based single cell data](https://doi.org/10.1038/s41592-026-03224-5)Florian Ingelfinger; Nathan Levy; Can Ergen; et al. 2026. Nature Methodsjournal articleCited in: [Single-Cell Foundation Models](cells/single-cell-models.html#sec-scfm-cytovi)
- [scPRAM accurately predicts single-cell gene expression perturbation response based on attention mechanism](https://doi.org/10.1093/bioinformatics/btae265)Qun Jiang; Shengquan Chen; Xiaoyang Chen; et al. 2024. Bioinformaticsjournal articleCited in: [Single-Cell Foundation Models](cells/single-cell-models.html#scfm-theoretical)
- [Fast, sensitive and accurate integration of single-cell data with Harmony](https://doi.org/10.1038/s41592-019-0619-0)Ilya Korsunsky; Nghia Millard; Jean Fan; et al. 2019. Nature Methodsjournal articleCited in: [Single-Cell Foundation Models](cells/single-cell-models.html#sec-scfm-integration)
- [Deep generative modeling for single-cell transcriptomics](https://doi.org/10.1038/s41592-018-0229-2)Romain Lopez; Jeffrey Regier; Michael B. Cole; et al. 2018. Nature Methodsjournal articleCited in: [Single-Cell Foundation Models](cells/single-cell-models.html#sec-scfm-integration)
- [scGen predicts single-cell perturbation responses](https://doi.org/10.1038/s41592-019-0494-8)Mohammad Lotfollahi; F. Alexander Wolf; Fabian J. Theis. 2019. Nature Methodsjournal articleCited in: [Single-Cell Foundation Models](cells/single-cell-models.html#scfm-theoretical)
- [Predicting cellular responses to complex perturbations in high‐throughput screens](https://doi.org/10.15252/msb.202211517)Mohammad Lotfollahi; Anna Klimovskaia Susmelj; Carlo De Donno; et al. 2023. Molecular Systems Biologyjournal articleCited in: [Single-Cell Foundation Models](cells/single-cell-models.html#scfm-theoretical)
- [Benchmarking atlas-level data integration in single-cell genomics](https://doi.org/10.1038/s41592-021-01336-8)Malte D. Luecken; M. Büttner; K. Chaichoompu; et al. 2021. Nature Methodsjournal articleCited in: [Single-Cell Foundation Models (passage 1)](cells/single-cell-models.html#sec-scfm-integration); [Single-Cell Foundation Models (passage 2)](cells/single-cell-models.html#scfm-practice-notes)
- [Defining and benchmarking open problems in single-cell analysis](https://doi.org/10.1038/s41587-025-02694-w)Malte D. Luecken; Scott Gigante; Daniel B. Burkhardt; et al. 2025. Nature Biotechnologyjournal articleCited in: [Single-Cell Foundation Models](cells/single-cell-models.html#scfm-practice-notes)
- [Deep learning perturbation models can outperform baselines on calibrated metrics](https://doi.org/10.1038/s41587-026-03307-w)Miller et al. 2026. Nature Biotechnologyjournal articleCited in: [Single-Cell Foundation Models (passage 1)](cells/single-cell-models.html); [Single-Cell Foundation Models (passage 2)](cells/single-cell-models.html#scfm-introduction); [Single-Cell Foundation Models (passage 3)](cells/single-cell-models.html#scfm-beyond-current-capabilities)
- [The Human Cell Atlas](https://doi.org/10.7554/eLife.27041)Aviv Regev; Sarah A Teichmann; Eric S Lander; et al. 2017. eLifejournal articleCited in: [Single-Cell Foundation Models](cells/single-cell-models.html#sec-scfm-data)
- [The Human Cell Atlas from a cell census to a unified foundation model](https://doi.org/10.1038/s41586-024-08338-4)Jennifer E. Rood; Samantha Wynne; Lucia Robson; et al. 2024. Naturejournal articleCited in: [Single-Cell Foundation Models (passage 1)](cells/single-cell-models.html#sec-scfm-data); [Single-Cell Foundation Models (passage 2)](cells/single-cell-models.html#scfm-theoretical)
- [Predicting transcriptional outcomes of novel multigene perturbations with GEARS](https://doi.org/10.1038/s41587-023-01905-6)Yusuf Roohani; Kexin Huang; Jure Leskovec. 2023. Nature Biotechnologyjournal articleCited in: [Single-Cell Foundation Models](cells/single-cell-models.html#scfm-theoretical)
- [Universal cell embedding provides a foundation model for cell biology](https://doi.org/10.1038/s41586-026-10689-z)Yanay Rosen; Yusuf Roohani; Ayush Agrawal; et al. 2026. Naturejournal articleCited in: [Single-Cell Foundation Models (passage 1)](cells/single-cell-models.html); [Single-Cell Foundation Models (passage 2)](cells/single-cell-models.html#sec-scfm-representation); [Single-Cell Foundation Models (passage 3)](cells/single-cell-models.html#scfm-theoretical); [Single-Cell Foundation Models (passage 4)](cells/single-cell-models.html#scfm-practice-notes)
- [Comprehensive Integration of Single-Cell Data](https://doi.org/10.1016/j.cell.2019.05.031)Tim Stuart; Andrew Butler; Paul Hoffman; et al. 2019. Celljournal articleCited in: [Single-Cell Foundation Models](cells/single-cell-models.html#sec-scfm-integration)
- [Tabula Sapiens Consortium, 2022](https://doi.org/10.1126/science.abl4896)Cited in: [Single-Cell Foundation Models](cells/single-cell-models.html#sec-scfm-data)
- [Nicheformer: a foundation model for single-cell and spatial omics](https://doi.org/10.1038/s41592-025-02814-z)Alejandro Tejada-Lapuerta; Anna C. Schaar; Robert Gutgesell; et al. 2025. Nature Methodsjournal articleCited in: [Single-Cell Foundation Models (passage 1)](cells/single-cell-models.html); [Single-Cell Foundation Models (passage 2)](cells/single-cell-models.html#scfm-introduction); [Single-Cell Foundation Models (passage 3)](cells/single-cell-models.html#sec-scfm-representation)
- [Transfer learning enables predictions in network biology](https://doi.org/10.1038/s41586-023-06139-9)Christina V. Theodoris; Ling Xiao; Anant Chopra; et al. 2023. Naturejournal articleCited in: [Single-Cell Foundation Models (passage 1)](cells/single-cell-models.html#scfm-introduction); [Single-Cell Foundation Models (passage 2)](cells/single-cell-models.html#sec-scfm-representation)
- [Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models](https://doi.org/10.15252/msb.20209620)Chenling Xu; Romain Lopez; Edouard Mehlman; et al. 2021. Molecular Systems Biologyjournal articleCited in: [Single-Cell Foundation Models](cells/single-cell-models.html#sec-scfm-integration)
- [scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data](https://doi.org/10.1038/s42256-022-00534-z)Fan Yang; Wenchuan Wang; Fang Wang; et al. 2022. Nature Machine Intelligencejournal articleCited in: [Single-Cell Foundation Models](cells/single-cell-models.html#sec-scfm-representation)

### Spatial Omics and Tissue Models

#### References

- [Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram](https://doi.org/10.1038/s41592-021-01264-7)Tommaso Biancalani; Gabriele Scalia; Lorenzo Buffoni; et al. 2021. Nature Methodsjournal articleCited in: [Spatial Omics and Tissue Models](cells/spatial-omics.html#spatial-omics-deconvolution)
- [Novae: a graph-based foundation model for spatial transcriptomics data](https://doi.org/10.1038/s41592-025-02899-6)Quentin Blampey; Hakim Benkirane; Nadège Bercovici; et al. 2025. Nature Methodsjournal articleCited in: [Spatial Omics and Tissue Models (passage 1)](cells/spatial-omics.html); [Spatial Omics and Tissue Models (passage 2)](cells/spatial-omics.html#spatial-omics-tissue-models-demonstrated)
- [Spatially resolved, highly multiplexed RNA profiling in single cells](https://doi.org/10.1126/science.aaa6090)Kok Hao Chen; Alistair N. Boettiger; Jeffrey R. Moffitt; et al. 2015. Sciencejournal articleCited in: [Spatial Omics and Tissue Models](cells/spatial-omics.html#spatial-omics-tissue-models-introduction)
- [Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays](https://doi.org/10.1016/j.cell.2022.04.003)Ao Chen; Sha Liao; Mengnan Cheng; et al. 2022. Celljournal articleCited in: [Spatial Omics and Tissue Models](cells/spatial-omics.html#spatial-omics-tissue-models-introduction)
- [Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics](https://doi.org/10.1038/s41580-024-00768-2)Gunsagar S. Gulati; Jeremy Philip D’Silva; Yunhe Liu; et al. 2024. Nature Reviews Molecular Cell Biologyjournal articleCited in: [Spatial Omics and Tissue Models (passage 1)](cells/spatial-omics.html#spatial-omics-tissue-models-introduction); [Spatial Omics and Tissue Models (passage 2)](cells/spatial-omics.html#spatial-omics-tissue-models-demonstrated)
- [High-plex imaging of RNA and proteins at subcellular resolution in fixed tissue by spatial molecular imaging](https://doi.org/10.1038/s41587-022-01483-z)Shanshan He; Ruchir Bhatt; Carl Brown; et al. 2022. Nature Biotechnologyjournal articleCited in: [Spatial Omics and Tissue Models](cells/spatial-omics.html#spatial-omics-tissue-models-introduction)
- [Cell2location maps fine-grained cell types in spatial transcriptomics](https://doi.org/10.1038/s41587-021-01139-4)Vitalii Kleshchevnikov; Artem Shmatko; Emma Dann; et al. 2022. Nature Biotechnologyjournal articleCited in: [Spatial Omics and Tissue Models](cells/spatial-omics.html#spatial-omics-deconvolution)
- [Li et al., 2026](https://doi.org/10.1038/s41556-026-02067-8)Cited in: [Spatial Omics and Tissue Models](cells/spatial-omics.html#spatial-omics-tissue-models-demonstrated)
- [SpatialData: an open and universal data framework for spatial omics](https://doi.org/10.1038/s41592-024-02212-x)Luca Marconato; Giovanni Palla; Kevin A. Yamauchi; et al. 2024. Nature Methodsjournal articleCited in: [Spatial Omics and Tissue Models (passage 1)](cells/spatial-omics.html); [Spatial Omics and Tissue Models (passage 2)](cells/spatial-omics.html#spatial-omics-tissue-models-demonstrated)
- [SpaCEy links spatial tissue patterns to clinical outcomes using explainable graph neural networks](https://doi.org/10.1038/s41467-026-77924-z)Ahmet Sureyya Rifaioglu; Egle Helene Ervin; Ahmet Sarigun; et al. 2026. Nature Communicationsjournal articleCited in: [Spatial Omics and Tissue Models](cells/spatial-omics.html#spatial-omics-tissue-models-demonstrated)
- [Visualization and analysis of gene expression in tissue sections by spatial transcriptomics](https://doi.org/10.1126/science.aaf2403)Patrik L. Ståhl; Fredrik Salmén; Sanja Vickovic; et al. 2016. Sciencejournal articleCited in: [Spatial Omics and Tissue Models](cells/spatial-omics.html#spatial-omics-tissue-models-introduction)
- [Nicheformer: a foundation model for single-cell and spatial omics](https://doi.org/10.1038/s41592-025-02814-z)Alejandro Tejada-Lapuerta; Anna C. Schaar; Robert Gutgesell; et al. 2025. Nature Methodsjournal articleCited in: [Spatial Omics and Tissue Models (passage 1)](cells/spatial-omics.html); [Spatial Omics and Tissue Models (passage 2)](cells/spatial-omics.html#spatial-omics-tissue-models-demonstrated)
- [The Virtual Tissues foundation model resolves spatial proteomics across scales](https://doi.org/10.1038/s41586-026-10884-y)Johann Wenckstern; Eeshaan Jain; Benedikt von Querfurth; et al. 2026. Naturejournal articleCited in: [Spatial Omics and Tissue Models](cells/spatial-omics.html#spatial-omics-tissue-models-demonstrated)

### Cell Painting and Image-Based Phenotyping

#### References

- [Evaluating batch correction methods for image-based cell profiling](https://doi.org/10.1038/s41467-024-50613-5)John Arevalo; Ellen Su; Jessica D. Ewald; et al. 2024. Nature Communicationsjournal articleCited in: [Cell Painting and Image-Based Phenotyping (passage 1)](cells/cell-painting.html#cell-painting-image-phenotyping-batch); [Cell Painting and Image-Based Phenotyping (passage 2)](cells/cell-painting.html#cell-painting-image-phenotyping-demonstrated)
- [Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes](https://doi.org/10.1038/nprot.2016.105)Mark-Anthony Bray; Shantanu Singh; Han Han; et al. 2016. Nature Protocolsjournal articleCited in: [Cell Painting and Image-Based Phenotyping (passage 1)](cells/cell-painting.html#cell-painting-image-phenotyping-introduction); [Cell Painting and Image-Based Phenotyping (passage 2)](cells/cell-painting.html#cell-painting-image-phenotyping-demonstrated)
- [Data-analysis strategies for image-based cell profiling](https://doi.org/10.1038/nmeth.4397)Juan C Caicedo; Sam Cooper; Florian Heigwer; et al. 2017. Nature Methodsjournal articleCited in: [Cell Painting and Image-Based Phenotyping](cells/cell-painting.html#cell-painting-image-phenotyping-demonstrated)
- [CellProfiler: image analysis software for identifying and quantifying cell phenotypes](https://doi.org/10.1186/gb-2006-7-10-r100)Anne E Carpenter; Thouis R Jones; Michael R Lamprecht; et al. 2006. Genome Biologyjournal articleCited in: [Cell Painting and Image-Based Phenotyping (passage 1)](cells/cell-painting.html#cell-painting-image-phenotyping-introduction); [Cell Painting and Image-Based Phenotyping (passage 2)](cells/cell-painting.html#cell-painting-image-phenotyping-demonstrated)
- [Three million images and morphological profiles of cells treated with matched chemical and genetic perturbations](https://doi.org/10.1038/s41592-024-02241-6)Srinivas Niranj Chandrasekaran; Beth A. Cimini; Amy Goodale; et al. 2024. Nature Methodsjournal articleCited in: [Cell Painting and Image-Based Phenotyping](cells/cell-painting.html#cell-painting-image-phenotyping-demonstrated)
- [Morphological map of under- and overexpression of genes in human cells](https://doi.org/10.1038/s41592-025-02753-9)Srinivas Niranj Chandrasekaran; Eric Alix; John Arevalo; et al. 2025. Nature Methodsjournal articleCited in: [Cell Painting and Image-Based Phenotyping](cells/cell-painting.html#cell-painting-image-phenotyping-demonstrated)
- [Optimizing the Cell Painting assay for image-based profiling](https://doi.org/10.1038/s41596-023-00840-9)Beth A. Cimini; Srinivas Niranj Chandrasekaran; Maria Kost-Alimova; et al. 2023. Nature Protocolsjournal articleCited in: [Cell Painting and Image-Based Phenotyping (passage 1)](cells/cell-painting.html#cell-painting-image-phenotyping-introduction); [Cell Painting and Image-Based Phenotyping (passage 2)](cells/cell-painting.html#cell-painting-image-phenotyping-demonstrated)
- [CellProfiler 3.0: Next-generation image processing for biology](https://doi.org/10.1371/journal.pbio.2005970)Claire McQuin; Allen Goodman; Vasiliy Chernyshev; et al. 2018. PLOS Biologyjournal articleCited in: [Cell Painting and Image-Based Phenotyping (passage 1)](cells/cell-painting.html#cell-painting-image-phenotyping-introduction); [Cell Painting and Image-Based Phenotyping (passage 2)](cells/cell-painting.html#cell-painting-image-phenotyping-demonstrated)
- [Learning representations for image-based profiling of perturbations](https://doi.org/10.1038/s41467-024-45999-1)Nikita Moshkov; Michael Bornholdt; Santiago Benoit; et al. 2024. Nature Communicationsjournal articleCited in: [Cell Painting and Image-Based Phenotyping](cells/cell-painting.html#cell-painting-image-phenotyping-demonstrated)
- [A genome-wide atlas of human cell morphology](https://doi.org/10.1038/s41592-024-02537-7)Meraj Ramezani; Erin Weisbart; Julia Bauman; et al. 2025. Nature Methodsjournal articleCited in: [Cell Painting and Image-Based Phenotyping](cells/cell-painting.html#cell-painting-image-phenotyping-demonstrated)
- [Toward performance-diverse small-molecule libraries for cell-based phenotypic screening using multiplexed high-dimensional profiling](https://doi.org/10.1073/pnas.1410933111)Mathias J. Wawer; Kejie Li; Sigrun M. Gustafsdottir; et al. 2014. Proceedings of the National Academy of Sciencesjournal articleCited in: [Cell Painting and Image-Based Phenotyping](cells/cell-painting.html#cell-painting-image-phenotyping-demonstrated)
- [Cell Painting Gallery: an open resource for image-based profiling](https://doi.org/10.1038/s41592-024-02399-z)Erin Weisbart; Ankur Kumar; John Arevalo; et al. 2024. Nature Methodsjournal articleCited in: [Cell Painting and Image-Based Phenotyping](cells/cell-painting.html#cell-painting-image-phenotyping-demonstrated)
- [Cell Painting in activated cells illuminates phenotypic dark space and drug mechanisms of action](https://doi.org/10.1038/s41467-026-76252-6)Matylda A. Zietek; Akshar Lohith; Derfel Terciano; et al. 2026. Nature Communicationsjournal articleCited in: [Cell Painting and Image-Based Phenotyping](cells/cell-painting.html#cell-painting-image-phenotyping-demonstrated)

### Histopathology AI

#### References

- [Aiforia, 2026](https://www.aiforia.com/)Cited in: [Histopathology AI](cells/histopathology.html#histopathology-industry)
- [Clinical-grade computational pathology using weakly supervised deep learning on whole slide images](https://doi.org/10.1038/s41591-019-0508-1)Gabriele Campanella; Matthew G. Hanna; Luke Geneslaw; et al. 2019. Nature Medicinejournal articleCited in: [Histopathology AI](cells/histopathology.html#histopathology-foundation-models)
- [Real-world deployment of a fine-tuned pathology foundation model for lung cancer biomarker detection](https://doi.org/10.1038/s41591-025-03780-x)Gabriele Campanella; Neeraj Kumar; Swaraj Nanda; et al. 2025. Nature Medicinejournal articleCited in: [Histopathology AI](cells/histopathology.html#histopathology-foundation-models)
- [Towards a general-purpose foundation model for computational pathology](https://doi.org/10.1038/s41591-024-02857-3)Richard J. Chen; Tong Ding; Ming Y. Lu; et al. 2024. Nature Medicinejournal articleCited in: [Histopathology AI](cells/histopathology.html#histopathology-foundation-models)
- [Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning](https://doi.org/10.1038/s41591-018-0177-5)Nicolas Coudray; Paolo Santiago Ocampo; Theodore Sakellaropoulos; et al. 2018. Nature Medicinejournal articleCited in: [Histopathology AI](cells/histopathology.html#histopathology-biomarkers)
- [A multimodal whole-slide foundation model for pathology](https://doi.org/10.1038/s41591-025-03982-3)Tong Ding; Sophia J. Wagner; Andrew H. Song; et al. 2025. Nature Medicinejournal articleCited in: [Histopathology AI](cells/histopathology.html#histopathology-foundation-models)
- [ALPaCA: Adapting Llama for Pathology Context Analysis to enable slide-level question answering](https://doi.org/10.1038/s41467-026-76372-z)Zeyu Gao; Kai He; Weiheng Su; et al. 2026. Nature Communicationsjournal articleCited in: [Histopathology AI](cells/histopathology.html#histopathology-foundation-models)
- [Deep-learning triage of three-dimensional pathology datasets for comprehensive and efficient pathologist assessments](https://doi.org/10.1038/s41551-026-01760-1)Gan Gao; Renao Yan; Andrew H. Song; et al. 2026. Nature Biomedical Engineeringjournal articleCited in: [Histopathology AI](cells/histopathology.html#histopathology-foundation-models)
- [Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images](https://doi.org/10.1016/j.media.2019.101563)Simon Graham; Quoc Dang Vu; Shan E Ahmed Raza; et al. 2019. Medical Image Analysisjournal articleCited in: [Histopathology AI](cells/histopathology.html#histopathology-segmentation)
- [The impact of site-specific digital histology signatures on deep learning model accuracy and bias](https://doi.org/10.1038/s41467-021-24698-1)Frederick M. Howard; James Dolezal; Sara Kochanny; et al. 2021. Nature Communicationsjournal articleCited in: [Histopathology AI](cells/histopathology.html#histopathology-foundation-models)
- [Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer](https://doi.org/10.1038/s41591-019-0462-y)Jakob Nikolas Kather; Alexander T. Pearson; Niels Halama; et al. 2019. Nature Medicinejournal articleCited in: [Histopathology AI](cells/histopathology.html#histopathology-biomarkers)
- [Pan-cancer image-based detection of clinically actionable genetic alterations](https://doi.org/10.1038/s43018-020-0087-6)Jakob Nikolas Kather; Lara R. Heij; Heike I. Grabsch; et al. 2020. Nature Cancerjournal articleCited in: [Histopathology AI](cells/histopathology.html#histopathology-biomarkers)
- [A visual-language foundation model for computational pathology](https://doi.org/10.1038/s41591-024-02856-4)Ming Y. Lu; Bowen Chen; Drew F. K. Williamson; et al. 2024. Nature Medicinejournal articleCited in: [Histopathology AI](cells/histopathology.html#histopathology-foundation-models)
- [nnMIL: a generalizable multiple instance learning framework for computational pathology](https://doi.org/10.1038/s41551-026-01767-8)Xiangde Luo; Jinxi Xiang; Yuanfeng Ji; et al. 2026. Nature Biomedical Engineeringjournal articleCited in: [Histopathology AI](cells/histopathology.html#histopathology-foundation-models)
- [An explainable biomedical foundation model via large-scale concept-enhanced vision–language pretraining](https://doi.org/10.1038/s41551-026-01764-x)Yuxiang Nie; Sunan He; Yequan Bie; et al. 2026. Nature Biomedical Engineeringjournal articleCited in: [Histopathology AI](cells/histopathology.html#histopathology-foundation-models)
- [Owkin, 2026](https://www.owkin.com/)Cited in: [Histopathology AI](cells/histopathology.html#histopathology-industry)
- [Paige, 2026](https://www.paige.ai/)Cited in: [Histopathology AI](cells/histopathology.html#histopathology-industry)
- [PathAI, 2026](https://www.pathai.com/)Cited in: [Histopathology AI](cells/histopathology.html#histopathology-industry)
- [SpaCEy links spatial tissue patterns to clinical outcomes using explainable graph neural networks](https://doi.org/10.1038/s41467-026-77924-z)Ahmet Sureyya Rifaioglu; Egle Helene Ervin; Ahmet Sarigun; et al. 2026. Nature Communicationsjournal articleCited in: [Histopathology AI](cells/histopathology.html#histopathology-tme)
- [Tempus, 2026](https://www.tempus.com/)Cited in: [Histopathology AI](cells/histopathology.html#histopathology-industry)
- [A foundation model for clinical-grade computational pathology and rare cancers detection](https://doi.org/10.1038/s41591-024-03141-0)Eugene Vorontsov; Alican Bozkurt; Adam Casson; et al. 2024. Nature Medicinejournal articleCited in: [Histopathology AI](cells/histopathology.html#histopathology-foundation-models)
- [A pathology foundation model for cancer diagnosis and prognosis prediction](https://doi.org/10.1038/s41586-024-07894-z)Xiyue Wang; Junhan Zhao; Eliana Marostica; et al. 2024. Naturejournal articleCited in: [Histopathology AI](cells/histopathology.html#histopathology-foundation-models)
- [Foundation Model for Predicting Prognosis and Adjuvant Therapy Benefit From Digital Pathology in GI Cancers](https://doi.org/10.1200/JCO-24-01501)Xiyue Wang; Yuming Jiang; Sen Yang; et al. 2025. Journal of Clinical Oncologyjournal articleCited in: [Histopathology AI](cells/histopathology.html#histopathology-foundation-models)
- [A vision–language foundation model for precision oncology](https://doi.org/10.1038/s41586-024-08378-w)Jinxi Xiang; Xiyue Wang; Xiaoming Zhang; et al. 2025. Naturejournal articleCited in: [Histopathology AI](cells/histopathology.html#histopathology-foundation-models)
- [A whole-slide foundation model for digital pathology from real-world data](https://doi.org/10.1038/s41586-024-07441-w)Hanwen Xu; Naoto Usuyama; Jaspreet Bagga; et al. 2024. Naturejournal articleCited in: [Histopathology AI](cells/histopathology.html#histopathology-foundation-models)

### Microscopy and Cryo-EM AI

#### References

- [Segment Anything for Microscopy](https://doi.org/10.1038/s41592-024-02580-4)Anwai Archit; Luca Freckmann; Sushmita Nair; et al. 2025. Nature Methodsjournal articleCited in: [Microscopy and Cryo-EM AI](cells/microscopy-cryo-em.html#microscopy-cryo-em-segmentation)
- [Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs](https://doi.org/10.1038/s41592-019-0575-8)Tristan Bepler; Andrew Morin; Micah Rapp; et al. 2019. Nature Methodsjournal articleCited in: [Microscopy and Cryo-EM AI](cells/microscopy-cryo-em.html#microscopy-cryo-em-particles)
- [In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images](https://doi.org/10.1016/j.cell.2018.03.040)Eric M. Christiansen; Samuel J. Yang; D. Michael Ando; et al. 2018. Celljournal articleCited in: [Microscopy and Cryo-EM AI](cells/microscopy-cryo-em.html#microscopy-cryo-em-super-resolution)
- [Automated model building and protein identification in cryo-EM maps](https://doi.org/10.1038/s41586-024-07215-4)Kiarash Jamali; Lukas Käll; Rui Zhang; et al. 2024. Naturejournal articleCited in: [Microscopy and Cryo-EM AI](cells/microscopy-cryo-em.html#microscopy-cryo-em-model-building)
- [Noise2Void - Learning Denoising From Single Noisy Images](https://doi.org/10.1109/CVPR.2019.00223)Alexander Krull; Tim-Oliver Buchholz; Florian Jug. 2019. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)proceedings articleCited in: [Microscopy and Cryo-EM AI](cells/microscopy-cryo-em.html#microscopy-cryo-em-restoration)
- [Avoiding a replication crisis in deep-learning-based bioimage analysis](https://doi.org/10.1038/s41592-021-01284-3)Romain F. Laine; Ignacio Arganda-Carreras; Ricardo Henriques; et al. 2021. Nature Methodsjournal articleCited in: [Microscopy and Cryo-EM AI](cells/microscopy-cryo-em.html#microscopy-cryo-em-restoration)
- [Disruption of the research software landscape through AI software generation](https://doi.org/10.1038/s41592-026-03210-x)Nelson D. Medina; Joergen M. R. Kornfeld. 2026. Nature Methodsjournal articleCited in: [Microscopy and Cryo-EM AI](cells/microscopy-cryo-em.html#microscopy-cryo-em-segmentation)
- [Deep-STORM: super-resolution single-molecule microscopy by deep learning](https://doi.org/10.1364/OPTICA.5.000458)Elias Nehme; Lucien E. Weiss; Tomer Michaeli; et al. 2018. Opticajournal articleCited in: [Microscopy and Cryo-EM AI](cells/microscopy-cryo-em.html#microscopy-cryo-em-super-resolution)
- [Deep learning massively accelerates super-resolution localization microscopy](https://doi.org/10.1038/nbt.4106)Wei Ouyang; Andrey Aristov; Mickaël Lelek; et al. 2018. Nature Biotechnologyjournal articleCited in: [Microscopy and Cryo-EM AI](cells/microscopy-cryo-em.html#microscopy-cryo-em-super-resolution)
- [A realistic phantom dataset for benchmarking cryo-ET data annotation](https://doi.org/10.1038/s41592-025-02800-5)Ariana Peck; Yue Yu; Jonathan Schwartz; et al. 2025. Nature Methodsjournal articleCited in: [Microscopy and Cryo-EM AI](cells/microscopy-cryo-em.html#microscopy-cryo-em-particles)
- [Learning structural heterogeneity from cryo-electron sub-tomograms with tomoDRGN](https://doi.org/10.1038/s41592-024-02210-z)Barrett M. Powell; Joseph H. Davis. 2024. Nature Methodsjournal articleCited in: [Microscopy and Cryo-EM AI](cells/microscopy-cryo-em.html#microscopy-cryo-em-particles)
- [cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination](https://doi.org/10.1038/nmeth.4169)Ali Punjani; John L Rubinstein; David J Fleet; et al. 2017. Nature Methodsjournal articleCited in: [Microscopy and Cryo-EM AI](cells/microscopy-cryo-em.html#microscopy-cryo-em-particles)
- [RELION: Implementation of a Bayesian approach to cryo-EM structure determination](https://doi.org/10.1016/j.jsb.2012.09.006)Sjors H.W. Scheres. 2012. Journal of Structural Biologyjournal articleCited in: [Microscopy and Cryo-EM AI](cells/microscopy-cryo-em.html#microscopy-cryo-em-model-building)
- [Cell Detection with Star-Convex Polygons](https://doi.org/10.1007/978-3-030-00934-2_30)Uwe Schmidt; Martin Weigert; Coleman Broaddus; et al. 2018. Lecture Notes in Computer Sciencebook chapterCited in: [Microscopy and Cryo-EM AI](cells/microscopy-cryo-em.html#microscopy-cryo-em-segmentation)
- [DynaMight: estimating molecular motions with improved reconstruction from cryo-EM images](https://doi.org/10.1038/s41592-024-02377-5)Johannes Schwab; Dari Kimanius; Alister Burt; et al. 2024. Nature Methodsjournal articleCited in: [Microscopy and Cryo-EM AI](cells/microscopy-cryo-em.html#microscopy-cryo-em-particles)
- [Cellpose: a generalist algorithm for cellular segmentation](https://doi.org/10.1038/s41592-020-01018-x)Carsen Stringer; Tim Wang; Michalis Michaelos; et al. 2020. Nature Methodsjournal articleCited in: [Microscopy and Cryo-EM AI](cells/microscopy-cryo-em.html#microscopy-cryo-em-segmentation)
- [Cellpose3: one-click image restoration for improved cellular segmentation](https://doi.org/10.1038/s41592-025-02595-5)Carsen Stringer; Marius Pachitariu. 2025. Nature Methodsjournal articleCited in: [Microscopy and Cryo-EM AI](cells/microscopy-cryo-em.html#microscopy-cryo-em-segmentation)
- [Democratising deep learning for microscopy with ZeroCostDL4Mic](https://doi.org/10.1038/s41467-021-22518-0)Lucas von Chamier; Romain F. Laine; Johanna Jukkala; et al. 2021. Nature Communicationsjournal articleCited in: [Microscopy and Cryo-EM AI](cells/microscopy-cryo-em.html#microscopy-cryo-em-restoration)
- [SPHIRE-crYOLO is a fast and accurate fully automated particle picker for cryo-EM](https://doi.org/10.1038/s42003-019-0437-z)Thorsten Wagner; Felipe Merino; Markus Stabrin; et al. 2019. Communications Biologyjournal articleCited in: [Microscopy and Cryo-EM AI](cells/microscopy-cryo-em.html#microscopy-cryo-em-particles)
- [Content-aware image restoration: pushing the limits of fluorescence microscopy](https://doi.org/10.1038/s41592-018-0216-7)Martin Weigert; Uwe Schmidt; Tobias Boothe; et al. 2018. Nature Methodsjournal articleCited in: [Microscopy and Cryo-EM AI](cells/microscopy-cryo-em.html#microscopy-cryo-em-restoration)
- [CryoDRGN: reconstruction of heterogeneous cryo-EM structures using neural networks](https://doi.org/10.1038/s41592-020-01049-4)Ellen D. Zhong; Tristan Bepler; Bonnie Berger; et al. 2021. Nature Methodsjournal articleCited in: [Microscopy and Cryo-EM AI](cells/microscopy-cryo-em.html#microscopy-cryo-em-particles)

### Perturbation Prediction and Virtual Cells

#### References

- [A Multiplexed Single-Cell CRISPR Screening Platform Enables Systematic Dissection of the Unfolded Protein Response](https://doi.org/10.1016/j.cell.2016.11.048)Britt Adamson; Thomas M. Norman; Marco Jost; et al. 2016. Celljournal articleCited in: [Perturbation Prediction and Virtual Cells](cells/virtual-cells.html#perturbation-virtual-cells-introduction)
- [Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines](https://doi.org/10.1038/s41592-025-02772-6)Constantin Ahlmann-Eltze; Wolfgang Huber; Simon Anders. 2025. Nature Methodsjournal articleCited in: [Perturbation Prediction and Virtual Cells (passage 1)](cells/virtual-cells.html); [Perturbation Prediction and Virtual Cells (passage 2)](cells/virtual-cells.html#perturbation-virtual-cells-introduction); [Perturbation Prediction and Virtual Cells (passage 3)](cells/virtual-cells.html#perturbation-virtual-cells-demonstrated)
- [Arc Institute, 2025](https://arcinstitute.org/news/virtual-cell-challenge-2025-wrap-up)Cited in: [Perturbation Prediction and Virtual Cells (passage 1)](cells/virtual-cells.html); [Perturbation Prediction and Virtual Cells (passage 2)](cells/virtual-cells.html#perturbation-virtual-cells-demonstrated)
- [International, cross-sector collaboration commits nearly \$2 billion to build foundational data for AI models to predict and treat disease](https://biohub.org/news/virtual-biology-initiative-expansion/)Biohub. 2026. Biohubinstitutional announcementCited in: [Perturbation Prediction and Virtual Cells](cells/virtual-cells.html#perturbation-virtual-cells-theoretical)
- [Learning single-cell perturbation responses using neural optimal transport](https://doi.org/10.1038/s41592-023-01969-x)Charlotte Bunne; Stefan G. Stark; Gabriele Gut; et al. 2023. Nature Methodsjournal articleCited in: [Perturbation Prediction and Virtual Cells](cells/virtual-cells.html#perturbation-virtual-cells-introduction)
- [How to build the virtual cell with artificial intelligence: Priorities and opportunities](https://doi.org/10.1016/j.cell.2024.11.015)Charlotte Bunne; Yusuf Roohani; Yanay Rosen; et al. 2024. Celljournal articleCited in: [Perturbation Prediction and Virtual Cells](cells/virtual-cells.html#perturbation-virtual-cells-theoretical)
- [X-Cell: Scaling Causal Perturbation Prediction Across Diverse Cellular Contexts via Diffusion Language Models](https://doi.org/10.64898/2026.03.18.712807)Chloe Wang; Mehran Karimzadeh; Neal G. Ravindra; Lexi R. Bounds; Nader Alerasool; Ann C. Huang; Shihao Ma; Daniel R. Gulbranson; Haotian Cui; Yongju Lee; Anusuya Arjavalingam; Elliot J. MacKrell; Matthew S. Wilken; Jieming Chen; Benjamin W. Herken; Jesse A. Weber; Massimo M. Onesto; Barbara Gonzalez-Teran; Nicole F. Leung; Sally Yu Shi; Byron J. Smith; Sharon K. Lam; Adam Barner; Philip Wright; Elizabeth M. Rumsey; Soohong Kim; Rene V. Sit; Adam J. Litterman; Ci Chu; Bo Wang. 2026. bioRxivpreprintCited in: [Perturbation Prediction and Virtual Cells](cells/virtual-cells.html#perturbation-virtual-cells-theoretical)
- [Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens](https://doi.org/10.1016/j.cell.2016.11.038)Atray Dixit; Oren Parnas; Biyu Li; et al. 2016. Celljournal articleCited in: [Perturbation Prediction and Virtual Cells](cells/virtual-cells.html#perturbation-virtual-cells-introduction)
- [Multimodal pooled Perturb-CITE-seq screens in patient models define mechanisms of cancer immune evasion](https://doi.org/10.1038/s41588-021-00779-1)Chris J. Frangieh; Johannes C. Melms; Pratiksha I. Thakore; et al. 2021. Nature Geneticsjournal articleCited in: [Perturbation Prediction and Virtual Cells](cells/virtual-cells.html#perturbation-virtual-cells-introduction)
- [scPRAM accurately predicts single-cell gene expression perturbation response based on attention mechanism](https://doi.org/10.1093/bioinformatics/btae265)Qun Jiang; Shengquan Chen; Xiaoyang Chen; et al. 2024. Bioinformaticsjournal articleCited in: [Perturbation Prediction and Virtual Cells](cells/virtual-cells.html#perturbation-virtual-cells-introduction)
- [US government, Google join Zuckerberg-backed Biohub in \$1.8 billion push for AI biology data](https://www.reuters.com/business/healthcare-pharmaceuticals/us-government-google-join-zuckerberg-backed-biohub-18-billion-push-ai-biology-2026-10-07/)Krystal Hu. 2026. Reutersnews articleCited in: [Perturbation Prediction and Virtual Cells](cells/virtual-cells.html#perturbation-virtual-cells-theoretical)
- [scGen predicts single-cell perturbation responses](https://doi.org/10.1038/s41592-019-0494-8)Mohammad Lotfollahi; F. Alexander Wolf; Fabian J. Theis. 2019. Nature Methodsjournal articleCited in: [Perturbation Prediction and Virtual Cells](cells/virtual-cells.html#perturbation-virtual-cells-introduction)
- [Predicting cellular responses to complex perturbations in high‐throughput screens](https://doi.org/10.15252/msb.202211517)Mohammad Lotfollahi; Anna Klimovskaia Susmelj; Carlo De Donno; et al. 2023. Molecular Systems Biologyjournal articleCited in: [Perturbation Prediction and Virtual Cells](cells/virtual-cells.html#perturbation-virtual-cells-introduction)
- [Deep learning perturbation models can outperform baselines on calibrated metrics](https://doi.org/10.1038/s41587-026-03307-w)Miller et al. 2026. Nature Biotechnologyjournal articleCited in: [Perturbation Prediction and Virtual Cells](cells/virtual-cells.html)
- [World models for biomedicine](https://doi.org/10.1016/j.cell.2026.08.032)Ayush Noori; Nic Fishman; Ada Fang; Lukas Fesser; Marinka Zitnik. 2026. Celljournal articleCited in: [Perturbation Prediction and Virtual Cells](cells/virtual-cells.html#perturbation-virtual-cells-theoretical)
- [Exploring genetic interaction manifolds constructed from rich single-cell phenotypes](https://doi.org/10.1126/science.aax4438)Thomas M. Norman; Max A. Horlbeck; Joseph M. Replogle; et al. 2019. Sciencejournal articleCited in: [Perturbation Prediction and Virtual Cells](cells/virtual-cells.html#perturbation-virtual-cells-introduction)
- [Virtual Cells Need Context, Not Just Scale](https://doi.org/10.64898/2026.02.04.703804)Payam Dibaeinia; Sudarshan Babu; Mei Knudson; Ali ElSheikh; Yibo Wen; Han Liu; Jason Perera; Aly A. Khan. 2026. bioRxivpreprintCited in: [Perturbation Prediction and Virtual Cells](cells/virtual-cells.html#perturbation-virtual-cells-theoretical)
- [Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq](https://doi.org/10.1016/j.cell.2022.05.013)Joseph M. Replogle; Reuben A. Saunders; Angela N. Pogson; et al. 2022. Celljournal articleCited in: [Perturbation Prediction and Virtual Cells](cells/virtual-cells.html#perturbation-virtual-cells-introduction)
- [Predicting transcriptional outcomes of novel multigene perturbations with GEARS](https://doi.org/10.1038/s41587-023-01905-6)Yusuf Roohani; Kexin Huang; Jure Leskovec. 2023. Nature Biotechnologyjournal articleCited in: [Perturbation Prediction and Virtual Cells (passage 1)](cells/virtual-cells.html); [Perturbation Prediction and Virtual Cells (passage 2)](cells/virtual-cells.html#perturbation-virtual-cells-introduction); [Perturbation Prediction and Virtual Cells (passage 3)](cells/virtual-cells.html#perturbation-virtual-cells-demonstrated)
- [Virtual Cell Challenge: Toward a Turing test for the virtual cell](https://doi.org/10.1016/j.cell.2025.06.008)Yusuf H. Roohani; Tony J. Hua; Po-Yuan Tung; et al. 2025. Celljournal articleCited in: [Perturbation Prediction and Virtual Cells (passage 1)](cells/virtual-cells.html); [Perturbation Prediction and Virtual Cells (passage 2)](cells/virtual-cells.html#perturbation-virtual-cells-demonstrated)
- [A world model of the virtual cell](https://doi.org/10.1016/j.cell.2026.08.042)Eric P. Xing; Le Song. 2026. Celljournal articleCited in: [Perturbation Prediction and Virtual Cells](cells/virtual-cells.html#perturbation-virtual-cells-theoretical)

### Microbiome and Multi-Omics AI

#### References

- [MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data](https://doi.org/10.1186/s13059-020-02015-1)Ricard Argelaguet; Damien Arnol; Danila Bredikhin; et al. 2020. Genome Biologyjournal articleCited in: [Microbiome and Multi-Omics AI](cells/multiomics.html#microbiome-multiomics-ai-demonstrated)
- [He et al., 2018](https://doi.org/10.1038/s41591-018-0164-x)Cited in: [Microbiome and Multi-Omics AI](cells/multiomics.html#microbiome-multiomics-ai-beyond-current-capabilities)
- [Human Microbiome Project Consortium, 2012](https://doi.org/10.1038/nature11234)Cited in: [Microbiome and Multi-Omics AI](cells/multiomics.html#microbiome-multiomics-ai-introduction)
- [Li et al., 2026](https://doi.org/10.1038/s41556-026-02067-8)Cited in: [Microbiome and Multi-Omics AI](cells/multiomics.html#microbiome-multiomics-ai-demonstrated)
- [Evolutionary-scale prediction of atomic-level protein structure with a language model](https://doi.org/10.1126/science.ade2574)Zeming Lin; Halil Akin; Roshan Rao; et al. 2023. Sciencejournal articleCited in: [Microbiome and Multi-Omics AI](cells/multiomics.html#microbiome-multiomics-ai-demonstrated)
- [Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases](https://doi.org/10.1038/s41586-019-1237-9)Jason Lloyd-Price; Cesar Arze; Ashwin N. Ananthakrishnan; et al. 2019. Naturejournal articleCited in: [Microbiome and Multi-Omics AI (passage 1)](cells/multiomics.html#microbiome-multiomics-ai-introduction); [Microbiome and Multi-Omics AI (passage 2)](cells/multiomics.html#microbiome-multiomics-longitudinal)
- [NIH Common Fund](https://commonfund.nih.gov/bridge2ai)Cited in: [Microbiome and Multi-Omics AI (passage 1)](cells/multiomics.html#microbiome-multiomics-ai-introduction); [Microbiome and Multi-Omics AI (passage 2)](cells/multiomics.html#microbiome-multiomics-ai-demonstrated)
- [NIH Common Fund, January 2026](https://commonfund.nih.gov/bridge2ai/news/nih-common-fund-bridge2ai-program-advances-next-stage)Cited in: [Microbiome and Multi-Omics AI](cells/multiomics.html#microbiome-multiomics-ai-demonstrated)
- [Qin et al., 2010](https://doi.org/10.1038/nature08821)Cited in: [Microbiome and Multi-Omics AI](cells/multiomics.html#microbiome-multiomics-ai-introduction)
- [mixOmics: An R package for ‘omics feature selection and multiple data integration](https://doi.org/10.1371/journal.pcbi.1005752)Florian Rohart; Benoît Gautier; Amrit Singh; et al. 2017. PLOS Computational Biologyjournal articleCited in: [Microbiome and Multi-Omics AI](cells/multiomics.html#microbiome-multiomics-ai-demonstrated)
- [Discovery of antimicrobial peptides in the global microbiome with machine learning](https://doi.org/10.1016/j.cell.2024.05.013)Célio Dias Santos-Júnior; Marcelo D.T. Torres; Yiqian Duan; et al. 2024. Celljournal articleCited in: [Microbiome and Multi-Omics AI](cells/multiomics.html#microbiome-multiomics-ai-demonstrated)
- [A generalizable Hi-C foundation model for chromatin architecture, single-cell and multiomics analysis across species](https://doi.org/10.1038/s41592-026-03097-8)Xiao Wang; Yuanyuan Zhang; Suhita Ray; et al. 2026. Nature Methodsjournal articleCited in: [Microbiome and Multi-Omics AI](cells/multiomics.html#microbiome-multiomics-ai-demonstrated)
- [High-resolution reconstruction of cell-type-specific transcriptional regulatory processes from bulk sequencing samples](https://doi.org/10.1038/s41587-026-03218-w)Li Yao; Sagar R. Shah; Abdullah Ozer; et al. 2026. Nature Biotechnologyjournal articleCited in: [Microbiome and Multi-Omics AI](cells/multiomics.html#microbiome-multiomics-ai-demonstrated)
- [Personalized Nutrition by Prediction of Glycemic Responses](https://doi.org/10.1016/j.cell.2015.11.001)David Zeevi; Tal Korem; Niv Zmora; et al. 2015. Celljournal articleCited in: [Microbiome and Multi-Omics AI](cells/multiomics.html#microbiome-multiomics-ai-theoretical)

### Systems Biology and Multiscale Modeling

#### References

- [SCENIC: single-cell regulatory network inference and clustering](https://doi.org/10.1038/nmeth.4463)Sara Aibar; Carmen Bravo González-Blas; Thomas Moerman; et al. 2017. Nature Methodsjournal articleCited in: [Systems Biology and Multiscale Modeling (passage 1)](cells/systems-biology.html#systems-biology-introduction); [Systems Biology and Multiscale Modeling (passage 2)](cells/systems-biology.html#systems-biology-grns); [Systems Biology and Multiscale Modeling (passage 3)](cells/systems-biology.html#systems-biology-models-datasets-benchmarks)
- [Gene regulatory network inference in the era of single-cell multi-omics](https://doi.org/10.1038/s41576-023-00618-5)Pau Badia-i-Mompel; Lorna Wessels; Sophia Müller-Dott; et al. 2023. Nature Reviews Geneticsjournal articleCited in: [Systems Biology and Multiscale Modeling](cells/systems-biology.html#systems-biology-introduction)
- [NicheNet: modeling intercellular communication by linking ligands to target genes](https://doi.org/10.1038/s41592-019-0667-5)Robin Browaeys; Wouter Saelens; Yvan Saeys. 2019. Nature Methodsjournal articleCited in: [Systems Biology and Multiscale Modeling](cells/systems-biology.html#systems-biology-models-datasets-benchmarks)
- [Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens](https://doi.org/10.1016/j.cell.2016.11.038)Atray Dixit; Oren Parnas; Biyu Li; et al. 2016. Celljournal articleCited in: [Systems Biology and Multiscale Modeling (passage 1)](cells/systems-biology.html#systems-biology-grns); [Systems Biology and Multiscale Modeling (passage 2)](cells/systems-biology.html#systems-biology-models-datasets-benchmarks)
- [PhysiCell: An open source physics-based cell simulator for 3-D multicellular systems](https://doi.org/10.1371/journal.pcbi.1005991)Ahmadreza Ghaffarizadeh; Randy Heiland; Samuel H. Friedman; et al. 2018. PLOS Computational Biologyjournal articleCited in: [Systems Biology and Multiscale Modeling (passage 1)](cells/systems-biology.html#systems-biology-multiscale); [Systems Biology and Multiscale Modeling (passage 2)](cells/systems-biology.html#systems-biology-models-datasets-benchmarks)
- [Dissecting cell identity via network inference and in silico gene perturbation](https://doi.org/10.1038/s41586-022-05688-9)Kenji Kamimoto; Blerta Stringa; Christy M. Hoffmann; et al. 2023. Naturejournal articleCited in: [Systems Biology and Multiscale Modeling (passage 1)](cells/systems-biology.html#systems-biology-grns); [Systems Biology and Multiscale Modeling (passage 2)](cells/systems-biology.html#systems-biology-models-datasets-benchmarks)
- [A Whole-Cell Computational Model Predicts Phenotype from Genotype](https://doi.org/10.1016/j.cell.2012.05.044)Jonathan R. Karr; Jayodita C. Sanghvi; Derek N. Macklin; et al. 2012. Celljournal articleCited in: [Systems Biology and Multiscale Modeling (passage 1)](cells/systems-biology.html#systems-biology-introduction); [Systems Biology and Multiscale Modeling (passage 2)](cells/systems-biology.html#systems-biology-multiscale); [Systems Biology and Multiscale Modeling (passage 3)](cells/systems-biology.html#systems-biology-models-datasets-benchmarks)
- [SBML Level 3: an extensible format for the exchange and reuse of biological models](https://doi.org/10.15252/msb.20199110)Sarah M Keating; Dagmar Waltemath; Matthias König; et al. 2020. Molecular Systems Biologyjournal articleCited in: [Systems Biology and Multiscale Modeling (passage 1)](cells/systems-biology.html#systems-biology-pathway-metabolic); [Systems Biology and Multiscale Modeling (passage 2)](cells/systems-biology.html#systems-biology-models-datasets-benchmarks)
- [BiGG Models: A platform for integrating, standardizing and sharing genome-scale models](https://doi.org/10.1093/nar/gkv1049)Zachary A. King; Justin Lu; Andreas Dräger; et al. 2016. Nucleic Acids Researchjournal articleCited in: [Systems Biology and Multiscale Modeling (passage 1)](cells/systems-biology.html#systems-biology-pathway-metabolic); [Systems Biology and Multiscale Modeling (passage 2)](cells/systems-biology.html#systems-biology-models-datasets-benchmarks)
- [BioModels Database: a free, centralized database of curated, published, quantitative kinetic models of biochemical and cellular systems](https://doi.org/10.1093/nar/gkj092)N. Le Novere. 2006. Nucleic Acids Researchjournal articleCited in: [Systems Biology and Multiscale Modeling (passage 1)](cells/systems-biology.html#systems-biology-pathway-metabolic); [Systems Biology and Multiscale Modeling (passage 2)](cells/systems-biology.html#systems-biology-models-datasets-benchmarks)
- [What is flux balance analysis?](https://doi.org/10.1038/nbt.1614)Jeffrey D Orth; Ines Thiele; Bernhard Ø Palsson. 2010. Nature Biotechnologyjournal articleCited in: [Systems Biology and Multiscale Modeling (passage 1)](cells/systems-biology.html#systems-biology-pathway-metabolic); [Systems Biology and Multiscale Modeling (passage 2)](cells/systems-biology.html#systems-biology-models-datasets-benchmarks)
- [Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data](https://doi.org/10.1038/s41592-019-0690-6)Aditya Pratapa; Amogh P. Jalihal; Jeffrey N. Law; et al. 2020. Nature Methodsjournal articleCited in: [Systems Biology and Multiscale Modeling (passage 1)](cells/systems-biology.html#systems-biology-introduction); [Systems Biology and Multiscale Modeling (passage 2)](cells/systems-biology.html#systems-biology-models-datasets-benchmarks)
- [Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq](https://doi.org/10.1016/j.cell.2022.05.013)Joseph M. Replogle; Reuben A. Saunders; Angela N. Pogson; et al. 2022. Celljournal articleCited in: [Systems Biology and Multiscale Modeling (passage 1)](cells/systems-biology.html#systems-biology-grns); [Systems Biology and Multiscale Modeling (passage 2)](cells/systems-biology.html#systems-biology-models-datasets-benchmarks)
- [Morpheus: a user-friendly modeling environment for multiscale and multicellular systems biology](https://doi.org/10.1093/bioinformatics/btt772)Jörn Starruß; Walter de Back; Lutz Brusch; et al. 2014. Bioinformaticsjournal articleCited in: [Systems Biology and Multiscale Modeling (passage 1)](cells/systems-biology.html#systems-biology-multiscale); [Systems Biology and Multiscale Modeling (passage 2)](cells/systems-biology.html#systems-biology-models-datasets-benchmarks)
- [A community-driven global reconstruction of human metabolism](https://doi.org/10.1038/nbt.2488)Ines Thiele; Neil Swainston; Ronan M T Fleming; et al. 2013. Nature Biotechnologyjournal articleCited in: [Systems Biology and Multiscale Modeling](cells/systems-biology.html#systems-biology-pathway-metabolic)

## Part IV: Organismal and Environmental Biology

### Neuroscience AI and Brain Foundation Models

#### References

- [Azabou et al., 2023](https://poyo-brain.github.io/)Cited in: [Neuroscience AI and Brain Foundation Models (passage 1)](organisms/neuroscience-ai.html#neuroscience-ai-latent-dynamics); [Neuroscience AI and Brain Foundation Models (passage 2)](organisms/neuroscience-ai.html#neuroscience-ai-cross-modal-foundation); [Neuroscience AI and Brain Foundation Models (passage 3)](organisms/neuroscience-ai.html#neuroscience-ai-models-datasets-benchmarks)
- [BICAN, NIH BRAIN Initiative](https://www.braininitiative.nih.gov/research/tools-and-technologies-brain-cells-and-circuits/brain-initiative-cell-atlas-network)Cited in: [Neuroscience AI and Brain Foundation Models](organisms/neuroscience-ai.html#neuroscience-ai-models-datasets-benchmarks)
- [BICAN, NIH BRAIN Initiative](https://www.nih.gov/brain/research/tools-technologies-brain-cells-circuits/brain-initiative-cell-atlas-network)Cited in: [Neuroscience AI and Brain Foundation Models (passage 1)](organisms/neuroscience-ai.html#neuroscience-ai-scope); [Neuroscience AI and Brain Foundation Models (passage 2)](organisms/neuroscience-ai.html#neuroscience-ai-brain-atlases)
- [Brain-Score](https://www.brain-score.org/tutorials/benchmarks/what-is-benchmark/)Cited in: [Neuroscience AI and Brain Foundation Models](organisms/neuroscience-ai.html#neuroscience-ai-models-datasets-benchmarks)
- [DANDI Archive](https://dandiarchive.org/)Cited in: [Neuroscience AI and Brain Foundation Models (passage 1)](organisms/neuroscience-ai.html#neuroscience-ai-activity-prediction); [Neuroscience AI and Brain Foundation Models (passage 2)](organisms/neuroscience-ai.html#neuroscience-ai-models-datasets-benchmarks)
- [A large-scale standardized physiological survey reveals functional organization of the mouse visual cortex](https://doi.org/10.1038/s41593-019-0550-9)Saskia E. J. de Vries; Jerome A. Lecoq; Michael A. Buice; et al. 2019. Nature Neurosciencejournal articleCited in: [Neuroscience AI and Brain Foundation Models (passage 1)](organisms/neuroscience-ai.html#neuroscience-ai-activity-prediction); [Neuroscience AI and Brain Foundation Models (passage 2)](organisms/neuroscience-ai.html#neuroscience-ai-models-datasets-benchmarks)
- [Functional connectomics reveals general wiring rule in mouse visual cortex](https://doi.org/10.1038/s41586-025-08840-3)Zhuokun Ding; Paul G. Fahey; Stelios Papadopoulos; et al. 2025. Naturejournal articleCited in: [Neuroscience AI and Brain Foundation Models (passage 1)](organisms/neuroscience-ai.html#neuroscience-ai-introduction)Recommended in: [Neuroscience AI and Brain Foundation Models (passage 2)](organisms/neuroscience-ai.html#neuroscience-ai-connectomics)
- [A high-performance neuroprosthesis for speech decoding and avatar control](https://doi.org/10.1038/s41586-023-06443-4)Sean L. Metzger; Kaylo T. Littlejohn; Alexander B. Silva; et al. 2023. Naturejournal articleCited in: [Neuroscience AI and Brain Foundation Models (passage 1)](organisms/neuroscience-ai.html#neuroscience-ai-introduction); [Neuroscience AI and Brain Foundation Models (passage 2)](organisms/neuroscience-ai.html#neuroscience-ai-neural-decoding)
- [MICrONS Consortium, 2025](https://doi.org/10.1038/s41586-025-08790-w)Cited in: [Neuroscience AI and Brain Foundation Models (passage 1)](organisms/neuroscience-ai.html#neuroscience-ai-introduction); [Neuroscience AI and Brain Foundation Models (passage 3)](organisms/neuroscience-ai.html#neuroscience-ai-models-datasets-benchmarks)Recommended in: [Neuroscience AI and Brain Foundation Models (passage 2)](organisms/neuroscience-ai.html#neuroscience-ai-connectomics)
- [Ortega Caro et al., 2024](https://proceedings.iclr.cc/paper_files/paper/2024/hash/029ce70401321de3808b3ac39e1ab167-Abstract-Conference.html)Cited in: [Neuroscience AI and Brain Foundation Models (passage 1)](organisms/neuroscience-ai.html#neuroscience-ai-scope); [Neuroscience AI and Brain Foundation Models (passage 2)](organisms/neuroscience-ai.html#neuroscience-ai-cross-modal-foundation); [Neuroscience AI and Brain Foundation Models (passage 3)](organisms/neuroscience-ai.html#neuroscience-ai-models-datasets-benchmarks)
- [Inferring single-trial neural population dynamics using sequential auto-encoders](https://doi.org/10.1038/s41592-018-0109-9)Chethan Pandarinath; Daniel J. O’Shea; Jasmine Collins; et al. 2018. Nature Methodsjournal articleCited in: [Neuroscience AI and Brain Foundation Models (passage 1)](organisms/neuroscience-ai.html#neuroscience-ai-scope); [Neuroscience AI and Brain Foundation Models (passage 2)](organisms/neuroscience-ai.html#neuroscience-ai-latent-dynamics); [Neuroscience AI and Brain Foundation Models (passage 3)](organisms/neuroscience-ai.html#neuroscience-ai-models-datasets-benchmarks)
- [Learnable latent embeddings for joint behavioural and neural analysis](https://doi.org/10.1038/s41586-023-06031-6)Steffen Schneider; Jin Hwa Lee; Mackenzie Weygandt Mathis. 2023. Naturejournal articleCited in: [Neuroscience AI and Brain Foundation Models (passage 1)](organisms/neuroscience-ai.html#neuroscience-ai-scope); [Neuroscience AI and Brain Foundation Models (passage 2)](organisms/neuroscience-ai.html#neuroscience-ai-latent-dynamics); [Neuroscience AI and Brain Foundation Models (passage 3)](organisms/neuroscience-ai.html#neuroscience-ai-models-datasets-benchmarks)
- [Semantic reconstruction of continuous language from non-invasive brain recordings](https://doi.org/10.1038/s41593-023-01304-9)Jerry Tang; Amanda LeBel; Shailee Jain; et al. 2023. Nature Neurosciencejournal articleCited in: [Neuroscience AI and Brain Foundation Models (passage 1)](organisms/neuroscience-ai.html#neuroscience-ai-introduction); [Neuroscience AI and Brain Foundation Models (passage 2)](organisms/neuroscience-ai.html#neuroscience-ai-neural-decoding)
- [The Allen Mouse Brain Common Coordinate Framework: A 3D Reference Atlas](https://doi.org/10.1016/j.cell.2020.04.007)Quanxin Wang; Song-Lin Ding; Yang Li; et al. 2020. Celljournal articleCited in: [Neuroscience AI and Brain Foundation Models (passage 1)](organisms/neuroscience-ai.html#neuroscience-ai-scope); [Neuroscience AI and Brain Foundation Models (passage 2)](organisms/neuroscience-ai.html#neuroscience-ai-brain-atlases); [Neuroscience AI and Brain Foundation Models (passage 3)](organisms/neuroscience-ai.html#neuroscience-ai-models-datasets-benchmarks)
- [Foundation model of neural activity predicts response to new stimulus types](https://doi.org/10.1038/s41586-025-08829-y)Eric Y. Wang; Paul G. Fahey; Zhuokun Ding; et al. 2025. Naturejournal articleCited in: [Neuroscience AI and Brain Foundation Models (passage 1)](organisms/neuroscience-ai.html#neuroscience-ai-introduction); [Neuroscience AI and Brain Foundation Models (passage 2)](organisms/neuroscience-ai.html#neuroscience-ai-activity-prediction); [Neuroscience AI and Brain Foundation Models (passage 3)](organisms/neuroscience-ai.html#neuroscience-ai-models-datasets-benchmarks)
- [A high-performance speech neuroprosthesis](https://doi.org/10.1038/s41586-023-06377-x)Francis R. Willett; Erin M. Kunz; Chaofei Fan; et al. 2023. Naturejournal articleCited in: [Neuroscience AI and Brain Foundation Models (passage 1)](organisms/neuroscience-ai.html#neuroscience-ai-introduction); [Neuroscience AI and Brain Foundation Models (passage 2)](organisms/neuroscience-ai.html#neuroscience-ai-neural-decoding)

### Aging and Longevity Biology AI

#### References

- [DNA methylation aging clocks: challenges and recommendations](https://doi.org/10.1186/s13059-019-1824-y)Christopher G. Bell; Robert Lowe; Peter D. Adams; et al. 2019. Genome Biologyjournal articleCited in: [Aging and Longevity Biology AI](organisms/aging-longevity.html#aging-longevity-methylation-clocks)
- [DunedinPACE, a DNA methylation biomarker of the pace of aging](https://doi.org/10.7554/eLife.73420)Daniel W Belsky; Avshalom Caspi; David L Corcoran; et al. 2022. eLifejournal articleCited in: [Aging and Longevity Biology AI (passage 1)](organisms/aging-longevity.html#aging-longevity-introduction); [Aging and Longevity Biology AI (passage 2)](organisms/aging-longevity.html#aging-longevity-what-it-measures); [Aging and Longevity Biology AI (passage 3)](organisms/aging-longevity.html#aging-longevity-pace-of-aging); [Aging and Longevity Biology AI (passage 4)](organisms/aging-longevity.html#aging-longevity-models-datasets-benchmarks)
- [CellAge, Human Ageing Genomic Resources](https://genomics.senescence.info/cells/)Cited in: [Aging and Longevity Biology AI (passage 1)](organisms/aging-longevity.html#aging-longevity-what-it-measures); [Aging and Longevity Biology AI (passage 2)](organisms/aging-longevity.html#aging-longevity-senescence); [Aging and Longevity Biology AI (passage 3)](organisms/aging-longevity.html#aging-longevity-models-datasets-benchmarks)
- [Genome-wide Methylation Profiles Reveal Quantitative Views of Human Aging Rates](https://doi.org/10.1016/j.molcel.2012.10.016)Gregory Hannum; Justin Guinney; Ling Zhao; et al. 2013. Molecular Celljournal articleCited in: [Aging and Longevity Biology AI (passage 1)](organisms/aging-longevity.html#aging-longevity-introduction); [Aging and Longevity Biology AI (passage 2)](organisms/aging-longevity.html#aging-longevity-methylation-clocks); [Aging and Longevity Biology AI (passage 3)](organisms/aging-longevity.html#aging-longevity-models-datasets-benchmarks)
- [Rapamycin fed late in life extends lifespan in genetically heterogeneous mice](https://doi.org/10.1038/nature08221)David E. Harrison; Randy Strong; Zelton Dave Sharp; et al. 2009. Naturejournal articleCited in: [Aging and Longevity Biology AI](organisms/aging-longevity.html#aging-longevity-geroscience)
- [DNA methylation age of human tissues and cell types](https://doi.org/10.1186/gb-2013-14-10-r115)Steve Horvath. 2013. Genome Biologyjournal articleCited in: [Aging and Longevity Biology AI (passage 1)](organisms/aging-longevity.html#aging-longevity-introduction); [Aging and Longevity Biology AI (passage 2)](organisms/aging-longevity.html#aging-longevity-methylation-clocks); [Aging and Longevity Biology AI (passage 3)](organisms/aging-longevity.html#aging-longevity-models-datasets-benchmarks)
- [Geroscience: Linking Aging to Chronic Disease](https://doi.org/10.1016/j.cell.2014.10.039)Brian K. Kennedy; Shelley L. Berger; Anne Brunet; et al. 2014. Celljournal articleCited in: [Aging and Longevity Biology AI](organisms/aging-longevity.html#aging-longevity-geroscience)
- [Undulating changes in human plasma proteome profiles across the lifespan](https://doi.org/10.1038/s41591-019-0673-2)Benoit Lehallier; David Gate; Nicholas Schaum; et al. 2019. Nature Medicinejournal articleCited in: [Aging and Longevity Biology AI (passage 1)](organisms/aging-longevity.html#aging-longevity-omics-clocks); [Aging and Longevity Biology AI (passage 2)](organisms/aging-longevity.html#aging-longevity-models-datasets-benchmarks)
- [An epigenetic biomarker of aging for lifespan and healthspan](https://doi.org/10.18632/aging.101414)Morgan E. Levine; Ake T. Lu; Austin Quach; et al. 2018. Agingjournal articleCited in: [Aging and Longevity Biology AI (passage 1)](organisms/aging-longevity.html#aging-longevity-introduction); [Aging and Longevity Biology AI (passage 2)](organisms/aging-longevity.html#aging-longevity-methylation-clocks); [Aging and Longevity Biology AI (passage 3)](organisms/aging-longevity.html#aging-longevity-models-datasets-benchmarks)
- [DNA methylation GrimAge strongly predicts lifespan and healthspan](https://doi.org/10.18632/aging.101684)Ake T. Lu; Austin Quach; James G. Wilson; et al. 2019. Agingjournal articleCited in: [Aging and Longevity Biology AI (passage 1)](organisms/aging-longevity.html#aging-longevity-introduction); [Aging and Longevity Biology AI (passage 2)](organisms/aging-longevity.html#aging-longevity-methylation-clocks); [Aging and Longevity Biology AI (passage 3)](organisms/aging-longevity.html#aging-longevity-models-datasets-benchmarks)
- [Hallmarks of aging: An expanding universe](https://doi.org/10.1016/j.cell.2022.11.001)Carlos López-Otín; Maria A. Blasco; Linda Partridge; et al. 2023. Celljournal articleCited in: [Aging and Longevity Biology AI](organisms/aging-longevity.html#aging-longevity-geroscience)
- [Validation of biomarkers of aging](https://doi.org/10.1038/s41591-023-02784-9)Mahdi Moqri; Chiara Herzog; Jesse R. Poganik; et al. 2024. Nature Medicinejournal articleCited in: [Aging and Longevity Biology AI (passage 1)](organisms/aging-longevity.html#aging-longevity-methylation-clocks); [Aging and Longevity Biology AI (passage 2)](organisms/aging-longevity.html#aging-longevity-models-datasets-benchmarks)
- [NIA ITP, 2026](https://grants.nih.gov/grants/guide/notice-files/NOT-AG-25-014.html)Cited in: [Aging and Longevity Biology AI (passage 1)](organisms/aging-longevity.html#aging-longevity-geroscience); [Aging and Longevity Biology AI (passage 2)](organisms/aging-longevity.html#aging-longevity-models-datasets-benchmarks)
- [NIH SenNet](https://commonfund.nih.gov/senescence)Cited in: [Aging and Longevity Biology AI (passage 1)](organisms/aging-longevity.html#aging-longevity-what-it-measures); [Aging and Longevity Biology AI (passage 2)](organisms/aging-longevity.html#aging-longevity-senescence); [Aging and Longevity Biology AI (passage 3)](organisms/aging-longevity.html#aging-longevity-models-datasets-benchmarks)
- [Organ aging signatures in the plasma proteome track health and disease](https://doi.org/10.1038/s41586-023-06802-1)Hamilton Se-Hwee Oh; Jarod Rutledge; Daniel Nachun; et al. 2023. Naturejournal articleCited in: [Aging and Longevity Biology AI (passage 1)](organisms/aging-longevity.html#aging-longevity-what-it-measures); [Aging and Longevity Biology AI (passage 2)](organisms/aging-longevity.html#aging-longevity-omics-clocks); [Aging and Longevity Biology AI (passage 3)](organisms/aging-longevity.html#aging-longevity-models-datasets-benchmarks)
- [Effect of long-term caloric restriction on DNA methylation measures of biological aging in healthy adults from the CALERIE trial](https://doi.org/10.1038/s43587-022-00357-y)R. Waziry; C. P. Ryan; D. L. Corcoran; et al. 2023. Nature Agingjournal articleCited in: [Aging and Longevity Biology AI](organisms/aging-longevity.html#aging-longevity-pace-of-aging)
- [An open benchmark and language models for AI in aging biology](https://doi.org/10.1016/j.cell.2026.08.026)Alex Zhavoronkov; Vladimir Naumov; Denis Sidorenko; Alex Aliper; Vladimir Aladinskiy; Ramin Hasani; Alexander Amini; Katerina Nasto; Mathieu Reymond; Rim Shayakhmetov; Zulfat Miftakhutdinov; Vadim N. Gladyshev; Fedor Galkin. 2026. Celljournal articleCited in: [Aging and Longevity Biology AI](organisms/aging-longevity.html#aging-longevity-models-datasets-benchmarks)

### Plant, Crop, and Agricultural AI

#### References

- [Field high-throughput phenotyping: the new crop breeding frontier](https://doi.org/10.1016/j.tplants.2013.09.008)José Luis Araus; Jill E. Cairns. 2014. Trends in Plant Sciencejournal articleCited in: [Plant, Crop, and Agricultural AI (passage 1)](organisms/agriculture-ai.html#agriculture-ai-measurement)Recommended in: [Plant, Crop, and Agricultural AI (passage 2)](organisms/agriculture-ai.html#agriculture-ai-further-reading)
- [BrAPI](https://brapi.org/)Cited in: [Plant, Crop, and Agricultural AI (passage 1)](organisms/agriculture-ai.html#agriculture-ai-demonstrated); [Plant, Crop, and Agricultural AI (passage 2)](organisms/agriculture-ai.html#agriculture-ai-further-reading)
- [Breedbase About](https://breedbase.org/about/index.pl)Cited in: [Plant, Crop, and Agricultural AI](organisms/agriculture-ai.html#agriculture-ai-demonstrated)
- [CGIAR EBS implementation note](https://www.cgiar.org/initiative-result/how-ebs-advances-iita-legume-breeding)Cited in: [Plant, Crop, and Agricultural AI (passage 1)](organisms/agriculture-ai.html#agriculture-ai-demonstrated); [Plant, Crop, and Agricultural AI (passage 2)](organisms/agriculture-ai.html#agriculture-ai-models-datasets-benchmarks)
- [CGIAR Open Access and Open Data](https://www.cgiar.org/how-we-work/open-access)Cited in: [Plant, Crop, and Agricultural AI (passage 1)](organisms/agriculture-ai.html#agriculture-ai-models-datasets-benchmarks); [Plant, Crop, and Agricultural AI (passage 2)](organisms/agriculture-ai.html#agriculture-ai-further-reading)
- [Genomic Selection in Plant Breeding: Methods, Models, and Perspectives](https://doi.org/10.1016/j.tplants.2017.08.011)José Crossa; Paulino Pérez-Rodríguez; Jaime Cuevas; et al. 2017. Trends in Plant Sciencejournal articleCited in: [Plant, Crop, and Agricultural AI (passage 1)](organisms/agriculture-ai.html#agriculture-ai-introduction); [Plant, Crop, and Agricultural AI (passage 2)](organisms/agriculture-ai.html#agriculture-ai-demonstrated)Recommended in: [Plant, Crop, and Agricultural AI (passage 3)](organisms/agriculture-ai.html#agriculture-ai-further-reading)
- [Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods](https://doi.org/10.34133/2020/3521852)Etienne David; Simon Madec; Pouria Sadeghi-Tehran; et al. 2020. Plant Phenomicsjournal articleCited in: [Plant, Crop, and Agricultural AI (passage 1)](organisms/agriculture-ai.html#agriculture-ai-demonstrated); [Plant, Crop, and Agricultural AI (passage 2)](organisms/agriculture-ai.html#agriculture-ai-models-datasets-benchmarks)Recommended in: [Plant, Crop, and Agricultural AI (passage 3)](organisms/agriculture-ai.html#agriculture-ai-further-reading)
- [Global Wheat Head Detection Challenges: Winning Models and Application for Head Counting](https://doi.org/10.34133/plantphenomics.0059)Etienne David; Franklin Ogidi; Daniel Smith; et al. 2023. Plant Phenomicsjournal articleCited in: [Plant, Crop, and Agricultural AI (passage 1)](organisms/agriculture-ai.html#agriculture-ai-demonstrated); [Plant, Crop, and Agricultural AI (passage 2)](organisms/agriculture-ai.html#agriculture-ai-models-datasets-benchmarks)
- [Ensembl Plants](https://plants.ensembl.org/)Cited in: [Plant, Crop, and Agricultural AI (passage 1)](organisms/agriculture-ai.html#agriculture-ai-theoretical); [Plant, Crop, and Agricultural AI (passage 2)](organisms/agriculture-ai.html#agriculture-ai-models-datasets-benchmarks); [Plant, Crop, and Agricultural AI (passage 3)](organisms/agriculture-ai.html#agriculture-ai-further-reading)
- [FAOSTAT](https://www.fao.org/faostat/en/)Cited in: [Plant, Crop, and Agricultural AI (passage 1)](organisms/agriculture-ai.html#agriculture-ai-demonstrated); [Plant, Crop, and Agricultural AI (passage 2)](organisms/agriculture-ai.html#agriculture-ai-models-datasets-benchmarks); [Plant, Crop, and Agricultural AI (passage 3)](organisms/agriculture-ai.html#agriculture-ai-further-reading)
- [Gramene](https://www.gramene.org/)Cited in: [Plant, Crop, and Agricultural AI (passage 1)](organisms/agriculture-ai.html#agriculture-ai-theoretical); [Plant, Crop, and Agricultural AI (passage 2)](organisms/agriculture-ai.html#agriculture-ai-models-datasets-benchmarks); [Plant, Crop, and Agricultural AI (passage 3)](organisms/agriculture-ai.html#agriculture-ai-further-reading)
- [A foundational large language model for edible plant genomes](https://doi.org/10.1038/s42003-024-06465-2)Javier Mendoza-Revilla; Evan Trop; Liam Gonzalez; et al. 2024. Communications Biologyjournal articleCited in: [Plant, Crop, and Agricultural AI (passage 1)](organisms/agriculture-ai.html#agriculture-ai-introduction); [Plant, Crop, and Agricultural AI (passage 2)](organisms/agriculture-ai.html#agriculture-ai-demonstrated); [Plant, Crop, and Agricultural AI (passage 3)](organisms/agriculture-ai.html#agriculture-ai-models-datasets-benchmarks)Recommended in: [Plant, Crop, and Agricultural AI (passage 4)](organisms/agriculture-ai.html#agriculture-ai-further-reading)
- [Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps](https://doi.org/10.1093/genetics/157.4.1819)T H E Meuwissen; B J Hayes; M E Goddard. 2001. Geneticsjournal articleCited in: [Plant, Crop, and Agricultural AI (passage 1)](organisms/agriculture-ai.html#agriculture-ai-introduction); [Plant, Crop, and Agricultural AI (passage 2)](organisms/agriculture-ai.html#agriculture-ai-demonstrated)Recommended in: [Plant, Crop, and Agricultural AI (passage 3)](organisms/agriculture-ai.html#agriculture-ai-further-reading)
- [Breedbase: a digital ecosystem for modern plant breeding](https://doi.org/10.1093/g3journal/jkac078)Nicolas Morales; Alex C Ogbonna; Bryan J Ellerbrock; et al. 2022. G3 Genes\|Genomes\|Geneticsjournal articleCited in: [Plant, Crop, and Agricultural AI (passage 1)](organisms/agriculture-ai.html#agriculture-ai-demonstrated); [Plant, Crop, and Agricultural AI (passage 2)](organisms/agriculture-ai.html#agriculture-ai-models-datasets-benchmarks)
- [BrAPI—an application programming interface for plant breeding applications](https://doi.org/10.1093/bioinformatics/btz190)Peter Selby; Rafael Abbeloos; Jan Erik Backlund; et al. 2019. Bioinformaticsjournal articleCited in: [Plant, Crop, and Agricultural AI (passage 1)](organisms/agriculture-ai.html#agriculture-ai-demonstrated); [Plant, Crop, and Agricultural AI (passage 2)](organisms/agriculture-ai.html#agriculture-ai-models-datasets-benchmarks)
- [Tseng et al., 2021](https://datasets-benchmarks-proceedings.neurips.cc/paper_files/paper/2021/hash/54229abfcfa5649e7003b83dd4755294-Abstract-round2.html)Cited in: [Plant, Crop, and Agricultural AI (passage 1)](organisms/agriculture-ai.html#agriculture-ai-demonstrated); [Plant, Crop, and Agricultural AI (passage 2)](organisms/agriculture-ai.html#agriculture-ai-models-datasets-benchmarks)Recommended in: [Plant, Crop, and Agricultural AI (passage 3)](organisms/agriculture-ai.html#agriculture-ai-further-reading)
- [USDA NASS Quick Stats](https://www.nass.usda.gov/quick-stats)Cited in: [Plant, Crop, and Agricultural AI (passage 1)](organisms/agriculture-ai.html#agriculture-ai-demonstrated); [Plant, Crop, and Agricultural AI (passage 2)](organisms/agriculture-ai.html#agriculture-ai-models-datasets-benchmarks); [Plant, Crop, and Agricultural AI (passage 3)](organisms/agriculture-ai.html#agriculture-ai-further-reading)
- [USDA-ARS GRIN](https://www.ars-grin.gov/)Cited in: [Plant, Crop, and Agricultural AI (passage 1)](organisms/agriculture-ai.html#agriculture-ai-models-datasets-benchmarks); [Plant, Crop, and Agricultural AI (passage 2)](organisms/agriculture-ai.html#agriculture-ai-further-reading)
- [An interpretable RNA foundation model for exploring functional RNA motifs in plants](https://doi.org/10.1038/s42256-024-00946-z)Haopeng Yu; Heng Yang; Wenqing Sun; et al. 2024. Nature Machine Intelligencejournal articleCited in: [Plant, Crop, and Agricultural AI (passage 1)](organisms/agriculture-ai.html#agriculture-ai-introduction); [Plant, Crop, and Agricultural AI (passage 2)](organisms/agriculture-ai.html#agriculture-ai-demonstrated); [Plant, Crop, and Agricultural AI (passage 3)](organisms/agriculture-ai.html#agriculture-ai-models-datasets-benchmarks)Recommended in: [Plant, Crop, and Agricultural AI (passage 4)](organisms/agriculture-ai.html#agriculture-ai-further-reading)

#### Recommended reading

- [Breedbase](https://breedbase.org/)Recommended in: [Plant, Crop, and Agricultural AI](organisms/agriculture-ai.html#agriculture-ai-further-reading)
- [Breeding crops to feed 10 billion](https://doi.org/10.1038/s41587-019-0152-9)Lee T. Hickey; Amber N. Hafeez; Hannah Robinson; et al. 2019. Nature Biotechnologyjournal articleRecommended in: [Plant, Crop, and Agricultural AI](organisms/agriculture-ai.html#agriculture-ai-further-reading)

### Environmental and Ecological AI

#### References

- [Standards for distribution models in biodiversity assessments](https://doi.org/10.1126/sciadv.aat4858)Miguel B. Araújo; Robert P. Anderson; A. Márcia Barbosa; et al. 2019. Science Advancesjournal articleCited in: [Environmental and Ecological AI (passage 1)](organisms/environmental-ecology-ai.html#environmental-ecology-demonstrated)Recommended in: [Environmental and Ecological AI (passage 2)](organisms/environmental-ecology-ai.html#environmental-ecology-further-reading)
- [BirdNET publications](https://birdnet.cornell.edu/publications/)Cited in: [Environmental and Ecological AI](organisms/environmental-ecology-ai.html#environmental-ecology-demonstrated)
- [Environmental DNA metabarcoding: Transforming how we survey animal and plant communities](https://doi.org/10.1111/mec.14350)Kristy Deiner; Holly M. Bik; Elvira Mächler; et al. 2017. Molecular Ecologyjournal articleCited in: [Environmental and Ecological AI (passage 1)](organisms/environmental-ecology-ai.html#environmental-ecology-demonstrated)Recommended in: [Environmental and Ecological AI (passage 2)](organisms/environmental-ecology-ai.html#environmental-ecology-further-reading)
- [Iterative near-term ecological forecasting: Needs, opportunities, and challenges](https://doi.org/10.1073/pnas.1710231115)Michael C. Dietze; Andrew Fox; Lindsay M. Beck-Johnson; et al. 2018. Proceedings of the National Academy of Sciencesjournal articleCited in: [Environmental and Ecological AI (passage 1)](organisms/environmental-ecology-ai.html#environmental-ecology-demonstrated)Recommended in: [Environmental and Ecological AI (passage 2)](organisms/environmental-ecology-ai.html#environmental-ecology-further-reading)
- [eBird data products](https://science.ebird.org/en/use-ebird-data/download-ebird-data-products)Cited in: [Environmental and Ecological AI](organisms/environmental-ecology-ai.html#environmental-ecology-models-datasets-benchmarks)
- [Species Distribution Models: Ecological Explanation and Prediction Across Space and Time](https://doi.org/10.1146/annurev.ecolsys.110308.120159)Jane Elith; John R. Leathwick. 2009. Annual Review of Ecology, Evolution, and Systematicsjournal articleCited in: [Environmental and Ecological AI (passage 1)](organisms/environmental-ecology-ai.html#environmental-ecology-demonstrated); [Environmental and Ecological AI (passage 2)](organisms/environmental-ecology-ai.html#environmental-ecology-models-datasets-benchmarks)Recommended in: [Environmental and Ecological AI (passage 3)](organisms/environmental-ecology-ai.html#environmental-ecology-further-reading)
- [GBIF](https://www.gbif.org/en/)Cited in: [Environmental and Ecological AI (passage 1)](organisms/environmental-ecology-ai.html#environmental-ecology-demonstrated); [Environmental and Ecological AI (passage 2)](organisms/environmental-ecology-ai.html#environmental-ecology-models-datasets-benchmarks); [Environmental and Ecological AI (passage 3)](organisms/environmental-ecology-ai.html#environmental-ecology-further-reading)
- [iNaturalist Research Grade](https://help.inaturalist.org/en/support/solutions/articles/151000169936)Cited in: [Environmental and Ecological AI (passage 1)](organisms/environmental-ecology-ai.html#environmental-ecology-demonstrated); [Environmental and Ecological AI (passage 2)](organisms/environmental-ecology-ai.html#environmental-ecology-models-datasets-benchmarks)
- [iNaturalist research use](https://help.inaturalist.org/en/support/solutions/articles/151000170341-how-are-inaturalist-data-used-for-research-)Cited in: [Environmental and Ecological AI (passage 1)](organisms/environmental-ecology-ai.html#environmental-ecology-demonstrated); [Environmental and Ecological AI (passage 2)](organisms/environmental-ecology-ai.html#environmental-ecology-further-reading)
- [BirdNET: A deep learning solution for avian diversity monitoring](https://doi.org/10.1016/j.ecoinf.2021.101236)Stefan Kahl; Connor M. Wood; Maximilian Eibl; et al. 2021. Ecological Informaticsjournal articleCited in: [Environmental and Ecological AI (passage 1)](organisms/environmental-ecology-ai.html#environmental-ecology-demonstrated); [Environmental and Ecological AI (passage 2)](organisms/environmental-ecology-ai.html#environmental-ecology-models-datasets-benchmarks)Recommended in: [Environmental and Ecological AI (passage 3)](organisms/environmental-ecology-ai.html#environmental-ecology-further-reading)
- [Map of Life](https://www.mol.org/)Cited in: [Environmental and Ecological AI (passage 1)](organisms/environmental-ecology-ai.html#environmental-ecology-models-datasets-benchmarks); [Environmental and Ecological AI (passage 2)](organisms/environmental-ecology-ai.html#environmental-ecology-further-reading)
- [NASA Earth Science Data](https://science.nasa.gov/earth/data/)Cited in: [Environmental and Ecological AI (passage 1)](organisms/environmental-ecology-ai.html#environmental-ecology-models-datasets-benchmarks); [Environmental and Ecological AI (passage 2)](organisms/environmental-ecology-ai.html#environmental-ecology-further-reading)
- [NOAA CoastWatch](https://coastwatch.noaa.gov/)Cited in: [Environmental and Ecological AI (passage 1)](organisms/environmental-ecology-ai.html#environmental-ecology-models-datasets-benchmarks); [Environmental and Ecological AI (passage 2)](organisms/environmental-ecology-ai.html#environmental-ecology-further-reading)
- [Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning](https://doi.org/10.1073/pnas.1719367115)Mohammad Sadegh Norouzzadeh; Anh Nguyen; Margaret Kosmala; et al. 2018. Proceedings of the National Academy of Sciencesjournal articleCited in: [Environmental and Ecological AI (passage 1)](organisms/environmental-ecology-ai.html#environmental-ecology-demonstrated)Recommended in: [Environmental and Ecological AI (passage 2)](organisms/environmental-ecology-ai.html#environmental-ecology-further-reading)
- [NSF NEON](https://www.neonscience.org/)Cited in: [Environmental and Ecological AI (passage 1)](organisms/environmental-ecology-ai.html#environmental-ecology-demonstrated); [Environmental and Ecological AI (passage 2)](organisms/environmental-ecology-ai.html#environmental-ecology-models-datasets-benchmarks); [Environmental and Ecological AI (passage 3)](organisms/environmental-ecology-ai.html#environmental-ecology-further-reading)
- [NSF NEON overview](https://www.nsf.gov/focus-areas/infrastructure/national-ecological-observatory-network)Cited in: [Environmental and Ecological AI](organisms/environmental-ecology-ai.html#environmental-ecology-demonstrated)
- [OBIS data access](https://obis.org/data/access)Cited in: [Environmental and Ecological AI (passage 1)](organisms/environmental-ecology-ai.html#environmental-ecology-demonstrated); [Environmental and Ecological AI (passage 2)](organisms/environmental-ecology-ai.html#environmental-ecology-models-datasets-benchmarks)
- [OBIS portal](https://portal.obis.org/)Cited in: [Environmental and Ecological AI (passage 1)](organisms/environmental-ecology-ai.html#environmental-ecology-demonstrated); [Environmental and Ecological AI (passage 2)](organisms/environmental-ecology-ai.html#environmental-ecology-further-reading)
- [Maximum entropy modeling of species geographic distributions](https://doi.org/10.1016/j.ecolmodel.2005.03.026)Steven J. Phillips; Robert P. Anderson; Robert E. Schapire. 2006. Ecological Modellingjournal articleCited in: [Environmental and Ecological AI (passage 1)](organisms/environmental-ecology-ai.html#environmental-ecology-demonstrated); [Environmental and Ecological AI (passage 2)](organisms/environmental-ecology-ai.html#environmental-ecology-models-datasets-benchmarks)Recommended in: [Environmental and Ecological AI (passage 3)](organisms/environmental-ecology-ai.html#environmental-ecology-further-reading)
- [Harnessing artificial intelligence to fill global shortfalls in biodiversity knowledge](https://doi.org/10.1038/s44358-025-00022-3)Laura J. Pollock; Justin Kitzes; Sara Beery; et al. 2025. Nature Reviews Biodiversityjournal articleCited in: [Environmental and Ecological AI (passage 1)](organisms/environmental-ecology-ai.html#environmental-ecology-introduction)Recommended in: [Environmental and Ecological AI (passage 2)](organisms/environmental-ecology-ai.html#environmental-ecology-further-reading)
- [Generative AI as a tool to accelerate the field of ecology](https://doi.org/10.1038/s41559-024-02623-1)Kasim Rafiq; Sara Beery; Meredith S. Palmer; et al. 2025. Nature Ecology & Evolutionjournal articleCited in: [Environmental and Ecological AI (passage 1)](organisms/environmental-ecology-ai.html#environmental-ecology-introduction)Recommended in: [Environmental and Ecological AI (passage 2)](organisms/environmental-ecology-ai.html#environmental-ecology-further-reading)
- [Cross‐validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure](https://doi.org/10.1111/ecog.02881)David R. Roberts; Volker Bahn; Simone Ciuti; et al. 2017. Ecographyjournal articleCited in: [Environmental and Ecological AI](organisms/environmental-ecology-ai.html#environmental-ecology-scale)
- [Towards next‐generation biodiversity assessment using DNA metabarcoding](https://doi.org/10.1111/j.1365-294X.2012.05470.x)PIERRE TABERLET; ERIC COISSAC; FRANÇOIS POMPANON; et al. 2012. Molecular Ecologyjournal articleCited in: [Environmental and Ecological AI (passage 1)](organisms/environmental-ecology-ai.html#environmental-ecology-demonstrated)Recommended in: [Environmental and Ecological AI (passage 2)](organisms/environmental-ecology-ai.html#environmental-ecology-further-reading)
- [USGS Landsat](https://www.usgs.gov/landsat)Cited in: [Environmental and Ecological AI (passage 1)](organisms/environmental-ecology-ai.html#environmental-ecology-models-datasets-benchmarks); [Environmental and Ecological AI (passage 2)](organisms/environmental-ecology-ai.html#environmental-ecology-further-reading)
- [Wildlife Insights](https://www.wildlifeinsights.org/)Cited in: [Environmental and Ecological AI (passage 1)](organisms/environmental-ecology-ai.html#environmental-ecology-demonstrated); [Environmental and Ecological AI (passage 2)](organisms/environmental-ecology-ai.html#environmental-ecology-models-datasets-benchmarks); [Environmental and Ecological AI (passage 3)](organisms/environmental-ecology-ai.html#environmental-ecology-further-reading)

### Virtual Organisms and Digital Biology

#### References

- [Enhancing randomized clinical trials with digital twins](https://doi.org/10.1038/s41540-025-00592-0)Hossein Akbarialiabad; Amirmohammad Pasdar; Dédée F. Murrell; et al. 2025. npj Systems Biology and Applicationsjournal articleCited in: [Virtual Organisms and Digital Biology](organisms/virtual-organisms.html#virtual-organisms-pbpk-qsp)
- [How to build the virtual cell with artificial intelligence: Priorities and opportunities](https://doi.org/10.1016/j.cell.2024.11.015)Charlotte Bunne; Yusuf Roohani; Yanay Rosen; et al. 2024. Celljournal articleCited in: [Virtual Organisms and Digital Biology (passage 1)](organisms/virtual-organisms.html#virtual-organisms-introduction); [Virtual Organisms and Digital Biology (passage 2)](organisms/virtual-organisms.html#virtual-organisms-theoretical)
- [Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis](https://doi.org/10.1126/science.aar3131)Jeffrey A. Farrell; Yiqun Wang; Samantha J. Riesenfeld; et al. 2018. Sciencejournal articleCited in: [Virtual Organisms and Digital Biology (passage 1)](organisms/virtual-organisms.html#zebrafish-development-imaging-and-embryo-scale-perturbation); [Virtual Organisms and Digital Biology (passage 2)](organisms/virtual-organisms.html#virtual-organisms-models-datasets-benchmarks)
- [FDA, 2025](https://www.fda.gov/regulatory-information/search-fda-guidance-documents/considerations-use-artificial-intelligence-support-regulatory-decision-making-drug-and-biological)Cited in: [Virtual Organisms and Digital Biology](organisms/virtual-organisms.html#virtual-organisms-pbpk-qsp)
- [FDA, Computational Modeling and Simulation Credibility](https://www.fda.gov/regulatory-information/search-fda-guidance-documents/assessing-credibility-computational-modeling-and-simulation-medical-device-submissions)Cited in: [Virtual Organisms and Digital Biology (passage 1)](organisms/virtual-organisms.html#virtual-organisms-introduction); [Virtual Organisms and Digital Biology (passage 2)](organisms/virtual-organisms.html#virtual-organisms-pbpk-qsp)
- [FDA, M15 General Principles for Model-Informed Drug Development](https://www.fda.gov/regulatory-information/search-fda-guidance-documents/m15-general-principles-model-informed-drug-development)Cited in: [Virtual Organisms and Digital Biology](organisms/virtual-organisms.html#virtual-organisms-pbpk-qsp)
- [FDA, Model-Informed Drug Development Paired Meeting Program](https://www.fda.gov/drugs/development-resources/model-informed-drug-development-paired-meeting-program)Cited in: [Virtual Organisms and Digital Biology](organisms/virtual-organisms.html#virtual-organisms-pbpk-qsp)
- [FDA, Model-Informed Product Development](https://www.fda.gov/science-research/focus-areas-regulatory-science-report/focus-area-model-informed-product-development)Cited in: [Virtual Organisms and Digital Biology](organisms/virtual-organisms.html#virtual-organisms-introduction)
- [PhysiCell: An open source physics-based cell simulator for 3-D multicellular systems](https://doi.org/10.1371/journal.pcbi.1005991)Ahmadreza Ghaffarizadeh; Randy Heiland; Samuel H. Friedman; et al. 2018. PLOS Computational Biologyjournal articleCited in: [Virtual Organisms and Digital Biology](organisms/virtual-organisms.html#virtual-organisms-models-datasets-benchmarks)
- [The International Mouse Phenotyping Consortium: comprehensive knockout phenotyping underpinning the study of human disease](https://doi.org/10.1093/nar/gkac972)Tudor Groza; Federico Lopez Gomez; Hamed Haseli Mashhadi; et al. 2022. Nucleic Acids Researchjournal articleCited in: [Virtual Organisms and Digital Biology (passage 1)](organisms/virtual-organisms.html#mouse-mammalian-physiology-knockout-phenotyping-and-brain-atlases); [Virtual Organisms and Digital Biology (passage 2)](organisms/virtual-organisms.html#virtual-organisms-models-datasets-benchmarks)
- [IMPC](https://www.mousephenotype.org/)Cited in: [Virtual Organisms and Digital Biology](organisms/virtual-organisms.html#mouse-mammalian-physiology-knockout-phenotyping-and-brain-atlases)
- [A Whole-Cell Computational Model Predicts Phenotype from Genotype](https://doi.org/10.1016/j.cell.2012.05.044)Jonathan R. Karr; Jayodita C. Sanghvi; Derek N. Macklin; et al. 2012. Celljournal articleCited in: [Virtual Organisms and Digital Biology (passage 1)](organisms/virtual-organisms.html#virtual-organisms-introduction); [Virtual Organisms and Digital Biology (passage 2)](organisms/virtual-organisms.html#virtual-organisms-models-datasets-benchmarks)
- [The Evolution and Future Directions of PBPK Modeling in FDA Regulatory Review](https://doi.org/10.3390/pharmaceutics17111413)Yangkexin Li; Henry Sun; Zuoli Zhang. 2025. Pharmaceuticsjournal articleCited in: [Virtual Organisms and Digital Biology](organisms/virtual-organisms.html#virtual-organisms-pbpk-qsp)
- [MGI](https://www.informatics.jax.org/)Cited in: [Virtual Organisms and Digital Biology (passage 1)](organisms/virtual-organisms.html#mouse-mammalian-physiology-knockout-phenotyping-and-brain-atlases); [Virtual Organisms and Digital Biology (passage 2)](organisms/virtual-organisms.html#virtual-organisms-models-datasets-benchmarks)
- [OpenWorm](https://openworm.org/)Cited in: [Virtual Organisms and Digital Biology (passage 1)](organisms/virtual-organisms.html#c.-elegans-anatomy-connectome-development-and-behavior); [Virtual Organisms and Digital Biology (passage 2)](organisms/virtual-organisms.html#virtual-organisms-models-datasets-benchmarks)
- [A lineage-resolved molecular atlas of C. elegans embryogenesis at single-cell resolution](https://doi.org/10.1126/science.aax1971)Jonathan S. Packer; Qin Zhu; Chau Huynh; et al. 2019. Sciencejournal articleCited in: [Virtual Organisms and Digital Biology (passage 1)](organisms/virtual-organisms.html#c.-elegans-anatomy-connectome-development-and-behavior); [Virtual Organisms and Digital Biology (passage 2)](organisms/virtual-organisms.html#virtual-organisms-models-datasets-benchmarks)
- [Digital twins, synthetic patient data, and in-silico trials: can they empower paediatric clinical trials?](https://doi.org/10.1016/j.landig.2025.01.007)Mohan Pammi; Prakesh S Shah; Liu K Yang; et al. 2025. The Lancet Digital Healthjournal articleCited in: [Virtual Organisms and Digital Biology](organisms/virtual-organisms.html#virtual-organisms-pbpk-qsp)
- [Credibility assessment of in silico clinical trials for medical devices](https://doi.org/10.1371/journal.pcbi.1012289)Pras Pathmanathan; Kenneth Aycock; Andreu Badal; et al. 2024. PLOS Computational Biologyjournal articleCited in: [Virtual Organisms and Digital Biology](organisms/virtual-organisms.html#virtual-organisms-pbpk-qsp)
- [PlantCV](https://plantcv.org/)Cited in: [Virtual Organisms and Digital Biology (passage 1)](organisms/virtual-organisms.html#plants-genotype-environment-development-and-field-conditions); [Virtual Organisms and Digital Biology (passage 2)](organisms/virtual-organisms.html#virtual-organisms-models-datasets-benchmarks)
- [Towards the construction of a virtual yeast](https://doi.org/10.1038/s41586-026-10574-9)Liujia Qian; Zizhuo Zhou; Peijie Zhou; et al. 2026. Naturejournal articleCited in: [Virtual Organisms and Digital Biology](organisms/virtual-organisms.html#virtual-organisms-theoretical)
- [Virtual Cell Challenge: Toward a Turing test for the virtual cell](https://doi.org/10.1016/j.cell.2025.06.008)Yusuf H. Roohani; Tony J. Hua; Po-Yuan Tung; et al. 2025. Celljournal articleCited in: [Virtual Organisms and Digital Biology (passage 1)](organisms/virtual-organisms.html#virtual-organisms-introduction); [Virtual Organisms and Digital Biology (passage 2)](organisms/virtual-organisms.html#virtual-organisms-models-datasets-benchmarks)
- [Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification](https://doi.org/10.1124/dmd.115.065920)Jennifer E. Sager; Jingjing Yu; Isabelle Ragueneau-Majlessi; et al. 2015. Drug Metabolism and Dispositionjournal articleCited in: [Virtual Organisms and Digital Biology](organisms/virtual-organisms.html#virtual-organisms-pbpk-qsp)
- [Embryo-scale reverse genetics at single-cell resolution](https://doi.org/10.1038/s41586-023-06720-2)Lauren M. Saunders; Sanjay R. Srivatsan; Madeleine Duran; et al. 2023. Naturejournal articleCited in: [Virtual Organisms and Digital Biology (passage 1)](organisms/virtual-organisms.html#zebrafish-development-imaging-and-embryo-scale-perturbation); [Virtual Organisms and Digital Biology (passage 2)](organisms/virtual-organisms.html#virtual-organisms-models-datasets-benchmarks)
- [How to build an AI-driven digital organism](https://doi.org/10.1038/s41591-026-04595-0)Le Song; Eran Segal; Eric Xing. 2026. Nature Medicinejournal articleCited in: [Virtual Organisms and Digital Biology](organisms/virtual-organisms.html#virtual-organisms-theoretical)
- [Morpheus: a user-friendly modeling environment for multiscale and multicellular systems biology](https://doi.org/10.1093/bioinformatics/btt772)Jörn Starruß; Walter de Back; Lutz Brusch; et al. 2014. Bioinformaticsjournal articleCited in: [Virtual Organisms and Digital Biology](organisms/virtual-organisms.html#virtual-organisms-models-datasets-benchmarks)
- [TAIR](https://www.arabidopsis.org/)Cited in: [Virtual Organisms and Digital Biology (passage 1)](organisms/virtual-organisms.html#plants-genotype-environment-development-and-field-conditions); [Virtual Organisms and Digital Biology (passage 2)](organisms/virtual-organisms.html#virtual-organisms-models-datasets-benchmarks)
- [Bringing the genetically minimal cell to life on a computer in 4D](https://doi.org/10.1016/j.cell.2026.02.009)Zane R. Thornburg; Andrew Maytin; Jiwoong Kwon; et al. 2026. Celljournal articleCited in: [Virtual Organisms and Digital Biology (passage 1)](organisms/virtual-organisms.html#virtual-organisms-models-datasets-benchmarks); [Virtual Organisms and Digital Biology (passage 2)](organisms/virtual-organisms.html#virtual-organisms-demonstrated)
- [The structure of the nervous system of the nematode Caenorhabditis elegans](https://doi.org/10.1098/rstb.1986.0056)John Graham White; Eileen Southgate; J. N. Thomson; et al. 1986. Philosophical Transactions of the Royal Society of London. B, Biological Sciencesjournal articleCited in: [Virtual Organisms and Digital Biology (passage 1)](organisms/virtual-organisms.html#c.-elegans-anatomy-connectome-development-and-behavior); [Virtual Organisms and Digital Biology (passage 2)](organisms/virtual-organisms.html#virtual-organisms-models-datasets-benchmarks)
- [WormBase](https://wormbase.org/)Cited in: [Virtual Organisms and Digital Biology (passage 1)](organisms/virtual-organisms.html#c.-elegans-anatomy-connectome-development-and-behavior); [Virtual Organisms and Digital Biology (passage 2)](organisms/virtual-organisms.html#virtual-organisms-models-datasets-benchmarks)
- [Yao et al., 2023](https://doi.org/10.1038/s41586-023-06812-z)Cited in: [Virtual Organisms and Digital Biology (passage 1)](organisms/virtual-organisms.html#mouse-mammalian-physiology-knockout-phenotyping-and-brain-atlases); [Virtual Organisms and Digital Biology (passage 2)](organisms/virtual-organisms.html#virtual-organisms-models-datasets-benchmarks)
- [ZFIN](https://zfin.org/)Cited in: [Virtual Organisms and Digital Biology (passage 1)](organisms/virtual-organisms.html#zebrafish-development-imaging-and-embryo-scale-perturbation); [Virtual Organisms and Digital Biology (passage 2)](organisms/virtual-organisms.html#virtual-organisms-models-datasets-benchmarks)

## Part V: Therapeutic Discovery and Translation

### Target Identification and Prioritization

#### References

- [Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery](https://doi.org/10.1093/nar/gkae1128)Annalisa Buniello; Daniel Suveges; Carlos Cruz-Castillo; et al. 2024. Nucleic Acids Researchjournal articleCited in: [Target Identification and Prioritization](therapeutics/targets.html#target-identification-hypothesis)
- [FDA, 2026](https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development)Cited in: [Target Identification and Prioritization](therapeutics/targets.html#target-identification-demonstrated)
- [The druggable genome and support for target identification and validation in drug development](https://doi.org/10.1126/scitranslmed.aag1166)Chris Finan; Anna Gaulton; Felix A. Kruger; et al. 2017. Science Translational Medicinejournal articleCited in: [Target Identification and Prioritization](therapeutics/targets.html#target-identification-introduction)
- [XunZi, an AI biologist, reveals disease-modifying targets](https://doi.org/10.1038/s41551-026-01769-6)Xinhe Huang; Junhong Qin; Fei Tang; et al. 2026. Nature Biomedical Engineeringjournal articleCited in: [Target Identification and Prioritization](therapeutics/targets.html#target-identification-demonstrated)
- [Advancing target discovery through disease-specific integration of multi-modal target identification models and comprehensive benchmarking system](https://doi.org/10.1038/s41598-026-47765-3)Howell Leung; Chengchen Duan; Wenhao Gou; et al. 2026. Scientific Reportsjournal articleCited in: [Target Identification and Prioritization](therapeutics/targets.html#target-identification-demonstrated)
- [Refining the impact of genetic evidence on clinical success](https://doi.org/10.1038/s41586-024-07316-0)Eric Vallabh Minikel; Jeffery L. Painter; Coco Chengliang Dong; et al. 2024. Naturejournal articleCited in: [Target Identification and Prioritization](therapeutics/targets.html#target-identification-direction)
- [The support of human genetic evidence for approved drug indications](https://doi.org/10.1038/ng.3314)Matthew R Nelson; Hannah Tipney; Jeffery L Painter; et al. 2015. Nature Geneticsjournal articleCited in: [Target Identification and Prioritization](therapeutics/targets.html#target-identification-introduction)
- [Open Targets Platform: supporting systematic drug–target identification and prioritisation](https://doi.org/10.1093/nar/gkaa1027)David Ochoa; Andrew Hercules; Miguel Carmona; et al. 2020. Nucleic Acids Researchjournal articleCited in: [Target Identification and Prioritization](therapeutics/targets.html)
- [Ochoa et al., 2021](https://pmc.ncbi.nlm.nih.gov/articles/PMC7779013/)Cited in: [Target Identification and Prioritization (passage 1)](therapeutics/targets.html#target-identification-introduction); [Target Identification and Prioritization (passage 2)](therapeutics/targets.html#target-identification-demonstrated)
- [AutoScreen: AI Co-Scientist System for Target Discovery in Functional Genomics](https://doi.org/10.64898/2026.09.06.749678)Yuanhao Qu; Xufeng Liu; Xiaotong Wang; et al. 2026. bioRxivpreprintCited in: [Target Identification and Prioritization](therapeutics/targets.html#target-identification-demonstrated)
- [Genetic factors associated with reasons for clinical trial stoppage](https://doi.org/10.1038/s41588-024-01854-z)Olesya Razuvayevskaya; Irene Lopez; Ian Dunham; et al. 2024. Nature Geneticsjournal articleCited in: [Target Identification and Prioritization](therapeutics/targets.html#target-identification-safety-tractability)
- [From target discovery to clinical drug development with human genetics](https://doi.org/10.1038/s41586-023-06388-8)Katerina Trajanoska; Claude Bhérer; Daniel Taliun; et al. 2023. Naturejournal articleCited in: [Target Identification and Prioritization](therapeutics/targets.html#target-identification-direction)
- [Defining a Cancer Dependency Map](https://doi.org/10.1016/j.cell.2017.06.010)Aviad Tsherniak; Francisca Vazquez; Phil G. Montgomery; et al. 2017. Celljournal articleCited in: [Target Identification and Prioritization](therapeutics/targets.html#target-identification-demonstrated)

### Small Molecule Generation and ADMET

#### References

- [Ahmad et al., 2022, preprint](https://arxiv.org/abs/2209.01712)Cited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-generative)
- [ApexGO](https://github.com/Yimeng-Zeng/ApexGO)Cited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-antimicrobial)
- [REINVENT 2.0: An AI Tool for De Novo Drug Design](https://doi.org/10.1021/acs.jcim.0c00915)Thomas Blaschke; Josep Arús-Pous; Hongming Chen; et al. 2020. Journal of Chemical Information and Modelingjournal articleCited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-generative)
- [GuacaMol: Benchmarking Models for de Novo Molecular Design](https://doi.org/10.1021/acs.jcim.8b00839)Nathan Brown; Marco Fiscato; Marwin H.S. Segler; et al. 2019. Journal of Chemical Information and Modelingjournal articleCited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-generative)
- [PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences](https://doi.org/10.1039/D3SC04185A)Martin Buttenschoen; Garrett M. Morris; Charlotte M. Deane. 2024. Chemical Sciencejournal articleCited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-docking)
- [CAMPER](https://doi.org/10.5281/zenodo.17781367)Cited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-antimicrobial)
- [Evaluating generative models for structure-based drug discovery from de novo design to lead optimization](https://doi.org/10.1038/s41467-026-78105-8)Duanhua Cao; Zhehuan Fan; Jie Yu; et al. 2026. Nature Communicationsjournal articleCited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-generative)
- [ClinicalTrials.gov, NCT07687459](https://clinicaltrials.gov/study/NCT07687459)Cited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#small-molecules-introduction)
- [Corso et al., 2023](https://openreview.net/forum?id=kKF8_K-mBbS)Cited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-docking)
- [Pretraining a foundation model for small-molecule natural products](https://doi.org/10.1038/s42256-026-01226-8)Yuheng Ding; Bo Qiang; Shaoning Li; et al. 2026. Nature Machine Intelligencejournal articleCited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-generative)
- [Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions](https://doi.org/10.1186/1758-2946-1-8)Peter Ertl; Ansgar Schuffenhauer. 2009. Journal of Cheminformaticsjournal articleCited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-benchmarks)
- [ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support](https://doi.org/10.1093/nar/gkae236)Li Fu; Shaohua Shi; Jiacai Yi; et al. 2024. Nucleic Acids Researchjournal articleCited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-property)
- [Artificial intelligence foundation for therapeutic science](https://doi.org/10.1038/s41589-022-01131-2)Kexin Huang; Tianfan Fu; Wenhao Gao; et al. 2022. Nature Chemical Biologyjournal articleCited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-benchmarks)
- [Chemformer: a pre-trained transformer for computational chemistry](https://doi.org/10.1088/2632-2153/ac3ffb)Ross Irwin; Spyridon Dimitriadis; Jiazhen He; et al. 2022. Machine Learning: Science and Technologyjournal articleCited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-generative)
- [Isomorphic Labs announces strategic multi-target research collaboration with Lilly](https://www.prnewswire.com/news-releases/isomorphic-labs-announces-strategic-multi-target-research-collaboration-with-lilly-302027392.html)Isomorphic Labs. 2024. PR Newswirecompany announcementCited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-clinical)
- [Isomorphic Labs announces strategic multi-target research collaboration with Novartis](https://www.prnewswire.com/news-releases/isomorphic-labs-announces-strategic-multi-target-research-collaboration-with-novartis-302027387.html)Isomorphic Labs. 2024. PR Newswirecompany announcementCited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-clinical)
- [Isomorphic Labs announces \$600 million funding to further develop its next-generation AI drug design engine and advance therapeutic programs into the clinic](https://www.prnewswire.com/news-releases/isomorphic-labs-announces-600-million-funding-to-further-develop-its-next-generation-ai-drug-design-engine-and-advance-therapeutic-programs-into-the-clinic-302415534.html)Isomorphic Labs. 2025. PR Newswirecompany announcementCited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-clinical)
- [Isomorphic Labs announces Novartis collaboration expansion](https://www.isomorphiclabs.com/articles/isomorphic-labs-announces-novartis-collaboration-expansion)Isomorphic Labs. 2025. Isomorphic Labscompany announcementCited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-clinical)
- [Jin et al., 2018](https://proceedings.mlr.press/v80/jin18a.html)Cited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-generative)
- [Lessons Learned in Empirical Scoring with smina from the CSAR 2011 Benchmarking Exercise](https://doi.org/10.1021/ci300604z)David Ryan Koes; Matthew P. Baumgartner; Carlos J. Camacho. 2013. Journal of Chemical Information and Modelingjournal articleCited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-docking)
- [From Benchmark to Bench: Can Agents Survive Real-World Drug Discovery?](https://doi.org/10.64898/2026.09.30.755603)Pierre Llompart; Levent Guner; Helen Lai; et al. 2026. bioRxivpreprintCited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#small-molecules-theoretical)
- [Reinvent 4: Modern AI–driven generative molecule design](https://doi.org/10.1186/s13321-024-00812-5)Hannes H. Loeffler; Jiazhen He; Alessandro Tibo; et al. 2024. Journal of Cheminformaticsjournal articleCited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-generative)
- [Augmenting large language models with chemistry tools](https://doi.org/10.1038/s42256-024-00832-8)Andres M. Bran; Sam Cox; Oliver Schilter; et al. 2024. Nature Machine Intelligencejournal articleCited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#small-molecules-theoretical)
- [Building a new path to make medicines with AI](https://www.isomorphiclabs.com/articles/building-a-new-path-to-make-medicines-with-ai)Max Jaderberg. 2026. Isomorphic Labscompany research postCited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-clinical)
- [Re-evaluating retrosynthesis algorithms with Syntheseus](https://doi.org/10.1039/D4FD00093E)Krzysztof Maziarz; Austin Tripp; Guoqing Liu; et al. 2025. Faraday Discussionsjournal articleCited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-benchmarks)
- [Parallel tempered genetic algorithm guided by deep neural networks for inverse molecular design](https://doi.org/10.1039/d2dd00003b)AkshatKumar Nigam; Robert Pollice; Alán Aspuru-Guzik. 2022. Digital Discoveryjournal articleCited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-generative)
- [Molecular de-novo design through deep reinforcement learning](https://doi.org/10.1186/s13321-017-0235-x)Marcus Olivecrona; Thomas Blaschke; Ola Engkvist; et al. 2017. Journal of Cheminformaticsjournal articleCited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-generative)
- [Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction](https://doi.org/10.1101/2025.06.14.659707)Saro Passaro; Gabriele Corso; Jeremy Wohlwend; et al. 2025preprintCited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#small-molecules-theoretical)
- [Peng et al., 2022](https://proceedings.mlr.press/v162/peng22b.html)Cited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-docking)
- [Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models](https://doi.org/10.3389/fphar.2020.565644)Daniil Polykovskiy; Alexander Zhebrak; Benjamin Sanchez-Lengeling; et al. 2020. Frontiers in Pharmacologyjournal articleCited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-generative)
- [A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models](https://doi.org/10.1038/s41587-024-02143-0)Feng Ren; Alex Aliper; Jian Chen; et al. 2024. Nature Biotechnologyjournal articleCited in: [Small Molecule Generation and ADMET (passage 1)](therapeutics/small-molecules.html#small-molecules-introduction); [Small Molecule Generation and ADMET (passage 2)](therapeutics/small-molecules.html#sec-small-molecules-clinical); [Small Molecule Generation and ADMET (passage 3)](therapeutics/small-molecules.html#small-molecules-practice-notes)
- [CAMPER: mechanistic artificial intelligence for designing peptides that target MRSA persisters](https://doi.org/10.1038/s41467-026-70348-9)Fadi Shehadeh; Biswajit Mishra; Raquel Ferrer-Espada; et al. 2026. Nature Communicationsjournal articleCited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-antimicrobial)
- [Time-Split Cross-Validation as a Method for Estimating the Goodness of Prospective Prediction.](https://doi.org/10.1021/ci400084k)Robert P. Sheridan. 2013. Journal of Chemical Information and Modelingjournal articleCited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-benchmarks)
- [Stärk et al., 2022](https://proceedings.mlr.press/v162/stark22b.html)Cited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-docking)
- [ADMET-AI: a machine learning ADMET platform for evaluation of large-scale chemical libraries](https://doi.org/10.1093/bioinformatics/btae416)Kyle Swanson; Parker Walther; Jeremy Leitz; et al. 2024. Bioinformaticsjournal articleCited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-property)
- [SyntheMol-RL: a flexible reinforcement learning framework for designing easily synthesizable antibiotics](https://doi.org/10.1038/s44320-026-00206-9)Kyle Swanson; Gary Liu; Denise B Catacutan; et al. 2026. Molecular Systems Biologyjournal articleCited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-antimicrobial)
- [SyntheMol-RL](https://github.com/swansonk14/SyntheMol)Cited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-antimicrobial)
- [A generative artificial intelligence approach for peptide antibiotic optimization](https://doi.org/10.1038/s42256-026-01237-5)Marcelo D. T. Torres; Yimeng Zeng; Fangping Wan; et al. 2026. Nature Machine Intelligencejournal articleCited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-antimicrobial)
- [AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading](https://doi.org/10.1002/jcc.21334)Oleg Trott; Arthur J. Olson. 2009. Journal of Computational Chemistryjournal articleCited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-docking)
- [MoleculeNet: a benchmark for molecular machine learning](https://doi.org/10.1039/C7SC02664A)Zhenqin Wu; Bharath Ramsundar; Evan N. Feinberg; et al. 2018. Chemical Sciencejournal articleCited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-benchmarks)
- [A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial](https://doi.org/10.1038/s41591-025-03743-2)Zuojun Xu; Feng Ren; Ping Wang; et al. 2025. Nature Medicinejournal articleCited in: [Small Molecule Generation and ADMET (passage 1)](therapeutics/small-molecules.html#small-molecules-introduction); [Small Molecule Generation and ADMET (passage 2)](therapeutics/small-molecules.html#sec-small-molecules-clinical); [Small Molecule Generation and ADMET (passage 3)](therapeutics/small-molecules.html#small-molecules-practice-notes)
- [Analyzing Learned Molecular Representations for Property Prediction](https://doi.org/10.1021/acs.jcim.9b00237)Kevin Yang; Kyle Swanson; Wengong Jin; et al. 2019. Journal of Chemical Information and Modelingjournal articleCited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-property)
- [Zagribelnyy et al., 2026, preprint](https://arxiv.org/abs/2602.03554)Cited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-benchmarks)
- [Integration of proteomic aging clocks in a phase 2a clinical trial supports simultaneous geroprotective assessment](https://doi.org/10.1038/s41587-026-03286-y)Alex Zhavoronkov; Fedor Galkin; Shan Chen; et al. 2026. Nature Biotechnologyjournal articleCited in: [Small Molecule Generation and ADMET](therapeutics/small-molecules.html#sec-small-molecules-clinical)

### Chemical Biology and Target Engagement

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- [Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes](https://doi.org/10.1038/nprot.2016.105)Mark-Anthony Bray; Shantanu Singh; Han Han; et al. 2016. Nature Protocolsjournal articleCited in: [Chemical Biology and Target Engagement (passage 1)](therapeutics/chemical-biology.html#sec-chemical-biology-models-datasets); [Chemical Biology and Target Engagement (passage 2)](therapeutics/chemical-biology.html#chemical-biology-demonstrated)
- [Generative AI designs functional thiolation domains for reprogramming non-ribosomal peptide synthetases](https://doi.org/10.1038/s41467-026-77963-6)Emre F. Bülbül; Seounggun Bang; Kevin George; et al. 2026. Nature Communicationsjournal articleCited in: [Chemical Biology and Target Engagement](therapeutics/chemical-biology.html#chemical-biology-demonstrated)
- [Quantitative and interface-aware prediction of peptide–protein interactions by VITAL](https://doi.org/10.1038/s42256-026-01291-z)Wei-Hao Chen; Qi-Wen Wang; Zhi-Yi Li; et al. 2026. Nature Machine Intelligencejournal articleCited in: [Chemical Biology and Target Engagement](therapeutics/chemical-biology.html#chemical-biology-demonstrated)
- [Optimizing the Cell Painting assay for image-based profiling](https://doi.org/10.1038/s41596-023-00840-9)Beth A. Cimini; Srinivas Niranj Chandrasekaran; Maria Kost-Alimova; et al. 2023. Nature Protocolsjournal articleCited in: [Chemical Biology and Target Engagement](therapeutics/chemical-biology.html#sec-chemical-biology-models-datasets)
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- [BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology](https://doi.org/10.1093/nar/gkv1072)Michael K. Gilson; Tiqing Liu; Michael Baitaluk; et al. 2015. Nucleic Acids Researchjournal articleCited in: [Chemical Biology and Target Engagement](therapeutics/chemical-biology.html#sec-chemical-biology-models-datasets)
- [PubChem 2023 update](https://doi.org/10.1093/nar/gkac956)Sunghwan Kim; Jie Chen; Tiejun Cheng; et al. 2022. Nucleic Acids Researchjournal articleCited in: [Chemical Biology and Target Engagement](therapeutics/chemical-biology.html#sec-chemical-biology-models-datasets)
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- [Monitoring Drug Target Engagement in Cells and Tissues Using the Cellular Thermal Shift Assay](https://doi.org/10.1126/science.1233606)Daniel Martinez Molina; Rozbeh Jafari; Marina Ignatushchenko; et al. 2013. Sciencejournal articleCited in: [Chemical Biology and Target Engagement (passage 1)](therapeutics/chemical-biology.html#sec-chemical-biology-models-datasets); [Chemical Biology and Target Engagement (passage 2)](therapeutics/chemical-biology.html#chemical-biology-demonstrated)
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- [Protacs: Chimeric molecules that target proteins to the Skp1–Cullin–F box complex for ubiquitination and degradation](https://doi.org/10.1073/pnas.141230798)Kathleen M. Sakamoto; Kyung B. Kim; Akiko Kumagai; et al. 2001. Proceedings of the National Academy of Sciencesjournal articleCited in: [Chemical Biology and Target Engagement (passage 1)](therapeutics/chemical-biology.html#sec-chemical-biology-models-datasets); [Chemical Biology and Target Engagement (passage 2)](therapeutics/chemical-biology.html#chemical-biology-demonstrated)
- [Tracking cancer drugs in living cells by thermal profiling of the proteome](https://doi.org/10.1126/science.1255784)Mikhail M. Savitski; Friedrich B. M. Reinhard; Holger Franken; et al. 2014. Sciencejournal articleCited in: [Chemical Biology and Target Engagement (passage 1)](therapeutics/chemical-biology.html#sec-chemical-biology-models-datasets); [Chemical Biology and Target Engagement (passage 2)](therapeutics/chemical-biology.html#chemical-biology-demonstrated)
- [A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles](https://doi.org/10.1016/j.cell.2017.10.049)Aravind Subramanian; Rajiv Narayan; Steven M. Corsello; et al. 2017. Celljournal articleCited in: [Chemical Biology and Target Engagement (passage 1)](therapeutics/chemical-biology.html#sec-chemical-biology-models-datasets); [Chemical Biology and Target Engagement (passage 2)](therapeutics/chemical-biology.html#chemical-biology-demonstrated)

### Drug Repurposing and Combination Therapy

#### References

- [Building a knowledge graph to enable precision medicine](https://doi.org/10.1038/s41597-023-01960-3)Payal Chandak; Kexin Huang; Marinka Zitnik. 2023. Scientific Datajournal articleCited in: [Drug Repurposing and Combination Therapy](therapeutics/repurposing-combinations.html#repurposing-graphs)
- [Network-based approach to prediction and population-based validation of in silico drug repurposing](https://doi.org/10.1038/s41467-018-05116-5)Feixiong Cheng; Rishi J. Desai; Diane E. Handy; et al. 2018. Nature Communicationsjournal articleCited in: [Drug Repurposing and Combination Therapy](therapeutics/repurposing-combinations.html#repurposing-graphs)
- [Network-based prediction of drug combinations](https://doi.org/10.1038/s41467-019-09186-x)Feixiong Cheng; István A. Kovács; Albert-László Barabási. 2019. Nature Communicationsjournal articleCited in: [Drug Repurposing and Combination Therapy](therapeutics/repurposing-combinations.html#repurposing-synergy)
- [A SARS-CoV-2 protein interaction map reveals targets for drug repurposing](https://doi.org/10.1038/s41586-020-2286-9)David E. Gordon; Gwendolyn M. Jang; Mehdi Bouhaddou; et al. 2020. Naturejournal articleCited in: [Drug Repurposing and Combination Therapy](therapeutics/repurposing-combinations.html#repurposing-covid)
- [Drug screen and machine learning predict neuroprotective agents in a preclinical human model of childhood dementia](https://doi.org/10.1038/s41467-026-76837-1)Zarina Greenberg; Ella McDonald; Alejandra Noreña Puerta; et al. 2026. Nature Communicationsjournal articleCited in: [Drug Repurposing and Combination Therapy](therapeutics/repurposing-combinations.html#repurposing-signature-matching)
- [Network-based in silico drug efficacy screening](https://doi.org/10.1038/ncomms10331)Emre Guney; Jörg Menche; Marc Vidal; et al. 2016. Nature Communicationsjournal articleCited in: [Drug Repurposing and Combination Therapy](therapeutics/repurposing-combinations.html#repurposing-graphs)
- [Systematic integration of biomedical knowledge prioritizes drugs for repurposing](https://doi.org/10.7554/elife.26726)Daniel Scott Himmelstein; Antoine Lizee; Christine Hessler; et al. 2017. eLifejournal articleCited in: [Drug Repurposing and Combination Therapy](therapeutics/repurposing-combinations.html#repurposing-graphs)
- [A foundation model for clinician-centered drug repurposing](https://doi.org/10.1038/s41591-024-03233-x)Kexin Huang; Payal Chandak; Qianwen Wang; et al. 2024. Nature Medicinejournal articleCited in: [Drug Repurposing and Combination Therapy](therapeutics/repurposing-combinations.html#repurposing-graphs)
- [SynergyFinder 2.0: visual analytics of multi-drug combination synergies](https://doi.org/10.1093/nar/gkaa216)Aleksandr Ianevski; Anil K Giri; Tero Aittokallio. 2020. Nucleic Acids Researchjournal articleCited in: [Drug Repurposing and Combination Therapy](therapeutics/repurposing-combinations.html#repurposing-synergy)
- [The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease](https://doi.org/10.1126/science.1132939)Justin Lamb; Emily D. Crawford; David Peck; et al. 2006. Sciencejournal articleCited in: [Drug Repurposing and Combination Therapy](therapeutics/repurposing-combinations.html#repurposing-signature-matching)
- [DeepSynergy: predicting anti-cancer drug synergy with Deep Learning](https://doi.org/10.1093/bioinformatics/btx806)Kristina Preuer; Richard P I Lewis; Sepp Hochreiter; et al. 2017. Bioinformaticsjournal articleCited in: [Drug Repurposing and Combination Therapy](therapeutics/repurposing-combinations.html#repurposing-synergy)
- [Drug repurposing: progress, challenges and recommendations](https://doi.org/10.1038/nrd.2018.168)Sudeep Pushpakom; Francesco Iorio; Patrick A. Eyers; et al. 2018. Nature Reviews Drug Discoveryjournal articleCited in: [Drug Repurposing and Combination Therapy](therapeutics/repurposing-combinations.html#repurposing-graphs)
- [RECOVERY Collaborative Group, 2021](https://doi.org/10.1056/NEJMoa2021436)Cited in: [Drug Repurposing and Combination Therapy](therapeutics/repurposing-combinations.html#repurposing-covid)
- [A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles](https://doi.org/10.1016/j.cell.2017.10.049)Aravind Subramanian; Rajiv Narayan; Steven M. Corsello; et al. 2017. Celljournal articleCited in: [Drug Repurposing and Combination Therapy](therapeutics/repurposing-combinations.html#repurposing-signature-matching)
- [Repurposed Antiviral Drugs for Covid-19 — Interim WHO Solidarity Trial Results](https://doi.org/10.1056/NEJMoa2023184)WHO Solidarity Trial Consortium. 2021. New England Journal of Medicinejournal articleCited in: [Drug Repurposing and Combination Therapy](therapeutics/repurposing-combinations.html#repurposing-covid)

### mRNA, RNA, and Vaccine Design

#### References

- [100 Days Mission](https://cepi.net/100-days-mission)Cited in: [mRNA, RNA, and Vaccine Design](therapeutics/vaccines.html#sec-vaccines-countermeasure-readiness)
- [Accurate structure prediction of biomolecular interactions with AlphaFold 3](https://doi.org/10.1038/s41586-024-07487-w)Josh Abramson; Jonas Adler; Jack Dunger; et al. 2024. Naturejournal articleCited in: [mRNA, RNA, and Vaccine Design (passage 1)](therapeutics/vaccines.html#mrna-rna-vaccine-design-introduction); [mRNA, RNA, and Vaccine Design (passage 2)](therapeutics/vaccines.html#mrna-rna-vaccine-design-demonstrated)
- [AI and the Future of Medical Countermeasures to Protect Against Biological Threats](https://doi.org/10.1093/ofid/ofag108)Amesh A Adalja; Jaspreet Pannu; Thomas V Inglesby. 2026. Open Forum Infectious Diseasesjournal articleCited in: [mRNA, RNA, and Vaccine Design](therapeutics/vaccines.html#sec-vaccines-countermeasure-readiness)
- [Advancing regulatory variant effect prediction with AlphaGenome](https://doi.org/10.1038/s41586-025-10014-0)Žiga Avsec; Natasha Latysheva; Jun Cheng; et al. 2026. Naturejournal articleCited in: [mRNA, RNA, and Vaccine Design](therapeutics/vaccines.html#mrna-rna-vaccine-design-introduction)
- [Orthrus: toward evolutionary and functional RNA foundation models](https://doi.org/10.1038/s41592-026-03064-3)Philip Fradkin; Ruian “Ian” Shi; Taykhoom Dalal; et al. 2026. Nature Methodsjournal articleCited in: [mRNA, RNA, and Vaccine Design (passage 1)](therapeutics/vaccines.html#mrna-rna-vaccine-design-introduction); [mRNA, RNA, and Vaccine Design (passage 2)](therapeutics/vaccines.html#mrna-rna-vaccine-design-demonstrated)
- [good AI practice principles](https://www.fda.gov/about-fda/artificial-intelligence-drug-development/guiding-principles-good-ai-practice-drug-development)Cited in: [mRNA, RNA, and Vaccine Design](therapeutics/vaccines.html#sec-vaccines-countermeasure-readiness)
- [Learning the language of viral evolution and escape](https://doi.org/10.1126/science.abd7331)Brian Hie; Ellen D. Zhong; Bonnie Berger; et al. 2021. Sciencejournal articleCited in: [mRNA, RNA, and Vaccine Design](therapeutics/vaccines.html#mrna-rna-vaccine-design-epitope-escape)
- [Advancing development of medical countermeasures: Incorporating COVID-19 lessons learned into future pandemic preparedness planning](https://doi.org/10.1080/21645515.2022.2129930)Robert A. Johnson; Richard C. White; Gary L. Disbrow. 2022. Human Vaccines & Immunotherapeuticsjournal articleCited in: [mRNA, RNA, and Vaccine Design](therapeutics/vaccines.html#sec-vaccines-countermeasure-readiness)
- [Suppression of RNA Recognition by Toll-like Receptors: The Impact of Nucleoside Modification and the Evolutionary Origin of RNA](https://doi.org/10.1016/j.immuni.2005.06.008)Katalin Karikó; Michael Buckstein; Houping Ni; et al. 2005. Immunityjournal articleCited in: [mRNA, RNA, and Vaccine Design](therapeutics/vaccines.html#mrna-rna-vaccine-design-introduction)
- [Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeutics](https://doi.org/10.1038/s41467-022-28776-w)Kathrin Leppek; Gun Woo Byeon; Wipapat Kladwang; et al. 2022. Nature Communicationsjournal articleCited in: [mRNA, RNA, and Vaccine Design](therapeutics/vaccines.html#mrna-rna-vaccine-design-demonstrated)
- [Sequence modeling and design from molecular to genome scale with Evo](https://doi.org/10.1126/science.ado9336)Eric Nguyen; Michael Poli; Matthew G. Durrant; et al. 2024. Sciencejournal articleCited in: [mRNA, RNA, and Vaccine Design (passage 1)](therapeutics/vaccines.html#mrna-rna-vaccine-design-introduction); [mRNA, RNA, and Vaccine Design (passage 2)](therapeutics/vaccines.html#mrna-rna-vaccine-design-demonstrated)
- [OpenAI, April 2024](https://openai.com/index/moderna/)Cited in: [mRNA, RNA, and Vaccine Design](therapeutics/vaccines.html#mrna-rna-vaccine-design-introduction)
- [Pandemic Preparedness Engine](https://cepi.net/artificial-intelligence/)Cited in: [mRNA, RNA, and Vaccine Design](therapeutics/vaccines.html#sec-vaccines-countermeasure-readiness)
- [mRNA vaccines — a new era in vaccinology](https://doi.org/10.1038/nrd.2017.243)Norbert Pardi; Michael J. Hogan; Frederick W. Porter; et al. 2018. Nature Reviews Drug Discoveryjournal articleCited in: [mRNA, RNA, and Vaccine Design](therapeutics/vaccines.html#mrna-rna-vaccine-design-introduction)
- [NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data](https://doi.org/10.1093/nar/gkaa379)Birkir Reynisson; Bruno Alvarez; Sinu Paul; et al. 2020. Nucleic Acids Researchjournal articleCited in: [mRNA, RNA, and Vaccine Design (passage 1)](therapeutics/vaccines.html#mrna-rna-vaccine-design-epitope-escape); [mRNA, RNA, and Vaccine Design (passage 2)](therapeutics/vaccines.html#mrna-rna-vaccine-design-demonstrated)
- [Human 5′ UTR design and variant effect prediction from a massively parallel translation assay](https://doi.org/10.1038/s41587-019-0164-5)Paul J. Sample; Ban Wang; David W. Reid; et al. 2019. Nature Biotechnologyjournal articleCited in: [mRNA, RNA, and Vaccine Design](therapeutics/vaccines.html#mrna-rna-vaccine-design-demonstrated)
- [Accelerated discovery of thermostable mRNA–lipid nanoparticle vaccines using data-efficient AI](https://doi.org/10.1038/s41587-026-03331-w)Jinbi Tian; Khanh T. M. Tran; Brett H. Pogostin; et al. 2026. Nature Biotechnologyjournal articleCited in: [mRNA, RNA, and Vaccine Design](therapeutics/vaccines.html#mrna-rna-vaccine-design-delivery)
- [Algorithm for optimized mRNA design improves stability and immunogenicity](https://doi.org/10.1038/s41586-023-06127-z)He Zhang; Liang Zhang; Ang Lin; et al. 2023. Naturejournal articleCited in: [mRNA, RNA, and Vaccine Design](therapeutics/vaccines.html#mrna-rna-vaccine-design-construct)

### Cell and Gene Therapy AI

#### References

- [Search-and-replace genome editing without double-strand breaks or donor DNA](https://doi.org/10.1038/s41586-019-1711-4)Andrew V. Anzalone; Peyton B. Randolph; Jessie R. Davis; et al. 2019. Naturejournal articleCited in: [Cell and Gene Therapy AI](therapeutics/cell-gene-therapy.html#sec-cell-gene-therapy-models-datasets)
- [Deep diversification of an AAV capsid protein by machine learning](https://doi.org/10.1038/s41587-020-00793-4)Drew H. Bryant; Ali Bashir; Sam Sinai; et al. 2021. Nature Biotechnologyjournal articleCited in: [Cell and Gene Therapy AI (passage 1)](therapeutics/cell-gene-therapy.html#sec-cell-gene-therapy-models-datasets); [Cell and Gene Therapy AI (passage 2)](therapeutics/cell-gene-therapy.html#cell-gene-therapy-demonstrated)
- [CRISPR-GPT public welcome page](https://github.com/cong-lab/crispr-gpt-pub)Cong Lab. 2026. GitHubwebsiteCited in: [Cell and Gene Therapy AI](therapeutics/cell-gene-therapy.html#cell-gene-therapy-demonstrated)
- [Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9](https://doi.org/10.1038/nbt.3437)John G Doench; Nicolo Fusi; Meagan Sullender; et al. 2016. Nature Biotechnologyjournal articleCited in: [Cell and Gene Therapy AI (passage 1)](therapeutics/cell-gene-therapy.html#sec-cell-gene-therapy-models-datasets); [Cell and Gene Therapy AI (passage 2)](therapeutics/cell-gene-therapy.html#cell-gene-therapy-demonstrated)
- [FDA, 2020](https://www.fda.gov/regulatory-information/search-fda-guidance-documents/long-term-follow-after-administration-human-gene-therapy-products)Cited in: [Cell and Gene Therapy AI](therapeutics/cell-gene-therapy.html#cell-gene-therapy-evidence-change)
- [FDA, 2024](https://www.fda.gov/regulatory-information/search-fda-guidance-documents/considerations-development-chimeric-antigen-receptor-car-t-cell-products)Cited in: [Cell and Gene Therapy AI (passage 1)](therapeutics/cell-gene-therapy.html#sec-cell-gene-therapy-introduction)Recommended in: [Cell and Gene Therapy AI (passage 2)](therapeutics/cell-gene-therapy.html#sec-cell-gene-therapy-related)
- [FDA, 2024](https://www.fda.gov/regulatory-information/search-fda-guidance-documents/human-gene-therapy-products-incorporating-human-genome-editing)Cited in: [Cell and Gene Therapy AI (passage 1)](therapeutics/cell-gene-therapy.html#sec-cell-gene-therapy-introduction)Recommended in: [Cell and Gene Therapy AI (passage 2)](therapeutics/cell-gene-therapy.html#sec-cell-gene-therapy-related)
- [FDA, 2026](https://www.fda.gov/regulatory-information/search-fda-guidance-documents/safety-assessment-genome-editing-human-gene-therapy-products-using-next-generation-sequencing)Cited in: [Cell and Gene Therapy AI (passage 1)](therapeutics/cell-gene-therapy.html#sec-cell-gene-therapy-introduction); [Cell and Gene Therapy AI (passage 2)](therapeutics/cell-gene-therapy.html#sec-cell-gene-therapy-models-datasets); [Cell and Gene Therapy AI (passage 3)](therapeutics/cell-gene-therapy.html#cell-gene-therapy-beyond-current-capabilities)
- [FDA, 2026](https://www.fda.gov/vaccines-blood-biologics/cellular-gene-therapy-products)Cited in: [Cell and Gene Therapy AI (passage 1)](therapeutics/cell-gene-therapy.html#sec-cell-gene-therapy-introduction)Recommended in: [Cell and Gene Therapy AI (passage 2)](therapeutics/cell-gene-therapy.html#sec-cell-gene-therapy-related)
- [Programmable base editing of A•T to G•C in genomic DNA without DNA cleavage](https://doi.org/10.1038/nature24644)Nicole M. Gaudelli; Alexis C. Komor; Holly A. Rees; et al. 2017. Naturejournal articleCited in: [Cell and Gene Therapy AI](therapeutics/cell-gene-therapy.html#sec-cell-gene-therapy-models-datasets)
- [Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR](https://doi.org/10.1186/s13059-016-1012-2)Maximilian Haeussler; Kai Schönig; Hélène Eckert; et al. 2016. Genome Biologyjournal articleCited in: [Cell and Gene Therapy AI (passage 1)](therapeutics/cell-gene-therapy.html#sec-cell-gene-therapy-models-datasets); [Cell and Gene Therapy AI (passage 2)](therapeutics/cell-gene-therapy.html#cell-gene-therapy-demonstrated)
- [Mechanistic machine learning for prediction of prime editing outcomes](https://doi.org/10.1038/s41587-026-03261-7)Alvin Hsu; Peter J. Chen; Angus H. Li; et al. 2026. Nature Biotechnologyjournal articleCited in: [Cell and Gene Therapy AI (passage 1)](therapeutics/cell-gene-therapy.html#sec-cell-gene-therapy-models-datasets); [Cell and Gene Therapy AI (passage 2)](therapeutics/cell-gene-therapy.html#cell-gene-therapy-demonstrated)
- [Programmable editing of a target base in genomic DNA without double-stranded DNA cleavage](https://doi.org/10.1038/nature17946)Alexis C. Komor; Yongjoo B. Kim; Michael S. Packer; et al. 2016. Naturejournal articleCited in: [Cell and Gene Therapy AI](therapeutics/cell-gene-therapy.html#sec-cell-gene-therapy-models-datasets)
- [CHANGE-seq reveals genetic and epigenetic effects on CRISPR–Cas9 genome-wide activity](https://doi.org/10.1038/s41587-020-0555-7)Cicera R. Lazzarotto; Nikolay L. Malinin; Yichao Li; et al. 2020. Nature Biotechnologyjournal articleCited in: [Cell and Gene Therapy AI (passage 1)](therapeutics/cell-gene-therapy.html#sec-cell-gene-therapy-models-datasets); [Cell and Gene Therapy AI (passage 2)](therapeutics/cell-gene-therapy.html#cell-gene-therapy-demonstrated)
- [Precise DNA base editing using AlphaFold3-based contact modelling](https://doi.org/10.1038/s41586-026-10794-z)Haowei Meng; Zhixin Lei; Yongchang Yan; et al. 2026. Naturejournal articleCited in: [Cell and Gene Therapy AI](therapeutics/cell-gene-therapy.html#cell-gene-therapy-demonstrated)
- [Munsamy et al., 2026, preprint](https://doi.org/10.64898/2026.01.12.699009)Cited in: [Cell and Gene Therapy AI](therapeutics/cell-gene-therapy.html#cell-gene-therapy-demonstrated)
- [Comprehensive AAV capsid fitness landscape reveals a viral gene and enables machine-guided design](https://doi.org/10.1126/science.aaw2900)Pierce J. Ogden; Eric D. Kelsic; Sam Sinai; et al. 2019. Sciencejournal articleCited in: [Cell and Gene Therapy AI (passage 1)](therapeutics/cell-gene-therapy.html#sec-cell-gene-therapy-models-datasets); [Cell and Gene Therapy AI (passage 2)](therapeutics/cell-gene-therapy.html#cell-gene-therapy-demonstrated)
- [AutoScreen: AI Co-Scientist System for Target Discovery in Functional Genomics](https://doi.org/10.64898/2026.09.06.749678)Yuanhao Qu; Xufeng Liu; Xiaotong Wang; et al. 2026. bioRxivpreprintCited in: [Cell and Gene Therapy AI](therapeutics/cell-gene-therapy.html#cell-gene-therapy-demonstrated)
- [GUIDE-seq enables genome-wide profiling of off-target cleavage by CRISPR-Cas nucleases](https://doi.org/10.1038/nbt.3117)Shengdar Q Tsai; Zongli Zheng; Nhu T Nguyen; et al. 2014. Nature Biotechnologyjournal articleCited in: [Cell and Gene Therapy AI (passage 1)](therapeutics/cell-gene-therapy.html#sec-cell-gene-therapy-models-datasets); [Cell and Gene Therapy AI (passage 2)](therapeutics/cell-gene-therapy.html#cell-gene-therapy-demonstrated)
- [CIRCLE-seq: a highly sensitive in vitro screen for genome-wide CRISPR–Cas9 nuclease off-targets](https://doi.org/10.1038/nmeth.4278)Shengdar Q Tsai; Nhu T Nguyen; Jose Malagon-Lopez; et al. 2017. Nature Methodsjournal articleCited in: [Cell and Gene Therapy AI (passage 1)](therapeutics/cell-gene-therapy.html#sec-cell-gene-therapy-models-datasets); [Cell and Gene Therapy AI (passage 2)](therapeutics/cell-gene-therapy.html#cell-gene-therapy-demonstrated)
- [CRISPR-GPT for agentic automation of gene-editing experiments](https://doi.org/10.1038/s41551-025-01463-z)Yuanhao Qu; Kaixuan Huang; Ming Yin; Kanghong Zhan; Dyllan Liu; Di Yin; Henry C. Cousins; William A. Johnson; Xiaotong Wang; Mihir Shah; Russ B. Altman; Denny Zhou; Mengdi Wang; Le Cong. 2025. Nature Biomedical Engineeringjournal articleCited in: [Cell and Gene Therapy AI](therapeutics/cell-gene-therapy.html#cell-gene-therapy-demonstrated)

### Diagnostics and Biomarker Translation

#### References

- [STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies](https://doi.org/10.1136/bmj.h5527)Patrick M Bossuyt; Johannes B Reitsma; David E Bruns; et al. 2015. BMJjournal articleCited in: [Diagnostics and Biomarker Translation](therapeutics/diagnostics-biomarkers.html#sec-diagnostics-biomarkers-models-datasets)
- [Detection and localization of surgically resectable cancers with a multi-analyte blood test](https://doi.org/10.1126/science.aar3247)Joshua D. Cohen; Lu Li; Yuxuan Wang; et al. 2018. Sciencejournal articleCited in: [Diagnostics and Biomarker Translation (passage 1)](therapeutics/diagnostics-biomarkers.html#sec-diagnostics-biomarkers-models-datasets); [Diagnostics and Biomarker Translation (passage 2)](therapeutics/diagnostics-biomarkers.html#diagnostics-biomarkers-demonstrated)
- [Genome-wide cell-free DNA fragmentation in patients with cancer](https://doi.org/10.1038/s41586-019-1272-6)Stephen Cristiano; Alessandro Leal; Jillian Phallen; et al. 2019. Naturejournal articleCited in: [Diagnostics and Biomarker Translation (passage 1)](therapeutics/diagnostics-biomarkers.html#sec-diagnostics-biomarkers-models-datasets); [Diagnostics and Biomarker Translation (passage 2)](therapeutics/diagnostics-biomarkers.html#diagnostics-biomarkers-demonstrated)
- [Dermatologist-level classification of skin cancer with deep neural networks](https://doi.org/10.1038/nature21056)Andre Esteva; Brett Kuprel; Roberto A. Novoa; et al. 2017. Naturejournal articleCited in: [Diagnostics and Biomarker Translation](therapeutics/diagnostics-biomarkers.html#diagnostics-biomarkers-demonstrated)
- [FDA, 2023](https://www.fda.gov/medical-devices/in-vitro-diagnostics/companion-diagnostics)Cited in: [Diagnostics and Biomarker Translation (passage 1)](therapeutics/diagnostics-biomarkers.html#diagnostics-biomarkers-demonstrated); [Diagnostics and Biomarker Translation (passage 2)](therapeutics/diagnostics-biomarkers.html#sec-diagnostics-biomarkers-failure-modes)Recommended in: [Diagnostics and Biomarker Translation (passage 3)](therapeutics/diagnostics-biomarkers.html#sec-diagnostics-biomarkers-related)
- [FDA, 2026](https://www.fda.gov/drugs/biomarker-qualification-program/qualifying-biomarker-through-biomarker-qualification-program)Cited in: [Diagnostics and Biomarker Translation (passage 1)](therapeutics/diagnostics-biomarkers.html#sec-diagnostics-biomarkers-introduction)Recommended in: [Diagnostics and Biomarker Translation (passage 2)](therapeutics/diagnostics-biomarkers.html#sec-diagnostics-biomarkers-related)
- [FDA, 2026](https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-enabled-medical-devices)Cited in: [Diagnostics and Biomarker Translation (passage 1)](therapeutics/diagnostics-biomarkers.html#sec-diagnostics-biomarkers-models-datasets); [Diagnostics and Biomarker Translation (passage 2)](therapeutics/diagnostics-biomarkers.html#diagnostics-biomarkers-demonstrated)Recommended in: [Diagnostics and Biomarker Translation (passage 3)](therapeutics/diagnostics-biomarkers.html#sec-diagnostics-biomarkers-related)
- [FDA-NIH Biomarker Working Group, 2025](https://www.ncbi.nlm.nih.gov/books/NBK326791/)Cited in: [Diagnostics and Biomarker Translation](therapeutics/diagnostics-biomarkers.html#sec-diagnostics-biomarkers-introduction)
- [HTAN, 2026](https://humantumoratlas.org/)Cited in: [Diagnostics and Biomarker Translation](therapeutics/diagnostics-biomarkers.html#sec-diagnostics-biomarkers-models-datasets)
- [Clinical validation of a targeted methylation-based multi-cancer early detection test using an independent validation set](https://doi.org/10.1016/j.annonc.2021.05.806)E.A. Klein; D. Richards; A. Cohn; et al. 2021. Annals of Oncologyjournal articleCited in: [Diagnostics and Biomarker Translation (passage 1)](therapeutics/diagnostics-biomarkers.html#sec-diagnostics-biomarkers-models-datasets); [Diagnostics and Biomarker Translation (passage 2)](therapeutics/diagnostics-biomarkers.html#diagnostics-biomarkers-demonstrated)
- [Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK)](https://doi.org/10.1093/jnci/dji237)Lisa M. McShane; Douglas G. Altman; Willi Sauerbrei; et al. 2005. JNCI: Journal of the National Cancer Institutejournal articleCited in: [Diagnostics and Biomarker Translation](therapeutics/diagnostics-biomarkers.html#sec-diagnostics-biomarkers-models-datasets)
- [NCI OCCPR, 2026](https://proteomics.cancer.gov/programs/cptac)Cited in: [Diagnostics and Biomarker Translation](therapeutics/diagnostics-biomarkers.html#sec-diagnostics-biomarkers-models-datasets)
- [NCI TCGA, 2026](https://www.cancer.gov/ccg/research/genome-sequencing/tcga)Cited in: [Diagnostics and Biomarker Translation](therapeutics/diagnostics-biomarkers.html#sec-diagnostics-biomarkers-models-datasets)

### Clinical Trial AI for Translational Research

#### References

- [Real-world validation of a multimodal LLM-powered pipeline for high-accuracy clinical trial patient matching](https://doi.org/10.1038/s43856-025-01256-0)Anatole Callies; Quentin Bodinier; Philippe Ravaud; et al. 2025. Communications Medicinejournal articleCited in: [Clinical Trial AI for Translational Research](therapeutics/clinical-trials.html#clinical-trial-matching-evidence)
- [ClinicalTrials.gov, NCT05279755](https://clinicaltrials.gov/study/NCT05279755)Cited in: [Clinical Trial AI for Translational Research](therapeutics/clinical-trials.html#clinical-trial-matching-evidence)
- [Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension](https://doi.org/10.1038/s41591-020-1037-7)Samantha Cruz Rivera; Xiaoxuan Liu; An-Wen Chan; et al. 2020. Nature Medicinejournal articleCited in: [Clinical Trial AI for Translational Research](therapeutics/clinical-trials.html#clinical-trial-ai-introduction)
- [EMA, 2024](https://www.ema.europa.eu/en/use-artificial-intelligence-ai-medicinal-product-lifecycle)Cited in: [Clinical Trial AI for Translational Research (passage 1)](therapeutics/clinical-trials.html#clinical-trial-ai-introduction); [Clinical Trial AI for Translational Research (passage 2)](therapeutics/clinical-trials.html#clinical-trial-ai-demonstrated)
- [FDA, 2026](https://www.fda.gov/industry/fda-actions-accelerate-and-modernize-early-and-late-stage-clinical-development)Cited in: [Clinical Trial AI for Translational Research](therapeutics/clinical-trials.html#clinical-trial-latency-ai-era)
- [FDA, 2026](https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development)Cited in: [Clinical Trial AI for Translational Research (passage 1)](therapeutics/clinical-trials.html#clinical-trial-ai-introduction); [Clinical Trial AI for Translational Research (passage 2)](therapeutics/clinical-trials.html#clinical-trial-ai-demonstrated)
- [FDA, 2026](https://www.federalregister.gov/documents/2026/04/29/2026-08281/ai-enabled-optimization-of-early-phase-clinical-trials-pilot-program-request-for-information)Cited in: [Clinical Trial AI for Translational Research](therapeutics/clinical-trials.html#clinical-trial-ai-introduction)
- [HINT: Hierarchical interaction network for clinical-trial-outcome predictions](https://doi.org/10.1016/j.patter.2022.100445)Tianfan Fu; Kexin Huang; Cao Xiao; et al. 2022. Patternsjournal articleCited in: [Clinical Trial AI for Translational Research](therapeutics/clinical-trials.html#clinical-trial-outcome-prediction)
- [A large-scale database for clinical trial outcomes and features](https://doi.org/10.1038/s44360-026-00081-6)Chufan Gao; Jathurshan Pradeepkumar; Trisha Das; et al. 2026. Nature Healthjournal articleCited in: [Clinical Trial AI for Translational Research](therapeutics/clinical-trials.html#clinical-trial-outcome-prediction)
- [A prospective pragmatic evaluation of automatic trial matching tools in a molecular tumor board](https://doi.org/10.1038/s41698-025-00806-y)Lilia Gueguen; Louise Olgiati; Clément Brutti-Mairesse; et al. 2025. npj Precision Oncologyjournal articleCited in: [Clinical Trial AI for Translational Research](therapeutics/clinical-trials.html#clinical-trial-matching-evidence)
- [HHS, 2026](https://www.hhs.gov/press-room/hhs-launches-clinical-trials-reform-initiative.html)Cited in: [Clinical Trial AI for Translational Research](therapeutics/clinical-trials.html#clinical-trial-latency-ai-era)
- [Matching patients to clinical trials with large language models](https://doi.org/10.1038/s41467-024-53081-z)Qiao Jin; Zifeng Wang; Charalampos S. Floudas; et al. 2024. Nature Communicationsjournal articleCited in: [Clinical Trial AI for Translational Research](therapeutics/clinical-trials.html#clinical-trial-matching-evidence)
- [Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension](https://doi.org/10.1038/s41591-020-1034-x)Xiaoxuan Liu; Samantha Cruz Rivera; David Moher; et al. 2020. Nature Medicinejournal articleCited in: [Clinical Trial AI for Translational Research](therapeutics/clinical-trials.html#clinical-trial-ai-introduction)
- [Clinical Trial Notifications Triggered by Artificial Intelligence–Detected Cancer Progression](https://doi.org/10.1001/jamanetworkopen.2025.2013)Tali Mazor; Karim S. Farhat; Pavel Trukhanov; et al. 2025. JAMA Network Openjournal articleCited in: [Clinical Trial AI for Translational Research](therapeutics/clinical-trials.html#clinical-trial-matching-evidence)
- [Adaptive designs in clinical trials: why use them, and how to run and report them](https://doi.org/10.1186/s12916-018-1017-7)Philip Pallmann; Alun W. Bedding; Babak Choodari-Oskooei; et al. 2018. BMC Medicinejournal articleCited in: [Clinical Trial AI for Translational Research](therapeutics/clinical-trials.html#clinical-trial-matching-evidence)
- [Enriching Clinical Trials With Machine Learning](https://doi.org/10.1001/jama.2026.13579)Roy Perlis. 2026. JAMAjournal articleCited in: [Clinical Trial AI for Translational Research](therapeutics/clinical-trials.html#clinical-trial-matching-evidence)
- [ProJenX, September 2024](https://projenx.com/projenx-and-unlearn-announce-partnership-to-augment-als-clinical-trial-pro-101-with-digital-twin-model/)Cited in: [Clinical Trial AI for Translational Research](therapeutics/clinical-trials.html#clinical-trial-matching-evidence)
- [Translating evidence into practice: adapting TrialGPT for real-world clinical trial eligibility screening](https://doi.org/10.1093/jamia/ocag006)Mahanazuddin Syed; Muayad Hamidi; Manju Bikkanuri; et al. 2026. Journal of the American Medical Informatics Associationjournal articleCited in: [Clinical Trial AI for Translational Research](therapeutics/clinical-trials.html#clinical-trial-matching-evidence)
- [Biotech's Lost Archive](https://ifp.org/biotechs-lost-archive/)Teslo. 2025. Institute for ProgressCited in: [Clinical Trial AI for Translational Research](therapeutics/clinical-trials.html#clinical-trial-ai-introduction)
- [Teslo, 2026](https://www.nytimes.com/2026/09/04/opinion/clinical-trials-drugs-science.html)Cited in: [Clinical Trial AI for Translational Research](therapeutics/clinical-trials.html#clinical-trial-latency-ai-era)
- [TGA, 2024](https://www.tga.gov.au/resources/guidance/australian-clinical-trial-handbook)Cited in: [Clinical Trial AI for Translational Research](therapeutics/clinical-trials.html#clinical-trial-latency-ai-era)
- [Manual vs AI-Assisted Prescreening for Trial Eligibility Using Large Language Models—A Randomized Clinical Trial](https://doi.org/10.1001/jama.2024.28047)Ozan Unlu; Matthew Varugheese; Jiyeon Shin; et al. 2025. JAMAjournal articleCited in: [Clinical Trial AI for Translational Research](therapeutics/clinical-trials.html#clinical-trial-matching-evidence)
- [FDA Perspective on the Regulation of Artificial Intelligence in Health Care and Biomedicine](https://doi.org/10.1001/jama.2024.21451)Haider J. Warraich; Troy Tazbaz; Robert M. Califf. 2025. JAMAjournal articleCited in: [Clinical Trial AI for Translational Research](therapeutics/clinical-trials.html#clinical-trial-ai-introduction)
- [Artificial intelligence in drug development](https://doi.org/10.1038/s41591-024-03434-4)Kang Zhang; Xin Yang; Yifei Wang; et al. 2025. Nature Medicinejournal articleCited in: [Clinical Trial AI for Translational Research](therapeutics/clinical-trials.html#clinical-trial-ai-demonstrated)
- [The Virtual Biotech: A multi-agent AI framework for therapeutic discovery and development](https://doi.org/10.1126/science.aeg6779)Harrison G. Zhang; Peter Eckmann; Jiacheng Miao; Andrew B. Mahon; James Zou. 2026. Sciencejournal articleCited in: [Clinical Trial AI for Translational Research](therapeutics/clinical-trials.html#clinical-trial-outcome-prediction)

### Real-World Evidence and Biomarker AI

#### References

- [Biomarkers Definitions Working Group, 2001](https://doi.org/10.1067/mcp.2001.113989)Cited in: [Real-World Evidence and Biomarker AI](therapeutics/real-world-evidence.html#rwe-biomarkers)
- [Real-World Evidence and Real-World Data for Evaluating Drug Safety and Effectiveness](https://doi.org/10.1001/jama.2018.10136)Jacqueline Corrigan-Curay; Leonard Sacks; Janet Woodcock. 2018. JAMAjournal articleCited in: [Real-World Evidence and Biomarker AI](therapeutics/real-world-evidence.html#rwe-introduction)
- [FDA, 2018](https://www.fda.gov/media/120060/download)Cited in: [Real-World Evidence and Biomarker AI (passage 1)](therapeutics/real-world-evidence.html#rwe-introduction); [Real-World Evidence and Biomarker AI (passage 2)](therapeutics/real-world-evidence.html#rwe-data-curation)
- [FDA, 2026](https://www.fda.gov/drugs/biomarker-qualification-program/about-biomarkers-and-qualification)Cited in: [Real-World Evidence and Biomarker AI](therapeutics/real-world-evidence.html#rwe-biomarkers)
- [FDA, 2026](https://www.fda.gov/drugs/biomarker-qualification-program/qualifying-biomarker-through-biomarker-qualification-program)Cited in: [Real-World Evidence and Biomarker AI](therapeutics/real-world-evidence.html#rwe-biomarkers)
- [FDA, 2026](https://www.fda.gov/science-research/science-and-research-special-topics/real-world-evidence)Cited in: [Real-World Evidence and Biomarker AI (passage 1)](therapeutics/real-world-evidence.html#rwe-introduction); [Real-World Evidence and Biomarker AI (passage 2)](therapeutics/real-world-evidence.html#rwe-data-curation)
- [Biomarkers and surrogate endpoints in clinical trials](https://doi.org/10.1002/sim.5403)Thomas R. Fleming; John H. Powers. 2012. Statistics in Medicinejournal articleCited in: [Real-World Evidence and Biomarker AI](therapeutics/real-world-evidence.html#rwe-biomarkers)
- [When and How Can Real World Data Analyses Substitute for Randomized Controlled Trials?](https://doi.org/10.1002/cpt.857)Jessica M. Franklin; Sebastian Schneeweiss. 2017. Clinical Pharmacology & Therapeuticsjournal articleCited in: [Real-World Evidence and Biomarker AI](therapeutics/real-world-evidence.html#rwe-data-curation)
- [Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available: Table 1.](https://doi.org/10.1093/aje/kwv254)Miguel A. Hernán; James M. Robins. 2016. American Journal of Epidemiologyjournal articleCited in: [Real-World Evidence and Biomarker AI](therapeutics/real-world-evidence.html#rwe-introduction)
- [Target Trial Emulation](https://doi.org/10.1001/jama.2022.21383)Miguel A. Hernán; Wei Wang; David E. Leaf. 2022. JAMAjournal articleCited in: [Real-World Evidence and Biomarker AI](therapeutics/real-world-evidence.html#rwe-target-trial-protocol)
- [“Target Trial Emulation” for Observational Studies — Potential and Pitfalls](https://doi.org/10.1056/NEJMp2407586)Rebecca A. Hubbard; Constantine A. Gatsonis; Joseph W. Hogan; et al. 2024. New England Journal of Medicinejournal articleCited in: [Real-World Evidence and Biomarker AI](therapeutics/real-world-evidence.html#rwe-target-trial-protocol)
- [Real-World Evidence — What Is It and What Can It Tell Us?](https://doi.org/10.1056/NEJMsb1609216)Rachel E. Sherman; Steven A. Anderson; Gerald J. Dal Pan; et al. 2016. New England Journal of Medicinejournal articleCited in: [Real-World Evidence and Biomarker AI](therapeutics/real-world-evidence.html#rwe-introduction)
- [Tempus AI, 2024](https://www.sec.gov/Archives/edgar/data/1717115/0001193125-24-142956-index.htm)Cited in: [Real-World Evidence and Biomarker AI](therapeutics/real-world-evidence.html#rwe-platforms)
- [High-throughput target trial emulation for Alzheimer’s disease drug repurposing with real-world data](https://doi.org/10.1038/s41467-023-43929-1)Chengxi Zang; Hao Zhang; Jie Xu; et al. 2023. Nature Communicationsjournal articleCited in: [Real-World Evidence and Biomarker AI](therapeutics/real-world-evidence.html#rwe-external-control-bias)

### Translational Evidence and Failure Modes

#### References

- [Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines](https://doi.org/10.1038/s41592-025-02772-6)Constantin Ahlmann-Eltze; Wolfgang Huber; Simon Anders. 2025. Nature Methodsjournal articleCited in: [Translational Evidence and Failure Modes (passage 1)](therapeutics/translation-failures.html#translational-evidence-failure-modes-introduction); [Translational Evidence and Failure Modes (passage 2)](therapeutics/translation-failures.html#translational-evidence-failure-modes-demonstrated)
- [PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences](https://doi.org/10.1039/D3SC04185A)Martin Buttenschoen; Garrett M. Morris; Charlotte M. Deane. 2024. Chemical Sciencejournal articleCited in: [Translational Evidence and Failure Modes (passage 1)](therapeutics/translation-failures.html#translational-evidence-failure-modes-introduction); [Translational Evidence and Failure Modes (passage 2)](therapeutics/translation-failures.html#translational-evidence-failure-modes-demonstrated)
- [EMA, 2024](https://www.ema.europa.eu/en/use-artificial-intelligence-ai-medicinal-product-lifecycle)Cited in: [Translational Evidence and Failure Modes](therapeutics/translation-failures.html#translational-evidence-failure-modes-demonstrated)
- [Biomarkers and surrogate endpoints in clinical trials](https://doi.org/10.1002/sim.5403)Thomas R. Fleming; John H. Powers. 2012. Statistics in Medicinejournal articleCited in: [Translational Evidence and Failure Modes](therapeutics/translation-failures.html#translational-evidence-failure-modes-beyond-current-capabilities)
- [good AI practice principles](https://www.fda.gov/about-fda/artificial-intelligence-drug-development/guiding-principles-good-ai-practice-drug-development)Cited in: [Translational Evidence and Failure Modes](therapeutics/translation-failures.html#translational-evidence-failure-modes-demonstrated)
- [Guerra and Kraus, 2026](https://www.bvp.com/atlas/the-2026-healthcare-ai-roi-scorecard)Cited in: [Translational Evidence and Failure Modes](therapeutics/translation-failures.html#translational-evidence-failure-modes-stage)
- [Clinical development success rates for investigational drugs](https://doi.org/10.1038/nbt.2786)Michael Hay; David W Thomas; John L Craighead; et al. 2014. Nature Biotechnologyjournal articleCited in: [Translational Evidence and Failure Modes](therapeutics/translation-failures.html#translational-evidence-failure-modes-introduction)
- [Diagnosing the decline in pharmaceutical R&D efficiency](https://doi.org/10.1038/nrd3681)Jack W. Scannell; Alex Blanckley; Helen Boldon; et al. 2012. Nature Reviews Drug Discoveryjournal articleCited in: [Translational Evidence and Failure Modes](therapeutics/translation-failures.html#translational-evidence-failure-modes-introduction)
- [Decision Curve Analysis: A Novel Method for Evaluating Prediction Models](https://doi.org/10.1177/0272989X06295361)Andrew J. Vickers; Elena B. Elkin. 2006. Medical Decision Makingjournal articleCited in: [Translational Evidence and Failure Modes](therapeutics/translation-failures.html#translational-evidence-failure-modes-demonstrated)
- [An analysis of the attrition of drug candidates from four major pharmaceutical companies](https://doi.org/10.1038/nrd4609)Michael J. Waring; John Arrowsmith; Andrew R. Leach; et al. 2015. Nature Reviews Drug Discoveryjournal articleCited in: [Translational Evidence and Failure Modes](therapeutics/translation-failures.html#translational-evidence-failure-modes-introduction)
- [MoleculeNet: a benchmark for molecular machine learning](https://doi.org/10.1039/C7SC02664A)Zhenqin Wu; Bharath Ramsundar; Evan N. Feinberg; et al. 2018. Chemical Sciencejournal articleCited in: [Translational Evidence and Failure Modes](therapeutics/translation-failures.html#translational-evidence-failure-modes-demonstrated)
- [Artificial intelligence in drug development](https://doi.org/10.1038/s41591-024-03434-4)Kang Zhang; Xin Yang; Yifei Wang; et al. 2025. Nature Medicinejournal articleCited in: [Translational Evidence and Failure Modes](therapeutics/translation-failures.html#translational-evidence-failure-modes-introduction)

## Part VI: Research Systems and Automation

### Self-Driving Laboratories

#### References

- [Will AI ever win its own Nobel? Some predict a prize-worthy science discovery soon](https://doi.org/10.1038/d41586-025-03223-0)Jenna Ahart. 2025. Naturenews featureCited in: [Self-Driving Laboratories](automation/self-driving-labs.html#sec-sdl-nobel-turing)
- [Anthropic, 2026](https://www.anthropic.com/news/model-hardware-standard-research-preview)Cited in: [Self-Driving Laboratories](automation/self-driving-labs.html#sec-sdl-chemistry)
- [What Does "Autonomous" Actually Mean in a Wet Lab?](https://stephbatalis.substack.com/p/what-does-autonomous-actually-mean)Steph Batalis. 2026. Substack — Notes on AI & BiologyessayCited in: [Self-Driving Laboratories](automation/self-driving-labs.html#sec-sdl-classification)
- [Autonomous chemical research with large language models](https://doi.org/10.1038/s41586-023-06792-0)Daniil A. Boiko; Robert MacKnight; Ben Kline; et al. 2023. Naturejournal articleCited in: [Self-Driving Laboratories (passage 1)](automation/self-driving-labs.html); [Self-Driving Laboratories (passage 2)](automation/self-driving-labs.html#sdl-introduction); [Self-Driving Laboratories (passage 3)](automation/self-driving-labs.html#sec-sdl-chemistry)
- [A mobile robotic chemist](https://doi.org/10.1038/s41586-020-2442-2)Benjamin Burger; Phillip M. Maffettone; Vladimir V. Gusev; et al. 2020. Naturejournal articleCited in: [Self-Driving Laboratories (passage 1)](automation/self-driving-labs.html#sdl-introduction); [Self-Driving Laboratories (passage 2)](automation/self-driving-labs.html#sec-sdl-chemistry)
- [The past, present and future of self-driving laboratories](https://doi.org/10.1038/s41570-026-00847-2)Richard B. Canty; Milad Abolhasani. 2026. Nature Reviews Chemistryjournal articleCited in: [Self-Driving Laboratories (passage 1)](automation/self-driving-labs.html#sec-sdl-chemistry); [Self-Driving Laboratories (passage 2)](automation/self-driving-labs.html#sec-sdl-anchors)
- [Closing the Loop in AI-Driven Biomedical Discovery](https://doi.org/10.20944/preprints202608.2107.v1)Ada Fang; Kevin Li; Ayush Noori; et al. 2026preprintCited in: [Self-Driving Laboratories](automation/self-driving-labs.html#sdl-theoretical)
- [A multi-agent system for automating scientific discovery](https://doi.org/10.1038/s41586-026-10652-y)Ali E. Ghareeb; Benjamin Chang; Ludovico Mitchener; et al. 2026. Naturejournal articleCited in: [Self-Driving Laboratories (passage 1)](automation/self-driving-labs.html); [Self-Driving Laboratories (passage 2)](automation/self-driving-labs.html#sdl-introduction); [Self-Driving Laboratories (passage 3)](automation/self-driving-labs.html#sec-sdl-biology)
- [Toward autonomous science with agentic artificial intelligence](https://doi.org/10.1016/j.cell.2026.08.052)Lucie Y. Guo; Darren S.J. Ting; Xinyi Su; Alex Aliper; Alex Zhavoronkov; Daniel S.W. Ting. 2026. Celljournal articleCited in: [Self-Driving Laboratories](automation/self-driving-labs.html#sdl-theoretical)
- [Häse et al., 2019](https://doi.org/10.1016/j.trechm.2019.02.007)Cited in: [Self-Driving Laboratories](automation/self-driving-labs.html#sdl-practice-notes)
- [Anthropic quietly sets up biology lab as it ramps AI drug program](https://www.reuters.com/world/anthropic-quietly-sets-up-biology-lab-it-ramps-ai-drug-program-2026-09-18/)Jeffrey Dastin and Michael Erman. 2026. Reutersnews articleCited in: [Self-Driving Laboratories](automation/self-driving-labs.html#sec-sdl-chemistry)
- [The Automation of Science](https://doi.org/10.1126/science.1165620)Ross D. King; Jem Rowland; Stephen G. Oliver; et al. 2009. Sciencejournal articleCited in: [Self-Driving Laboratories (passage 1)](automation/self-driving-labs.html#sdl-introduction); [Self-Driving Laboratories (passage 2)](automation/self-driving-labs.html#sec-sdl-history)
- [Nobel Turing Challenge: creating the engine for scientific discovery](https://doi.org/10.1038/s41540-021-00189-3)Hiroaki Kitano. 2021. npj Systems Biology and Applicationsjournal articleCited in: [Self-Driving Laboratories](automation/self-driving-labs.html#sec-sdl-nobel-turing)
- [Challenges in High-Throughput Inorganic Materials Prediction and Autonomous Synthesis](https://doi.org/10.1103/PRXEnergy.3.011002)Josh Leeman; Yuhan Liu; Joseph Stiles; et al. 2024. PRX Energyjournal articleCited in: [Self-Driving Laboratories (passage 1)](automation/self-driving-labs.html); [Self-Driving Laboratories (passage 2)](automation/self-driving-labs.html#sdl-introduction); [Self-Driving Laboratories (passage 3)](automation/self-driving-labs.html#sec-sdl-alab)
- [Augmenting large language models with chemistry tools](https://doi.org/10.1038/s42256-024-00832-8)Andres M. Bran; Sam Cox; Oliver Schilter; et al. 2024. Nature Machine Intelligencejournal articleCited in: [Self-Driving Laboratories](automation/self-driving-labs.html#sec-sdl-chemcrow)
- [Self-driving laboratory for accelerated discovery of thin-film materials](https://doi.org/10.1126/sciadv.aaz8867)B. P. MacLeod; F. G. L. Parlane; T. D. Morrissey; et al. 2020. Science Advancesjournal articleCited in: [Self-Driving Laboratories (passage 1)](automation/self-driving-labs.html#sdl-introduction); [Self-Driving Laboratories (passage 2)](automation/self-driving-labs.html#sec-sdl-chemistry)
- [Should We Teach AI a Better Scientific Method?](https://doi.org/10.1021/acs.chemmater.3c03078)James R. Neilson. 2023. Chemistry of Materialsjournal articleCited in: [Self-Driving Laboratories (passage 1)](automation/self-driving-labs.html); [Self-Driving Laboratories (passage 2)](automation/self-driving-labs.html#sdl-introduction); [Self-Driving Laboratories (passage 3)](automation/self-driving-labs.html#sec-sdl-alab)
- [AutoLabs: cognitive multi-agent systems with self-correction for autonomous chemical experimentation](https://doi.org/10.1038/s41598-026-45593-z)Gihan Panapitiya; Emily Saldanha; Heather Job; et al. 2026. Scientific Reportsjournal articleCited in: [Self-Driving Laboratories](automation/self-driving-labs.html#sec-sdl-chemistry)
- [Large language models as uncertainty-calibrated optimizers for experimental discovery](https://doi.org/10.1038/s42256-026-01283-z)Bojana Ranković; Ryan-Rhys Griffiths; Philippe Schwaller. 2026. Nature Machine Intelligencejournal articleCited in: [Self-Driving Laboratories](automation/self-driving-labs.html#sec-sdl-chemcrow)
- [ChemOS: An orchestration software to democratize autonomous discovery](https://doi.org/10.1371/journal.pone.0229862)Loïc M. Roch; Florian Häse; Christoph Kreisbeck; et al. 2020. PLOS ONEjournal articleCited in: [Self-Driving Laboratories (passage 1)](automation/self-driving-labs.html#sdl-introduction); [Self-Driving Laboratories (passage 2)](automation/self-driving-labs.html#sec-sdl-chemistry)
- [Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab](https://doi.org/10.1021/acs.accounts.2c00220)Martin Seifrid; Robert Pollice; Andrés Aguilar-Granda; et al. 2022. Accounts of Chemical Researchjournal articleCited in: [Self-Driving Laboratories](automation/self-driving-labs.html#sec-sdl-chemistry)
- [The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodies](https://doi.org/10.1038/s41586-025-09442-9)Kyle Swanson; Wesley Wu; Nash L. Bulaong; et al. 2025. Naturejournal articleCited in: [Self-Driving Laboratories (passage 1)](automation/self-driving-labs.html); [Self-Driving Laboratories (passage 2)](automation/self-driving-labs.html#sdl-introduction); [Self-Driving Laboratories (passage 3)](automation/self-driving-labs.html#sec-sdl-biology)
- [An autonomous laboratory for the accelerated synthesis of inorganic materials](https://doi.org/10.1038/s41586-023-06734-w)Nathan J. Szymanski; Bernardus Rendy; Yuxing Fei; et al. 2023. Naturejournal articleCited in: [Self-Driving Laboratories (passage 1)](automation/self-driving-labs.html); [Self-Driving Laboratories (passage 2)](automation/self-driving-labs.html#sdl-introduction); [Self-Driving Laboratories (passage 3)](automation/self-driving-labs.html#sec-sdl-alab)
- [Author Correction: An autonomous laboratory for the accelerated synthesis of novel materials](https://doi.org/10.1038/s41586-025-09992-y)Nathan J. Szymanski; Bernardus Rendy; Yuxing Fei; et al. 2026. Naturejournal articleCited in: [Self-Driving Laboratories (passage 1)](automation/self-driving-labs.html); [Self-Driving Laboratories (passage 2)](automation/self-driving-labs.html#sdl-introduction); [Self-Driving Laboratories (passage 3)](automation/self-driving-labs.html#sec-sdl-alab)

### Robotic Lab Automation and Cloud Labs

#### References

- [ARPA-H IGoR, 2026](https://arpa-h.gov/explore-funding/programs/igor)Cited in: [Robotic Lab Automation and Cloud Labs](automation/cloud-labs.html#robotic-lab-automation-cloud-labs-introduction)
- [Synthetic biology open language (SBOL) version 3.0.0](https://doi.org/10.1515/jib-2020-0017)Hasan Baig; Pedro Fontanarrosa; Vishwesh Kulkarni; et al. 2020. Journal of Integrative Bioinformaticsjournal articleCited in: [Robotic Lab Automation and Cloud Labs](automation/cloud-labs.html#robotic-lab-automation-cloud-labs-protocol-languages)
- [A mobile robotic chemist](https://doi.org/10.1038/s41586-020-2442-2)Benjamin Burger; Phillip M. Maffettone; Vladimir V. Gusev; et al. 2020. Naturejournal articleCited in: [Robotic Lab Automation and Cloud Labs (passage 1)](automation/cloud-labs.html#robotic-lab-automation-cloud-labs-introduction); [Robotic Lab Automation and Cloud Labs (passage 2)](automation/cloud-labs.html#robotic-lab-automation-cloud-labs-demonstrated)
- [PyLabRobot: An open-source, hardware-agnostic interface for liquid-handling robots and accessories](https://doi.org/10.1016/j.device.2023.100111)Rick P. Wierenga; Stefan M. Golas; Wilson Ho; et al. 2023. Devicejournal articleCited in: [Robotic Lab Automation and Cloud Labs (passage 1)](automation/cloud-labs.html#robotic-lab-automation-cloud-labs-introduction); [Robotic Lab Automation and Cloud Labs (passage 2)](automation/cloud-labs.html#robotic-lab-automation-cloud-labs-protocol-languages); [Robotic Lab Automation and Cloud Labs (passage 3)](automation/cloud-labs.html#robotic-lab-automation-cloud-labs-demonstrated)
- [The FAIR Guiding Principles for scientific data management and stewardship](https://doi.org/10.1038/sdata.2016.18)Mark D. Wilkinson; Michel Dumontier; IJsbrand Jan Aalbersberg; et al. 2016. Scientific Datajournal articleCited in: [Robotic Lab Automation and Cloud Labs](automation/cloud-labs.html#robotic-lab-automation-cloud-labs-theoretical)

### Synthetic Biology Design Tools

#### References

- [Automated synthetic cell-based screening for designed proteins with emergent functions](https://doi.org/10.1038/s41467-026-76787-8)Kareem Al Nahas; Béla P. Frohn; Aleksandra Šakanović; et al. 2026. Nature Communicationsjournal articleCited in: [Synthetic Biology Design Tools](automation/synbio-tools.html#synthetic-biology-design-tools-demonstrated)
- [Protein design meets biosecurity](https://doi.org/10.1126/science.ado1671)David Baker; George Church. 2024. Sciencejournal articleCited in: [Synthetic Biology Design Tools](automation/synbio-tools.html#synthetic-biology-design-tools-demonstrated)
- [Genome modelling and design across all domains of life with Evo 2](https://doi.org/10.1038/s41586-026-10176-5)Garyk Brixi; Matthew G. Durrant; Jerome Ku; et al. 2026. Naturejournal articleCited in: [Synthetic Biology Design Tools](automation/synbio-tools.html#synthetic-biology-design-tools-demonstrated)
- [Optknock: A bilevel programming framework for identifying gene knockout strategies for microbial strain optimization](https://doi.org/10.1002/bit.10803)Anthony P. Burgard; Priti Pharkya; Costas D. Maranas. 2003. Biotechnology and Bioengineeringjournal articleCited in: [Synthetic Biology Design Tools](automation/synbio-tools.html#synthetic-biology-design-tools-demonstrated)
- [Generative AI designs functional thiolation domains for reprogramming non-ribosomal peptide synthetases](https://doi.org/10.1038/s41467-026-77963-6)Emre F. Bülbül; Seounggun Bang; Kevin George; et al. 2026. Nature Communicationsjournal articleCited in: [Synthetic Biology Design Tools](automation/synbio-tools.html#synthetic-biology-design-tools-demonstrated)
- [Nucleotide Transformer: building and evaluating robust foundation models for human genomics](https://doi.org/10.1038/s41592-024-02523-z)Hugo Dalla-Torre; Liam Gonzalez; Javier Mendoza-Revilla; et al. 2024. Nature Methodsjournal articleCited in: [Synthetic Biology Design Tools](automation/synbio-tools.html#synthetic-biology-design-tools-demonstrated)
- [Robust deep learning–based protein sequence design using ProteinMPNN](https://doi.org/10.1126/science.add2187)J. Dauparas; I. Anishchenko; N. Bennett; et al. 2022. Sciencejournal articleCited in: [Synthetic Biology Design Tools (passage 1)](automation/synbio-tools.html#synthetic-biology-design-tools-introduction); [Synthetic Biology Design Tools (passage 2)](automation/synbio-tools.html#synthetic-biology-design-tools-demonstrated)
- [RetroPath2.0: A retrosynthesis workflow for metabolic engineers](https://doi.org/10.1016/j.ymben.2017.12.002)Baudoin Delépine; Thomas Duigou; Pablo Carbonell; et al. 2018. Metabolic Engineeringjournal articleCited in: [Synthetic Biology Design Tools](automation/synbio-tools.html#synthetic-biology-design-tools-demonstrated)
- [IGSC, 2024](https://genesynthesisconsortium.org/wp-content/uploads/IGSC-Harmonized-Screening-Protocol-v3.0-1.pdf)Cited in: [Synthetic Biology Design Tools](automation/synbio-tools.html#synthetic-biology-design-tools-demonstrated)
- [Ikonomova et al., 2025](https://www.nist.gov/publications/experimental-evaluation-ai%2Ddriven-protein-design-risks-using-safe-biological-proxies)Cited in: [Synthetic Biology Design Tools](automation/synbio-tools.html#synthetic-biology-design-tools-demonstrated)
- [Inter-tool Analysis of a NIST Dataset for Assessing Baseline Nucleic Acid Sequence Screening](https://doi.org/10.1177/15356760251401228)Tyler S. Laird; Kevin Flyangolts; Craig Bartling; et al. 2025. Applied Biosafetyjournal articleCited in: [Synthetic Biology Design Tools](automation/synbio-tools.html#synthetic-biology-design-tools-demonstrated)
- [Large language models generate functional protein sequences across diverse families](https://doi.org/10.1038/s41587-022-01618-2)Ali Madani; Ben Krause; Eric R. Greene; et al. 2023. Nature Biotechnologyjournal articleCited in: [Synthetic Biology Design Tools](automation/synbio-tools.html#synthetic-biology-design-tools-demonstrated)
- [Semantic design of functional de novo genes from a genomic language model](https://doi.org/10.1038/s41586-025-09749-7)Aditi T. Merchant; Samuel H. King; Eric Nguyen; et al. 2025. Naturejournal articleCited in: [Synthetic Biology Design Tools](automation/synbio-tools.html#synthetic-biology-design-tools-demonstrated)
- [National Academies, 2025](https://www.ncbi.nlm.nih.gov/books/NBK614601/)Cited in: [Synthetic Biology Design Tools](automation/synbio-tools.html#synthetic-biology-design-tools-introduction)
- [Sequence modeling and design from molecular to genome scale with Evo](https://doi.org/10.1126/science.ado9336)Eric Nguyen; Michael Poli; Matthew G. Durrant; et al. 2024. Sciencejournal articleCited in: [Synthetic Biology Design Tools (passage 1)](automation/synbio-tools.html#synthetic-biology-design-tools-introduction); [Synthetic Biology Design Tools (passage 2)](automation/synbio-tools.html#synthetic-biology-design-tools-demonstrated)
- [NIH Office of Science Policy, 2024](https://osp.od.nih.gov/us-government-releases-policy-for-oversight-of-dual-use-research-of-concern-and-pathogens-with-enhanced-pandemic-potential/)Cited in: [Synthetic Biology Design Tools](automation/synbio-tools.html#synthetic-biology-design-tools-demonstrated)
- [A call for built-in biosecurity safeguards for generative AI tools](https://doi.org/10.1038/s41587-025-02650-8)Mengdi Wang; Zaixi Zhang; Amrit Singh Bedi; et al. 2025. Nature Biotechnologyjournal articleCited in: [Synthetic Biology Design Tools](automation/synbio-tools.html#synthetic-biology-design-tools-demonstrated)
- [De novo design of protein structure and function with RFdiffusion](https://doi.org/10.1038/s41586-023-06415-8)Joseph L. Watson; David Juergens; Nathaniel R. Bennett; et al. 2023. Naturejournal articleCited in: [Synthetic Biology Design Tools (passage 1)](automation/synbio-tools.html#synthetic-biology-design-tools-introduction); [Synthetic Biology Design Tools (passage 2)](automation/synbio-tools.html#synthetic-biology-design-tools-demonstrated)

### AI for Biomanufacturing

#### References

- [Digital Twins in Pharmaceutical and Biopharmaceutical Manufacturing: A Literature Review](https://doi.org/10.3390/pr8091088)Yingjie Chen; Ou Yang; Chaitanya Sampat; et al. 2020. Processesjournal articleCited in: [AI for Biomanufacturing](automation/biomanufacturing.html#biomanufacturing-digital-twins)
- [Cytiva, 2026](https://www.cytivalifesciences.com/en/us/)Cited in: [AI for Biomanufacturing](automation/biomanufacturing.html#biomanufacturing-industry)
- [EMA, October 2026](https://www.ema.europa.eu/en/documents/report/report-multistakeholder-workshop-expert-contributions-artificial-intelligence-guidance-development-annex-22_en.pdf)Cited in: [AI for Biomanufacturing](automation/biomanufacturing.html#biomanufacturing-gmp)
- [FDA, 2004](https://www.fda.gov/regulatory-information/search-fda-guidance-documents/pat-framework-innovative-pharmaceutical-development-manufacturing-and-quality-assurance)Cited in: [AI for Biomanufacturing](automation/biomanufacturing.html#biomanufacturing-pat)
- [FDA, 2011](https://www.fda.gov/regulatory-information/search-fda-guidance-documents/process-validation-general-principles-and-practices)Cited in: [AI for Biomanufacturing](automation/biomanufacturing.html#biomanufacturing-pat)
- [Ginkgo Bioworks, 2026](https://www.ginkgo.bio/)Cited in: [AI for Biomanufacturing](automation/biomanufacturing.html#biomanufacturing-industry)
- [Machine learning in bioprocess development: from promise to practice](https://doi.org/10.1016/j.tibtech.2022.10.010)Laura M. Helleckes; Johannes Hemmerich; Wolfgang Wiechert; et al. 2023. Trends in Biotechnologyjournal articleCited in: [AI for Biomanufacturing (passage 1)](automation/biomanufacturing.html#biomanufacturing-introduction); [AI for Biomanufacturing (passage 2)](automation/biomanufacturing.html#biomanufacturing-bioprocess)
- [ICH Q10, 2008](https://database.ich.org/sites/default/files/Q10%20Guideline.pdf)Cited in: [AI for Biomanufacturing](automation/biomanufacturing.html#biomanufacturing-pat)
- [ICH Q8(R2), 2009](https://database.ich.org/sites/default/files/Q8_R2_Guideline.pdf)Cited in: [AI for Biomanufacturing](automation/biomanufacturing.html#biomanufacturing-pat)
- [Review on machine learning-based bioprocess optimization, monitoring, and control systems](https://doi.org/10.1016/j.biortech.2022.128523)Partha Pratim Mondal; Abhinav Galodha; Vishal Kumar Verma; et al. 2023. Bioresource Technologyjournal articleCited in: [AI for Biomanufacturing](automation/biomanufacturing.html#biomanufacturing-introduction)
- [Resilience, 2026](https://resilience.com/)Cited in: [AI for Biomanufacturing](automation/biomanufacturing.html#biomanufacturing-industry)
- [Sartorius, 2026](https://www.sartorius.com/en)Cited in: [AI for Biomanufacturing](automation/biomanufacturing.html#biomanufacturing-industry)
- [Big data and machine learning driven bioprocessing – Recent trends and critical analysis](https://doi.org/10.1016/j.biortech.2023.128625)Chao-Tung Yang; Endah Kristiani; Yoong Kit Leong; et al. 2023. Bioresource Technologyjournal articleCited in: [AI for Biomanufacturing](automation/biomanufacturing.html#biomanufacturing-introduction)

### Agentic Science Workflows

#### References

- [AI-Orchestrated De Novo Discovery of a Therapeutic Anti-IgE VHH in 16 Days](https://divergencelabs.com/publications/ige-preprint.pdf)Abramovitz et al. 2026. Divergence Labs PreprintpreprintCited in: [Agentic Science Workflows](automation/agentic-workflows.html#agentic-science-workflows-demonstrated)
- [CellVoyager: AI CompBio agent generates new insights by autonomously analyzing biological data](https://doi.org/10.1038/s41592-026-03029-6)Samuel Alber; Bowen Chen; Eric Sun; et al. 2026. Nature Methodsjournal articleCited in: [Agentic Science Workflows (passage 1)](automation/agentic-workflows.html#agentic-science-workflows-introduction); [Agentic Science Workflows (passage 2)](automation/agentic-workflows.html#agentic-science-workflows-demonstrated)
- [Claude discovers a novel enzyme system](https://www.anthropic.com/news/claude-discovers-novel-enzyme-system)Anthropic. 2026. AnthropicwebpageCited in: [Agentic Science Workflows](automation/agentic-workflows.html#agentic-science-workflows-demonstrated)
- [Early progress on accelerating drug development with Claude](https://www.anthropic.com/research/early-progress-drug-development)Anthropic. 2026. Anthropic Researchcompany research postCited in: [Agentic Science Workflows](automation/agentic-workflows.html#agentic-science-workflows-demonstrated)
- [ARPA-H IGoR, 2026](https://arpa-h.gov/explore-funding/programs/igor)Cited in: [Agentic Science Workflows (passage 1)](automation/agentic-workflows.html#agentic-science-workflows-introduction); [Agentic Science Workflows (passage 2)](automation/agentic-workflows.html#agentic-science-workflows-demonstrated)
- [Accelerating Antibody Lead Optimization with Frontier AI Agents and a High-Throughput Wet Lab](https://cdn.prod.website-files.com/64ff35ef45fc1e4545e5ffa1/6ae0ea8e28d4a390cbe66b52_bighat_pilot_retro_final_v2.pdf)BigHat Biosciences and Anthropic. 2026. BigHat Biosciences Technical Reporttechnical reportCited in: [Agentic Science Workflows](automation/agentic-workflows.html#agentic-science-workflows-demonstrated)
- [Autonomous chemical research with large language models](https://doi.org/10.1038/s41586-023-06792-0)Daniil A. Boiko; Robert MacKnight; Ben Kline; et al. 2023. Naturejournal articleCited in: [Agentic Science Workflows (passage 1)](automation/agentic-workflows.html#agentic-science-workflows-introduction); [Agentic Science Workflows (passage 2)](automation/agentic-workflows.html#agentic-science-workflows-demonstrated)
- [Humans or LLMs as the Judge? A Study on Judgement Bias](https://doi.org/10.18653/v1/2024.emnlp-main.474)Guiming Hardy Chen; Shunian Chen; Ziche Liu; et al. 2024. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processingproceedings articleCited in: [Agentic Science Workflows](automation/agentic-workflows.html#agentic-science-workflows-system-evaluation)
- [Codreanu, Imas, Mateos-Garcia et al., 2026](https://ai.google/static/documents/AI-in-Science.pdf)Cited in: [Agentic Science Workflows](automation/agentic-workflows.html#agentic-science-workflows-beyond-current-capabilities)
- [Accelerating CMC Document Review and Regulatory Authoring with Claude](https://enveda.com/CMC-Document-Processor/)Enveda. 2026. Enveda Technical Reporttechnical reportCited in: [Agentic Science Workflows](automation/agentic-workflows.html#agentic-science-workflows-demonstrated)
- [CMC Document Processor Supplementary Information](https://github.com/enveda/open-doc-review/blob/main/docs/Supplementary_Enveda-Anthropic_CDP_methods_paper.pdf)Enveda. 2026. Enveda Supplementary Informationtechnical reportCited in: [Agentic Science Workflows](automation/agentic-workflows.html#agentic-science-workflows-demonstrated)
- [open-doc-review: AI-powered batch record review and regulatory document authoring](https://github.com/enveda/open-doc-review)Enveda. 2026. GitHubsoftware repositoryCited in: [Agentic Science Workflows](automation/agentic-workflows.html#agentic-science-workflows-demonstrated)
- [Closing the Loop in AI-Driven Biomedical Discovery](https://doi.org/10.20944/preprints202608.2107.v1)Ada Fang; Kevin Li; Ayush Noori; et al. 2026preprintCited in: [Agentic Science Workflows](automation/agentic-workflows.html#agentic-science-workflows-beyond-current-capabilities)
- [Agentic discovery and synthesis of macrocyclic inhibitors of an undisclosed therapeutic target](https://generatebiomedicines.com/publications/refactored-intracellular-domains-enhance-car-expression-and-function/)Generate Biomedicines. 2026. Generate Biomedicines Technical Reporttechnical reportCited in: [Agentic Science Workflows](automation/agentic-workflows.html#agentic-science-workflows-demonstrated)
- [A multi-agent system for automating scientific discovery](https://doi.org/10.1038/s41586-026-10652-y)Ali E. Ghareeb; Benjamin Chang; Ludovico Mitchener; et al. 2026. Naturejournal articleCited in: [Agentic Science Workflows (passage 1)](automation/agentic-workflows.html#agentic-science-workflows-introduction); [Agentic Science Workflows (passage 2)](automation/agentic-workflows.html#agentic-science-workflows-demonstrated)
- [Accelerating scientific discovery with Co-Scientist](https://doi.org/10.1038/s41586-026-10644-y)Juraj Gottweis; Wei-Hung Weng; Alexander Daryin; et al. 2026. Naturejournal articleCited in: [Agentic Science Workflows (passage 1)](automation/agentic-workflows.html#agentic-science-workflows-introduction); [Agentic Science Workflows (passage 2)](automation/agentic-workflows.html#agentic-science-workflows-demonstrated)
- [Toward autonomous science with agentic artificial intelligence](https://doi.org/10.1016/j.cell.2026.08.052)Lucie Y. Guo; Darren S.J. Ting; Xinyi Su; Alex Aliper; Alex Zhavoronkov; Daniel S.W. Ting. 2026. Celljournal articleCited in: [Agentic Science Workflows](automation/agentic-workflows.html#agentic-science-workflows-theoretical)
- [Agentic AI and the rise of in silico team science in biomedical research](https://doi.org/10.1038/s41587-026-03035-1)Binglan Li; Anil Kumar Saini; Jose Guadalupe Hernandez; et al. 2026. Nature Biotechnologyjournal articleCited in: [Agentic Science Workflows](automation/agentic-workflows.html#agentic-science-workflows-architecture)
- [Augmenting large language models with chemistry tools](https://doi.org/10.1038/s42256-024-00832-8)Andres M. Bran; Sam Cox; Oliver Schilter; et al. 2024. Nature Machine Intelligencejournal articleCited in: [Agentic Science Workflows (passage 1)](automation/agentic-workflows.html#agentic-science-workflows-introduction); [Agentic Science Workflows (passage 2)](automation/agentic-workflows.html#agentic-science-workflows-demonstrated)
- [Paper2Agent](https://github.com/jmiao24/Paper2Agent)Jiacheng Miao. 2026. GitHubsoftwareCited in: [Agentic Science Workflows](automation/agentic-workflows.html#agentic-science-workflows-demonstrated)
- [Reimagining research papers as interactive and reliable AI agents](https://doi.org/10.1038/s41586-026-11044-y)Jiacheng Miao; Joe R. Davis; Yaohui Zhang; Jonathan K. Pritchard; James Zou. 2026. Naturejournal articleCited in: [Agentic Science Workflows](automation/agentic-workflows.html#agentic-science-workflows-demonstrated)
- [Nature, 2026](https://doi.org/10.1038/d41586-026-01551-3)Cited in: [Agentic Science Workflows](automation/agentic-workflows.html#agentic-science-workflows-beyond-current-capabilities)
- [NIST AI 800-2, initial public draft, 2026](https://doi.org/10.6028/NIST.AI.800-2.ipd)Cited in: [Agentic Science Workflows](automation/agentic-workflows.html#agentic-science-workflows-system-evaluation)
- [NIST, 2023](https://doi.org/10.6028/NIST.AI.100-1)Cited in: [Agentic Science Workflows](automation/agentic-workflows.html#agentic-science-workflows-introduction)
- [OpenAI, 2026](https://openai.com/index/research-acceleration-view-inside-openai/)Cited in: [Agentic Science Workflows](automation/agentic-workflows.html#agentic-science-workflows-beyond-current-capabilities)
- [Phuong et al., 2026](https://arxiv.org/abs/2607.13087)Cited in: [Agentic Science Workflows](automation/agentic-workflows.html#agentic-science-workflows-beyond-current-capabilities)
- [The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodies](https://doi.org/10.1038/s41586-025-09442-9)Kyle Swanson; Wesley Wu; Nash L. Bulaong; et al. 2025. Naturejournal articleCited in: [Agentic Science Workflows (passage 1)](automation/agentic-workflows.html#agentic-science-workflows-introduction); [Agentic Science Workflows (passage 2)](automation/agentic-workflows.html#agentic-science-workflows-demonstrated)
- [Target Discovery and Biologic Design for LAMA2-Related Muscular Dystrophy Using Claude](https://www-cdn.anthropic.com/52e1f5d50c47ca3818f56704c27cf19e59b62e77.pdf)Tranbarger and Mithril Medicine. 2026. Anthropic and Mithril Medicine Technical Reporttechnical reportCited in: [Agentic Science Workflows](automation/agentic-workflows.html#agentic-science-workflows-demonstrated)
- [WHO, 2021](https://www.who.int/publications/i/item/9789240029200)Cited in: [Agentic Science Workflows](automation/agentic-workflows.html#agentic-science-workflows-introduction)
- [Yao et al., 2025](https://proceedings.iclr.cc/paper_files/paper/2025/hash/1b126cc38b8638e07bef37e7b2bb72bf-Abstract-Conference.html)Cited in: [Agentic Science Workflows](automation/agentic-workflows.html#agentic-science-workflows-system-evaluation)
- [Autonomous AI agents discover reverse transcriptases with tandem repeat arrays](https://www-cdn.anthropic.com/22573675ada52a8ca8a97a1a4b4326b2f208a071.pdf)Yoon; Athukoralage; Ameisen; Kauderer-Abrams; Perry; Durrant. 2026. AnthropicpreprintCited in: [Agentic Science Workflows](automation/agentic-workflows.html#agentic-science-workflows-demonstrated)
- [CRISPR-GPT for agentic automation of gene-editing experiments](https://doi.org/10.1038/s41551-025-01463-z)Yuanhao Qu; Kaixuan Huang; Ming Yin; Kanghong Zhan; Dyllan Liu; Di Yin; Henry C. Cousins; William A. Johnson; Xiaotong Wang; Mihir Shah; Russ B. Altman; Denny Zhou; Mengdi Wang; Le Cong. 2025. Nature Biomedical Engineeringjournal articleCited in: [Agentic Science Workflows](automation/agentic-workflows.html#agentic-science-workflows-demonstrated)
- [The Virtual Biotech: A multi-agent AI framework for therapeutic discovery and development](https://doi.org/10.1126/science.aeg6779)Harrison G. Zhang; Peter Eckmann; Jiacheng Miao; Andrew B. Mahon; James Zou. 2026. Sciencejournal articleCited in: [Agentic Science Workflows](automation/agentic-workflows.html#agentic-science-workflows-demonstrated)

## Part VII: Evaluation, Practice, and Governance

### Toolkit for AI-Augmented Bio Research

#### References

- [Accurate structure prediction of biomolecular interactions with AlphaFold 3](https://doi.org/10.1038/s41586-024-07487-w)Josh Abramson; Jonas Adler; Jack Dunger; et al. 2024. Naturejournal articleCited in: [Toolkit for AI-Augmented Bio Research](practical/toolkit.html#sec-toolkit-inventory)
- [Addendum: Accurate structure prediction of biomolecular interactions with AlphaFold 3](https://doi.org/10.1038/s41586-024-08416-7)Josh Abramson; Jonas Adler; Jack Dunger; et al. 2024. Naturejournal articleCited in: [Toolkit for AI-Augmented Bio Research](practical/toolkit.html#sec-toolkit-inventory)
- [Basic local alignment search tool](https://doi.org/10.1016/S0022-2836%2805%2980360-2)Stephen F. Altschul; Warren Gish; Webb Miller; et al. 1990. Journal of Molecular Biologyjournal articleCited in: [Toolkit for AI-Augmented Bio Research](practical/toolkit.html#sec-toolkit-inventory)
- [Anthropic, June 2026](https://www.anthropic.com/news/claude-science-ai-workbench)Cited in: [Toolkit for AI-Augmented Bio Research](practical/toolkit.html#sec-toolkit-inventory)
- [Anthropic, Life Sciences Verification Program, 2026](https://www.anthropic.com/news/life-sciences-verification-program)Cited in: [Toolkit for AI-Augmented Bio Research](practical/toolkit.html#sec-toolkit-inventory)
- [GuacaMol: Benchmarking Models for de Novo Molecular Design](https://doi.org/10.1021/acs.jcim.8b00839)Nathan Brown; Marco Fiscato; Marwin H.S. Segler; et al. 2019. Journal of Chemical Information and Modelingjournal articleCited in: [Toolkit for AI-Augmented Bio Research](practical/toolkit.html#sec-toolkit-inventory)
- [Fast and sensitive protein alignment using DIAMOND](https://doi.org/10.1038/nmeth.3176)Benjamin Buchfink; Chao Xie; Daniel H Huson. 2014. Nature Methodsjournal articleCited in: [Toolkit for AI-Augmented Bio Research](practical/toolkit.html#sec-toolkit-inventory)
- [Accurate proteome-wide missense variant effect prediction with AlphaMissense](https://doi.org/10.1126/science.adg7492)Jun Cheng; Guido Novati; Joshua Pan; et al. 2023. Sciencejournal articleCited in: [Toolkit for AI-Augmented Bio Research](practical/toolkit.html#sec-toolkit-inventory)
- [scGPT: toward building a foundation model for single-cell multi-omics using generative AI](https://doi.org/10.1038/s41592-024-02201-0)Haotian Cui; Chloe Wang; Hassaan Maan; et al. 2024. Nature Methodsjournal articleCited in: [Toolkit for AI-Augmented Bio Research](practical/toolkit.html#sec-toolkit-inventory)
- [Robust deep learning–based protein sequence design using ProteinMPNN](https://doi.org/10.1126/science.add2187)J. Dauparas; I. Anishchenko; N. Bennett; et al. 2022. Sciencejournal articleCited in: [Toolkit for AI-Augmented Bio Research](practical/toolkit.html#sec-toolkit-inventory)
- [Nextflow enables reproducible computational workflows](https://doi.org/10.1038/nbt.3820)Paolo Di Tommaso; Maria Chatzou; Evan W Floden; et al. 2017. Nature Biotechnologyjournal articleCited in: [Toolkit for AI-Augmented Bio Research](practical/toolkit.html#sec-toolkit-inventory)
- [Datasheets for datasets](https://doi.org/10.1145/3458723)Timnit Gebru; Jamie Morgenstern; Briana Vecchione; et al. 2021. Communications of the ACMjournal articleCited in: [Toolkit for AI-Augmented Bio Research](practical/toolkit.html#sec-toolkit-principles)
- [Google DeepMind, AlphaFold 3 Model Parameters Terms of Use](https://github.com/google-deepmind/alphafold3/blob/main/WEIGHTS_TERMS_OF_USE.md)Cited in: [Toolkit for AI-Augmented Bio Research](practical/toolkit.html#sec-toolkit-inventory)
- [Google DeepMind, AlphaFold 3 repository README](https://github.com/google-deepmind/alphafold3/blob/main/README.md)Cited in: [Toolkit for AI-Augmented Bio Research](practical/toolkit.html#sec-toolkit-inventory)
- [Large-scale foundation model on single-cell transcriptomics](https://doi.org/10.1038/s41592-024-02305-7)Minsheng Hao; Jing Gong; Xin Zeng; et al. 2024. Nature Methodsjournal articleCited in: [Toolkit for AI-Augmented Bio Research](practical/toolkit.html#sec-toolkit-inventory)
- [Landrum, 2014](https://doi.org/10.5281/zenodo.10398)Cited in: [Toolkit for AI-Augmented Bio Research](practical/toolkit.html#sec-toolkit-inventory)
- [Evolutionary-scale prediction of atomic-level protein structure with a language model](https://doi.org/10.1126/science.ade2574)Zeming Lin; Halil Akin; Roshan Rao; et al. 2023. Sciencejournal articleCited in: [Toolkit for AI-Augmented Bio Research](practical/toolkit.html#sec-toolkit-inventory)
- [Disruption of the research software landscape through AI software generation](https://doi.org/10.1038/s41592-026-03210-x)Nelson D. Medina; Joergen M. R. Kornfeld. 2026. Nature Methodsjournal articleCited in: [Toolkit for AI-Augmented Bio Research](practical/toolkit.html#sec-toolkit-inventory)
- [ColabFold: making protein folding accessible to all](https://doi.org/10.1038/s41592-022-01488-1)Milot Mirdita; Konstantin Schütze; Yoshitaka Moriwaki; et al. 2022. Nature Methodsjournal articleCited in: [Toolkit for AI-Augmented Bio Research](practical/toolkit.html#sec-toolkit-inventory)
- [Model Cards for Model Reporting](https://doi.org/10.1145/3287560.3287596)Margaret Mitchell; Simone Wu; Andrew Zaldivar; et al. 2019. Proceedings of the Conference on Fairness, Accountability, and Transparencyproceedings articleCited in: [Toolkit for AI-Augmented Bio Research](practical/toolkit.html#sec-toolkit-principles)
- [Sustainable data analysis with Snakemake](https://doi.org/10.12688/f1000research.29032.2)Felix Mölder; Kim Philipp Jablonski; Brice Letcher; et al. 2021. F1000Researchjournal articleCited in: [Toolkit for AI-Augmented Bio Research](practical/toolkit.html#sec-toolkit-inventory)
- [Introducing GPT-Rosalind for life sciences research](https://openai.com/index/introducing-gpt-rosalind/)OpenAI. 2026. OpenAIvendor announcementCited in: [Toolkit for AI-Augmented Bio Research](practical/toolkit.html#sec-toolkit-inventory)
- [Meet Rosalind Workbench: Empowering every scientist to be their own research team](https://developers.openai.com/blog/rosalind-workbench)OpenAI Rosalind team. 2026. OpenAI Developersvendor announcementCited in: [Toolkit for AI-Augmented Bio Research](practical/toolkit.html#sec-toolkit-inventory)
- [MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets](https://doi.org/10.1038/nbt.3988)Martin Steinegger; Johannes Söding. 2017. Nature Biotechnologyjournal articleCited in: [Toolkit for AI-Augmented Bio Research](practical/toolkit.html#sec-toolkit-inventory)
- [Transfer learning enables predictions in network biology](https://doi.org/10.1038/s41586-023-06139-9)Christina V. Theodoris; Ling Xiao; Anant Chopra; et al. 2023. Naturejournal articleCited in: [Toolkit for AI-Augmented Bio Research](practical/toolkit.html#sec-toolkit-inventory)
- [DOME: recommendations for supervised machine learning validation in biology](https://doi.org/10.1038/s41592-021-01205-4)Ian Walsh; Dmytro Fishman; Dario Garcia-Gasulla; et al. 2021. Nature Methodsjournal articleCited in: [Toolkit for AI-Augmented Bio Research](practical/toolkit.html#sec-toolkit-principles)
- [De novo design of protein structure and function with RFdiffusion](https://doi.org/10.1038/s41586-023-06415-8)Joseph L. Watson; David Juergens; Nathaniel R. Bennett; et al. 2023. Naturejournal articleCited in: [Toolkit for AI-Augmented Bio Research](practical/toolkit.html#sec-toolkit-inventory)
- [The FAIR Guiding Principles for scientific data management and stewardship](https://doi.org/10.1038/sdata.2016.18)Mark D. Wilkinson; Michel Dumontier; IJsbrand Jan Aalbersberg; et al. 2016. Scientific Datajournal articleCited in: [Toolkit for AI-Augmented Bio Research](practical/toolkit.html#sec-toolkit-principles)
- [SCANPY: large-scale single-cell gene expression data analysis](https://doi.org/10.1186/s13059-017-1382-0)F. Alexander Wolf; Philipp Angerer; Fabian J. Theis. 2018. Genome Biologyjournal articleCited in: [Toolkit for AI-Augmented Bio Research](practical/toolkit.html#sec-toolkit-inventory)
- [MoleculeNet: a benchmark for molecular machine learning](https://doi.org/10.1039/C7SC02664A)Zhenqin Wu; Bharath Ramsundar; Evan N. Feinberg; et al. 2018. Chemical Sciencejournal articleCited in: [Toolkit for AI-Augmented Bio Research](practical/toolkit.html#sec-toolkit-inventory)

### Benchmarks for Bio AI

#### References

- [System Card: Claude Opus 5.5](https://www-cdn.anthropic.com/fc1b44717c85dc068bc6ba5024219938094694bd/Claude%20Opus%205.5%20System%20Card.pdf)Anthropic. 2026. AnthropicSystem cardCited in: [Benchmarks for Bio AI](governance/benchmarks.html#sec-benchmarks-dangerous-capability)
- [Anthropic, 2026, system card](https://www.anthropic.com/claude-opus-5-system-card)Cited in: [Benchmarks for Bio AI](governance/benchmarks.html#sec-benchmarks-dangerous-capability)
- [Arc Institute, 2025](https://arcinstitute.org/news/virtual-cell-challenge-2025-wrap-up)Cited in: [Benchmarks for Bio AI](governance/benchmarks.html#benchmarks-introduction)
- [GuacaMol: Benchmarking Models for de Novo Molecular Design](https://doi.org/10.1021/acs.jcim.8b00839)Nathan Brown; Marco Fiscato; Marwin H.S. Segler; et al. 2019. Journal of Chemical Information and Modelingjournal articleCited in: [Benchmarks for Bio AI](governance/benchmarks.html#benchmarks-demonstrated)
- [PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences](https://doi.org/10.1039/D3SC04185A)Martin Buttenschoen; Garrett M. Morris; Charlotte M. Deane. 2024. Chemical Sciencejournal articleCited in: [Benchmarks for Bio AI (passage 1)](governance/benchmarks.html#benchmarks-introduction); [Benchmarks for Bio AI (passage 2)](governance/benchmarks.html#benchmarks-demonstrated)
- [Continuous Automated Model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12](https://doi.org/10.1002/prot.25431)Jürgen Haas; Alessandro Barbato; Dario Behringer; et al. 2017. Proteins: Structure, Function, and Bioinformaticsjournal articleCited in: [Benchmarks for Bio AI (passage 1)](governance/benchmarks.html#benchmarks-introduction); [Benchmarks for Bio AI (passage 2)](governance/benchmarks.html#benchmarks-demonstrated)
- [Hong et al., 2026, preprint](https://arxiv.org/abs/2602.16703)Cited in: [Benchmarks for Bio AI](governance/benchmarks.html#sec-benchmarks-dangerous-capability)
- [Artificial intelligence foundation for therapeutic science](https://doi.org/10.1038/s41589-022-01131-2)Kexin Huang; Tianfan Fu; Wenhao Gao; et al. 2022. Nature Chemical Biologyjournal articleCited in: [Benchmarks for Bio AI](governance/benchmarks.html#benchmarks-demonstrated)
- [Benchmarking biomedical foundation models](https://doi.org/10.1038/s41592-026-03182-y)Julio Saez-Rodriguez; Philipp Sven Lars Schäfer; Nikolas Kalavros; Gustavo Stolovitzky. 2026. Nature Methodsjournal articleCited in: [Benchmarks for Bio AI](governance/benchmarks.html)
- [Expanding the AI evaluation toolbox with statistical models](https://doi.org/10.6028/NIST.AI.800-3)Drew Keller; Kweku Kwegyir-Aggrey; Ryan Steed; et al. 2026reportCited in: [Benchmarks for Bio AI](governance/benchmarks.html#sec-benchmarks-dangerous-capability)
- [Kryshtafovych et al., 2024](https://pmc.ncbi.nlm.nih.gov/articles/PMC10843301/)Cited in: [Benchmarks for Bio AI (passage 1)](governance/benchmarks.html#benchmarks-introduction); [Benchmarks for Bio AI (passage 2)](governance/benchmarks.html#benchmarks-demonstrated)
- [Laurent et al., 2024, preprint](https://doi.org/10.48550/arXiv.2407.10362)Cited in: [Benchmarks for Bio AI](governance/benchmarks.html#sec-benchmarks-dangerous-capability)
- [Li et al., 2024](https://proceedings.mlr.press/v235/li24bc.html)Cited in: [Benchmarks for Bio AI](governance/benchmarks.html#sec-benchmarks-dangerous-capability)
- [Benchmarking atlas-level data integration in single-cell genomics](https://doi.org/10.1038/s41592-021-01336-8)Malte D. Luecken; M. Büttner; K. Chaichoompu; et al. 2021. Nature Methodsjournal articleCited in: [Benchmarks for Bio AI](governance/benchmarks.html#benchmarks-demonstrated)
- [Defining and benchmarking open problems in single-cell analysis](https://doi.org/10.1038/s41587-025-02694-w)Malte D. Luecken; Scott Gigante; Daniel B. Burkhardt; et al. 2025. Nature Biotechnologyjournal articleCited in: [Benchmarks for Bio AI](governance/benchmarks.html#benchmarks-demonstrated)
- [Mouton et al., 2024](https://doi.org/10.7249/RRA2977-2)Cited in: [Benchmarks for Bio AI](governance/benchmarks.html#sec-benchmarks-dangerous-capability)
- [NIST AI 800-2, initial public draft, 2026](https://doi.org/10.6028/NIST.AI.800-2.ipd)Cited in: [Benchmarks for Bio AI](governance/benchmarks.html#sec-benchmarks-dangerous-capability)
- [OpenAI, 2024](https://openai.com/index/building-an-early-warning-system-for-llm-aided-biological-threat-creation/)Cited in: [Benchmarks for Bio AI](governance/benchmarks.html#sec-benchmarks-dangerous-capability)
- [OpenAI, 2026, system card](https://deploymentsafety.openai.com/gpt-5-6)Cited in: [Benchmarks for Bio AI](governance/benchmarks.html#sec-benchmarks-dangerous-capability)
- [Rao et al., 2026, preprint](https://arxiv.org/abs/2608.30165)Cited in: [Benchmarks for Bio AI](governance/benchmarks.html#benchmarks-demonstrated)
- [Virtual Cell Challenge: Toward a Turing test for the virtual cell](https://doi.org/10.1016/j.cell.2025.06.008)Yusuf H. Roohani; Tony J. Hua; Po-Yuan Tung; et al. 2025. Celljournal articleCited in: [Benchmarks for Bio AI](governance/benchmarks.html#benchmarks-introduction)
- [MMBU: A Massive Multi-modal Biomedical Understanding Benchmark to Probe the Perception Capabilities of Vision-Language Models](https://arxiv.org/abs/2606.06696v1)Ryan D'Cunha; Alejandro Lozano; Xiaoxiao Sun; Daniel Vela Jarquin; Min Woo Sun; Josiah Aklilu; James Burgess; Yuhui Zhang; Ryan Nayebi; Paola Avila Robayo; Jin Ye; Ming Hu; Zhongying Deng; Junjun He; Xin Chen; Yue Yao; Robert Tibshirani; Jeffrey J. Nirschl; Serena Yeung-Levy. 2026. arXivpreprintCited in: [Benchmarks for Bio AI](governance/benchmarks.html#benchmarks-demonstrated)
- [MoleculeNet: a benchmark for molecular machine learning](https://doi.org/10.1039/C7SC02664A)Zhenqin Wu; Bharath Ramsundar; Evan N. Feinberg; et al. 2018. Chemical Sciencejournal articleCited in: [Benchmarks for Bio AI](governance/benchmarks.html#benchmarks-introduction)
- [Zhang et al., 2026, preprint](https://arxiv.org/abs/2602.23329)Cited in: [Benchmarks for Bio AI](governance/benchmarks.html#sec-benchmarks-dangerous-capability)
- [An open benchmark and language models for AI in aging biology](https://doi.org/10.1016/j.cell.2026.08.026)Alex Zhavoronkov; Vladimir Naumov; Denis Sidorenko; Alex Aliper; Vladimir Aladinskiy; Ramin Hasani; Alexander Amini; Katerina Nasto; Mathieu Reymond; Rim Shayakhmetov; Zulfat Miftakhutdinov; Vadim N. Gladyshev; Fedor Galkin. 2026. Celljournal articleCited in: [Benchmarks for Bio AI](governance/benchmarks.html#benchmarks-demonstrated)
- [MetagenomicsBench: A Verifiable Benchmark for Agentic Metagenomics Analysis](https://doi.org/10.64898/2026.10.05.756819)Zhen Yang; John Connolly; Arjun Banerjee; Shaista Madad. 2026. bioRxivpreprintCited in: [Benchmarks for Bio AI](governance/benchmarks.html#benchmarks-demonstrated)

### Reproducibility and Open Science

#### References

- [AlphaFold Protein Structure Database, 2026](https://alphafold.ebi.ac.uk/about)Cited in: [Reproducibility and Open Science](governance/reproducibility.html#reproducibility-open-science-demonstrated)
- [ARPA-H IGoR, 2026](https://arpa-h.gov/explore-funding/programs/igor)Cited in: [Reproducibility and Open Science](governance/reproducibility.html#reproducibility-open-science-introduction)
- [Chai-1: Decoding the molecular interactions of life](https://doi.org/10.1101/2024.10.10.615955)Chai Discovery; Jacques Boitreaud; Jack Dent; et al. 2024preprintCited in: [Reproducibility and Open Science](governance/reproducibility.html#reproducibility-open-science-demonstrated)
- [ESM Metagenomic Atlas, 2026](https://esmatlas.com/about)Cited in: [Reproducibility and Open Science](governance/reproducibility.html#reproducibility-open-science-demonstrated)
- [Open and sustainable AI: challenges, opportunities and the road ahead in the life sciences](https://doi.org/10.1038/s41592-026-03037-6)Gavin Farrell; Eleni Adamidi; Rafael Andrade Buono; et al. 2026. Nature Methodsjournal articleCited in: [Reproducibility and Open Science](governance/reproducibility.html#reproducibility-open-science-demonstrated)
- [Datasheets for datasets](https://doi.org/10.1145/3458723)Timnit Gebru; Jamie Morgenstern; Briana Vecchione; et al. 2021. Communications of the ACMjournal articleCited in: [Reproducibility and Open Science](governance/reproducibility.html#reproducibility-open-science-demonstrated)
- [Model Cards for Model Reporting](https://doi.org/10.1145/3287560.3287596)Margaret Mitchell; Simone Wu; Andrew Zaldivar; et al. 2019. Proceedings of the Conference on Fairness, Accountability, and Transparencyproceedings articleCited in: [Reproducibility and Open Science](governance/reproducibility.html#reproducibility-open-science-demonstrated)
- [NIH Common Fund](https://commonfund.nih.gov/bridge2ai)Cited in: [Reproducibility and Open Science](governance/reproducibility.html#reproducibility-open-science-introduction)
- [DOME: recommendations for supervised machine learning validation in biology](https://doi.org/10.1038/s41592-021-01205-4)Ian Walsh; Dmytro Fishman; Dario Garcia-Gasulla; et al. 2021. Nature Methodsjournal articleCited in: [Reproducibility and Open Science (passage 1)](governance/reproducibility.html); [Reproducibility and Open Science (passage 2)](governance/reproducibility.html#reproducibility-open-science-introduction); [Reproducibility and Open Science (passage 3)](governance/reproducibility.html#reproducibility-open-science-demonstrated)
- [The FAIR Guiding Principles for scientific data management and stewardship](https://doi.org/10.1038/sdata.2016.18)Mark D. Wilkinson; Michel Dumontier; IJsbrand Jan Aalbersberg; et al. 2016. Scientific Datajournal articleCited in: [Reproducibility and Open Science](governance/reproducibility.html#reproducibility-open-science-demonstrated)
- [Boltz-1 Democratizing Biomolecular Interaction Modeling](https://doi.org/10.1101/2024.11.19.624167)Jeremy Wohlwend; Gabriele Corso; Saro Passaro; et al. 2024preprintCited in: [Reproducibility and Open Science (passage 1)](governance/reproducibility.html#reproducibility-open-science-introduction); [Reproducibility and Open Science (passage 2)](governance/reproducibility.html#reproducibility-open-science-demonstrated)

### Information Hazards in Capability Research

#### References

- [Accurate structure prediction of biomolecular interactions with AlphaFold 3](https://doi.org/10.1038/s41586-024-07487-w)Josh Abramson; Jonas Adler; Jack Dunger; et al. 2024. Naturejournal articleCited in: [Information Hazards in Capability Research](governance/information-hazards.html#information-hazards-demonstrated)
- [ARPA-H IGoR, 2026](https://arpa-h.gov/explore-funding/programs/igor)Cited in: [Information Hazards in Capability Research](governance/information-hazards.html#information-hazards-introduction)
- [Protein design meets biosecurity](https://doi.org/10.1126/science.ado1671)David Baker; George Church. 2024. Sciencejournal articleCited in: [Information Hazards in Capability Research](governance/information-hazards.html#information-hazards-introduction)
- [AI and biosecurity: The need for governance](https://doi.org/10.1126/science.adq1977)Doni Bloomfield; Jaspreet Pannu; Alex W. Zhu; et al. 2024. Sciencejournal articleCited in: [Information Hazards in Capability Research](governance/information-hazards.html#information-hazards-introduction)
- [IGSC, 2024](https://genesynthesisconsortium.org/wp-content/uploads/IGSC-Harmonized-Screening-Protocol-v3.0-1.pdf)Cited in: [Information Hazards in Capability Research](governance/information-hazards.html#information-hazards-theoretical)
- [Ikonomova et al., 2025](https://www.nist.gov/publications/experimental-evaluation-ai%2Ddriven-protein-design-risks-using-safe-biological-proxies)Cited in: [Information Hazards in Capability Research](governance/information-hazards.html#information-hazards-theoretical)
- [International AI Safety Report, 2026](https://internationalaisafetyreport.org/publication/international-ai-safety-report-2026)Cited in: [Information Hazards in Capability Research](governance/information-hazards.html#information-hazards-theoretical)
- [Sequence modeling and design from molecular to genome scale with Evo](https://doi.org/10.1126/science.ado9336)Eric Nguyen; Michael Poli; Matthew G. Durrant; et al. 2024. Sciencejournal articleCited in: [Information Hazards in Capability Research](governance/information-hazards.html#information-hazards-demonstrated)
- [NIH NOT-OD-26-101](https://grants.nih.gov/grants/guide/notice-files/NOT-OD-26-101.html)Cited in: [Information Hazards in Capability Research (passage 1)](governance/information-hazards.html); [Information Hazards in Capability Research (passage 2)](governance/information-hazards.html#information-hazards-introduction)
- [NIST, 2026](https://www.nist.gov/blogs/caisi-research-blog/insights-ai-agent-security-large-scale-red-teaming-competition)Cited in: [Information Hazards in Capability Research](governance/information-hazards.html#information-hazards-demonstrated)
- [NSABB 2023 report](https://osp.od.nih.gov/wp-content/uploads/2023/03/NSABB-Final-Report-Proposed-Biosecurity-Oversight-Framework-for-the-Future-of-Science.pdf)Cited in: [Information Hazards in Capability Research](governance/information-hazards.html#information-hazards-demonstrated)
- [USG Policy for Stopping High-Risk Life Sciences Research](https://www.whitehouse.gov/wp-content/uploads/2026/07/USG-Policy-for-Stopping-High-Risk-Life-Sciences-Research_July-2026.pdf)Cited in: [Information Hazards in Capability Research](governance/information-hazards.html)
- [A call for built-in biosecurity safeguards for generative AI tools](https://doi.org/10.1038/s41587-025-02650-8)Mengdi Wang; Zaixi Zhang; Amrit Singh Bedi; et al. 2025. Nature Biotechnologyjournal articleCited in: [Information Hazards in Capability Research (passage 1)](governance/information-hazards.html#information-hazards-introduction); [Information Hazards in Capability Research (passage 2)](governance/information-hazards.html#information-hazards-theoretical)
- [De novo design of protein structure and function with RFdiffusion](https://doi.org/10.1038/s41586-023-06415-8)Joseph L. Watson; David Juergens; Nathaniel R. Bennett; et al. 2023. Naturejournal articleCited in: [Information Hazards in Capability Research](governance/information-hazards.html#information-hazards-demonstrated)

### Workforce, Compute, and Institutional Readiness

#### References

- [Anthropic, June 2026](https://www.anthropic.com/news/claude-science-ai-workbench)Cited in: [Workforce, Compute, and Institutional Readiness](governance/workforce-compute.html#workforce-compute-readiness-demonstrated)
- [Anthropic, October 2025](https://www.anthropic.com/news/claude-for-life-sciences)Cited in: [Workforce, Compute, and Institutional Readiness](governance/workforce-compute.html#workforce-compute-readiness-demonstrated)
- [ARPA-H IGoR, 2026](https://arpa-h.gov/explore-funding/programs/igor)Cited in: [Workforce, Compute, and Institutional Readiness](governance/workforce-compute.html#workforce-compute-readiness-introduction)
- [EMA, 2024](https://www.ema.europa.eu/en/use-artificial-intelligence-ai-medicinal-product-lifecycle)Cited in: [Workforce, Compute, and Institutional Readiness](governance/workforce-compute.html#workforce-compute-readiness-introduction)
- [FDA, 2026](https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development)Cited in: [Workforce, Compute, and Institutional Readiness](governance/workforce-compute.html#workforce-compute-readiness-introduction)
- [Isomorphic Labs, January 2024](https://www.isomorphiclabs.com/articles/isomorphic-labs-kicks-off-2024-with-two-pharmaceutical-collaborations)Cited in: [Workforce, Compute, and Institutional Readiness](governance/workforce-compute.html#workforce-compute-readiness-demonstrated)
- [NIH Common Fund](https://commonfund.nih.gov/bridge2ai)Cited in: [Workforce, Compute, and Institutional Readiness](governance/workforce-compute.html#workforce-compute-readiness-introduction)
- [NIST, 2023](https://doi.org/10.6028/NIST.AI.100-1)Cited in: [Workforce, Compute, and Institutional Readiness](governance/workforce-compute.html#workforce-compute-readiness-introduction)
- [Novo Nordisk, September 2026](https://www.novonordisk.com/content/nncorp/global/en/news-and-media/news-and-ir-materials/news-details.html?id=916768)Cited in: [Workforce, Compute, and Institutional Readiness](governance/workforce-compute.html#workforce-compute-readiness-demonstrated)
- [Owkin, September 2026](https://www.owkin.com/newsfeed/owkin-to-license-k-pro-ai-scientist-and-multimodal-oncology-and-immunology-data-to-boehringer-ingelheim)Cited in: [Workforce, Compute, and Institutional Readiness](governance/workforce-compute.html#workforce-compute-readiness-demonstrated)
- [WHO, 2021](https://www.who.int/publications/i/item/9789240029200)Cited in: [Workforce, Compute, and Institutional Readiness](governance/workforce-compute.html#workforce-compute-readiness-introduction)

### Emerging Frontiers in AI for the Life Sciences

#### References

- [Accurate structure prediction of biomolecular interactions with AlphaFold 3](https://doi.org/10.1038/s41586-024-07487-w)Josh Abramson; Jonas Adler; Jack Dunger; et al. 2024. Naturejournal articleCited in: [Emerging Frontiers in AI for the Life Sciences](future/emerging.html#sec-emerging-multimodal)
- [Arc Institute, 2025](https://arcinstitute.org/news/virtual-cell-challenge-2025-wrap-up)Cited in: [Emerging Frontiers in AI for the Life Sciences](future/emerging.html#sec-emerging-virtual-cells)
- [Autonomous chemical research with large language models](https://doi.org/10.1038/s41586-023-06792-0)Daniil A. Boiko; Robert MacKnight; Ben Kline; et al. 2023. Naturejournal articleCited in: [Emerging Frontiers in AI for the Life Sciences](future/emerging.html#sec-emerging-autonomous-loops)
- [How to build the virtual cell with artificial intelligence: Priorities and opportunities](https://doi.org/10.1016/j.cell.2024.11.015)Charlotte Bunne; Yusuf Roohani; Yanay Rosen; et al. 2024. Celljournal articleCited in: [Emerging Frontiers in AI for the Life Sciences](future/emerging.html#sec-emerging-virtual-cells)
- [A mobile robotic chemist](https://doi.org/10.1038/s41586-020-2442-2)Benjamin Burger; Phillip M. Maffettone; Vladimir V. Gusev; et al. 2020. Naturejournal articleCited in: [Emerging Frontiers in AI for the Life Sciences](future/emerging.html#sec-emerging-autonomous-loops)
- [The past, present and future of self-driving laboratories](https://doi.org/10.1038/s41570-026-00847-2)Richard B. Canty; Milad Abolhasani. 2026. Nature Reviews Chemistryjournal articleCited in: [Emerging Frontiers in AI for the Life Sciences](future/emerging.html#sec-emerging-autonomous-loops)
- [Accurate proteome-wide missense variant effect prediction with AlphaMissense](https://doi.org/10.1126/science.adg7492)Jun Cheng; Guido Novati; Joshua Pan; et al. 2023. Sciencejournal articleCited in: [Emerging Frontiers in AI for the Life Sciences](future/emerging.html#sec-emerging-personalised)
- [A multimodal conversational agent for DNA, RNA and protein tasks](https://doi.org/10.1038/s42256-025-01047-1)Bernardo P. de Almeida; Guillaume Richard; Hugo Dalla-Torre; et al. 2025. Nature Machine Intelligencejournal articleCited in: [Emerging Frontiers in AI for the Life Sciences](future/emerging.html#sec-emerging-multimodal)
- [A multi-agent system for automating scientific discovery](https://doi.org/10.1038/s41586-026-10652-y)Ali E. Ghareeb; Benjamin Chang; Ludovico Mitchener; et al. 2026. Naturejournal articleCited in: [Emerging Frontiers in AI for the Life Sciences](future/emerging.html#sec-emerging-autonomous-loops)
- [Accelerating scientific discovery with Co-Scientist](https://doi.org/10.1038/s41586-026-10644-y)Juraj Gottweis; Wei-Hung Weng; Alexander Daryin; et al. 2026. Naturejournal articleCited in: [Emerging Frontiers in AI for the Life Sciences](future/emerging.html#sec-emerging-autonomous-loops)
- [Generalized biological foundation model with unified nucleic acid and protein language](https://doi.org/10.1038/s42256-025-01044-4)Yong He; Pan Fang; Yongtao Shan; et al. 2025. Nature Machine Intelligencejournal articleCited in: [Emerging Frontiers in AI for the Life Sciences](future/emerging.html#sec-emerging-multimodal)
- [Jayatunga et al., 2024](https://doi.org/10.1016/j.drudis.2024.104009)Cited in: [Emerging Frontiers in AI for the Life Sciences](future/emerging.html#sec-emerging-ai-therapeutics)
- [Self-driving laboratory for accelerated discovery of thin-film materials](https://doi.org/10.1126/sciadv.aaz8867)B. P. MacLeod; F. G. L. Parlane; T. D. Morrissey; et al. 2020. Science Advancesjournal articleCited in: [Emerging Frontiers in AI for the Life Sciences](future/emerging.html#sec-emerging-autonomous-loops)
- [Clinical use of current polygenic risk scores may exacerbate health disparities](https://doi.org/10.1038/s41588-019-0379-x)Alicia R. Martin; Masahiro Kanai; Yoichiro Kamatani; et al. 2019. Nature Geneticsjournal articleCited in: [Emerging Frontiers in AI for the Life Sciences](future/emerging.html#sec-emerging-personalised)
- [Responsible use of polygenic risk scores in the clinic: potential benefits, risks and gaps](https://doi.org/10.1038/s41591-021-01549-6)Polygenic Risk Score Task Force of the International Common Disease Alliance; Adebowale Adeyemo; Mary K. Balaconis; et al. 2021. Nature Medicinejournal articleCited in: [Emerging Frontiers in AI for the Life Sciences](future/emerging.html#sec-emerging-personalised)
- [Towards the construction of a virtual yeast](https://doi.org/10.1038/s41586-026-10574-9)Liujia Qian; Zizhuo Zhou; Peijie Zhou; et al. 2026. Naturejournal articleCited in: [Emerging Frontiers in AI for the Life Sciences](future/emerging.html#sec-emerging-virtual-cells)
- [Generalist biological artificial intelligence in modeling the language of life](https://doi.org/10.1038/s41587-026-03064-w)Vishwanatha M. Rao; Serena Zhang; Brian S. Plosky; et al. 2026. Nature Biotechnologyjournal articleCited in: [Emerging Frontiers in AI for the Life Sciences](future/emerging.html#sec-emerging-universal-model)
- [Virtual Cell Challenge: Toward a Turing test for the virtual cell](https://doi.org/10.1016/j.cell.2025.06.008)Yusuf H. Roohani; Tony J. Hua; Po-Yuan Tung; et al. 2025. Celljournal articleCited in: [Emerging Frontiers in AI for the Life Sciences](future/emerging.html#sec-emerging-virtual-cells)
- [The Missing Diversity in Human Genetic Studies](https://doi.org/10.1016/j.cell.2019.02.048)Giorgio Sirugo; Scott M. Williams; Sarah A. Tishkoff. 2019. Celljournal articleCited in: [Emerging Frontiers in AI for the Life Sciences](future/emerging.html#sec-emerging-personalised)
- [The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodies](https://doi.org/10.1038/s41586-025-09442-9)Kyle Swanson; Wesley Wu; Nash L. Bulaong; et al. 2025. Naturejournal articleCited in: [Emerging Frontiers in AI for the Life Sciences](future/emerging.html#sec-emerging-autonomous-loops)
- [Bringing the genetically minimal cell to life on a computer in 4D](https://doi.org/10.1016/j.cell.2026.02.009)Zane R. Thornburg; Andrew Maytin; Jiwoong Kwon; et al. 2026. Celljournal articleCited in: [Emerging Frontiers in AI for the Life Sciences](future/emerging.html#sec-emerging-virtual-cells)
- [WHO, 2021](https://www.who.int/publications/i/item/9789240029200)Cited in: [Emerging Frontiers in AI for the Life Sciences](future/emerging.html#sec-emerging-global-access)
- [Do no harm: a roadmap for responsible machine learning for health care](https://doi.org/10.1038/s41591-019-0548-6)Jenna Wiens; Suchi Saria; Mark Sendak; et al. 2019. Nature Medicinejournal articleCited in: [Emerging Frontiers in AI for the Life Sciences](future/emerging.html#sec-emerging-global-access)

## Appendices

### References

#### References

- [Will AI ever win its own Nobel? Some predict a prize-worthy science discovery soon](https://doi.org/10.1038/d41586-025-03223-0)Jenna Ahart. 2025. Naturenews featureCited in: [References](references.html#sec-references-automation)
- [Corso et al., 2023](https://openreview.net/forum?id=kKF8_K-mBbS)Cited in: [References](references.html#sec-references-small-molecules)
- [Jin et al., 2018](https://proceedings.mlr.press/v80/jin18a.html)Cited in: [References](references.html#sec-references-small-molecules)
- [Nobel Turing Challenge: creating the engine for scientific discovery](https://doi.org/10.1038/s41540-021-00189-3)Hiroaki Kitano. 2021. npj Systems Biology and Applicationsjournal articleCited in: [References](references.html#sec-references-automation)
- [Meier et al., 2021, NeurIPS](https://proceedings.neurips.cc/paper/2021/hash/f51338d736f95dd42427296047067694-Abstract.html)Cited in: [References](references.html#sec-references-genome)
- [Peng et al., 2022](https://proceedings.mlr.press/v162/peng22b.html)Cited in: [References](references.html#sec-references-small-molecules)
- [Stärk et al., 2022](https://proceedings.mlr.press/v162/stark22b.html)Cited in: [References](references.html#sec-references-small-molecules)

#### Recommended reading

- [Accurate structure prediction of biomolecular interactions with AlphaFold 3](https://doi.org/10.1038/s41586-024-07487-w)Josh Abramson; Jonas Adler; Jack Dunger; et al. 2024. Naturejournal articleRecommended in: [References](references.html#sec-references-structure)
- [A Multiplexed Single-Cell CRISPR Screening Platform Enables Systematic Dissection of the Unfolded Protein Response](https://doi.org/10.1016/j.cell.2016.11.048)Britt Adamson; Thomas M. Norman; Marco Jost; et al. 2016. Celljournal articleRecommended in: [References](references.html#sec-references-perturbation)
- [Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines](https://doi.org/10.1038/s41592-025-02772-6)Constantin Ahlmann-Eltze; Wolfgang Huber; Simon Anders. 2025. Nature Methodsjournal articleRecommended in: [References](references.html#sec-references-cells)
- [Ahmad et al., 2022, preprint](https://arxiv.org/abs/2209.01712)Recommended in: [References](references.html#sec-references-small-molecules)
- [Aiforia, 2026](https://www.aiforia.com/)Recommended in: [References](references.html#sec-references-imaging)
- [Enhancing randomized clinical trials with digital twins](https://doi.org/10.1038/s41540-025-00592-0)Hossein Akbarialiabad; Amirmohammad Pasdar; Dédée F. Murrell; et al. 2025. npj Systems Biology and Applicationsjournal articleRecommended in: [References](references.html#sec-references-systems-biology)
- [Protein generation with evolutionary diffusion: sequence is all you need](https://doi.org/10.1101/2023.09.11.556673)Sarah Alamdari; Nitya Thakkar; Rianne van den Berg; et al. 2023preprintRecommended in: [References](references.html#sec-references-design)
- [CellVoyager: AI CompBio agent generates new insights by autonomously analyzing biological data](https://doi.org/10.1038/s41592-026-03029-6)Samuel Alber; Bowen Chen; Eric Sun; et al. 2026. Nature Methodsjournal articleRecommended in: [References](references.html#sec-references-automation)
- [AlphaFold Protein Structure Database, 2026](https://alphafold.ebi.ac.uk/about)Recommended in: [References](references.html#sec-references-infrastructure)
- [Anthropic, August 2026](https://www.anthropic.com/research/Claude-accelerates-protein-design)Recommended in: [References (passage 1)](references.html#sec-references-design); [References (passage 2)](references.html#sec-references-frontier-labs)
- [Anthropic, June 2026](https://www.anthropic.com/news/claude-science-ai-workbench)Recommended in: [References](references.html#sec-references-frontier-labs)
- [Anthropic, October 2025](https://www.anthropic.com/news/claude-for-life-sciences)Recommended in: [References](references.html#sec-references-frontier-labs)
- [Arc Institute, 2025](https://arcinstitute.org/news/virtual-cell-challenge-2025-wrap-up)Recommended in: [References](references.html#sec-references-perturbation)
- [Segment Anything for Microscopy](https://doi.org/10.1038/s41592-024-02580-4)Anwai Archit; Luca Freckmann; Sushmita Nair; et al. 2025. Nature Methodsjournal articleRecommended in: [References](references.html#sec-references-imaging)
- [ARPA-H IGoR, 2026](https://arpa-h.gov/explore-funding/programs/igor)Recommended in: [References](references.html#sec-references-programs)
- [Effective gene expression prediction from sequence by integrating long-range interactions](https://doi.org/10.1038/s41592-021-01252-x)Žiga Avsec; Vikram Agarwal; Daniel Visentin; et al. 2021. Nature Methodsjournal articleRecommended in: [References](references.html#sec-references-genome)
- [Advancing regulatory variant effect prediction with AlphaGenome](https://doi.org/10.1038/s41586-025-10014-0)Žiga Avsec; Natasha Latysheva; Jun Cheng; et al. 2026. Naturejournal articleRecommended in: [References](references.html#sec-references-genome)
- [Gene regulatory network inference in the era of single-cell multi-omics](https://doi.org/10.1038/s41576-023-00618-5)Pau Badia-i-Mompel; Lorna Wessels; Sophia Müller-Dott; et al. 2023. Nature Reviews Geneticsjournal articleRecommended in: [References](references.html#sec-references-systems-biology)
- [Accurate prediction of protein structures and interactions using a three-track neural network](https://doi.org/10.1126/science.abj8754)Minkyung Baek; Frank DiMaio; Ivan Anishchenko; et al. 2021. Sciencejournal articleRecommended in: [References](references.html#sec-references-structure)
- [Synthetic biology open language (SBOL) version 3.0.0](https://doi.org/10.1515/jib-2020-0017)Hasan Baig; Pedro Fontanarrosa; Vishwesh Kulkarni; et al. 2020. Journal of Integrative Bioinformaticsjournal articleRecommended in: [References](references.html#sec-references-automation)
- [Protein design meets biosecurity](https://doi.org/10.1126/science.ado1671)David Baker; George Church. 2024. Sciencejournal articleRecommended in: [References](references.html#sec-references-programs)
- [DNA methylation aging clocks: challenges and recommendations](https://doi.org/10.1186/s13059-019-1824-y)Christopher G. Bell; Robert Lowe; Peter D. Adams; et al. 2019. Genome Biologyjournal articleRecommended in: [References](references.html#sec-references-organisms)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://doi.org/10.18653/v1/D19-1371)Iz Beltagy; Kyle Lo; Arman Cohan. 2019. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)proceedings articleRecommended in: [References](references.html#sec-references-knowledge-literature)
- [A DNA language model based on multispecies alignment predicts the effects of genome-wide variants](https://doi.org/10.1038/s41587-024-02511-w)Gonzalo Benegas; Carlos Albors; Alan J. Aw; et al. 2025. Nature Biotechnologyjournal articleRecommended in: [References](references.html#sec-references-genome)
- [Atomically accurate de novo design of antibodies with RFdiffusion](https://doi.org/10.1038/s41586-025-09721-5)Nathaniel R. Bennett; Joseph L. Watson; Robert J. Ragotte; et al. 2025. Naturejournal articleRecommended in: [References](references.html#sec-references-design)
- [Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs](https://doi.org/10.1038/s41592-019-0575-8)Tristan Bepler; Andrew Morin; Micah Rapp; et al. 2019. Nature Methodsjournal articleRecommended in: [References](references.html#sec-references-imaging)
- [The Protein Data Bank](https://doi.org/10.1093/nar/28.1.235)H. M. Berman. 2000. Nucleic Acids Researchjournal articleRecommended in: [References](references.html#sec-references-infrastructure)
- [Novae: a graph-based foundation model for spatial transcriptomics data](https://doi.org/10.1038/s41592-025-02899-6)Quentin Blampey; Hakim Benkirane; Nadège Bercovici; et al. 2025. Nature Methodsjournal articleRecommended in: [References](references.html#sec-references-cells)
- [REINVENT 2.0: An AI Tool for De Novo Drug Design](https://doi.org/10.1021/acs.jcim.0c00915)Thomas Blaschke; Josep Arús-Pous; Hongming Chen; et al. 2020. Journal of Chemical Information and Modelingjournal articleRecommended in: [References](references.html#sec-references-small-molecules)
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- [Boltz-1 Democratizing Biomolecular Interaction Modeling](https://doi.org/10.1101/2024.11.19.624167)Jeremy Wohlwend; Gabriele Corso; Saro Passaro; et al. 2024preprintRecommended in: [References](references.html#sec-references-structure)
- [MoleculeNet: a benchmark for molecular machine learning](https://doi.org/10.1039/C7SC02664A)Zhenqin Wu; Bharath Ramsundar; Evan N. Feinberg; et al. 2018. Chemical Sciencejournal articleRecommended in: [References](references.html#sec-references-small-molecules)
- [High-resolution de novo structure prediction from primary sequence](https://doi.org/10.1101/2022.07.21.500999)Ruidong Wu; Fan Ding; Rui Wang; et al. 2022preprintRecommended in: [References](references.html#sec-references-structure)
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- [A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial](https://doi.org/10.1038/s41591-025-03743-2)Zuojun Xu; Feng Ren; Ping Wang; et al. 2025. Nature Medicinejournal articleRecommended in: [References](references.html#sec-references-small-molecules)
- [Analyzing Learned Molecular Representations for Property Prediction](https://doi.org/10.1021/acs.jcim.9b00237)Kevin Yang; Kyle Swanson; Wengong Jin; et al. 2019. Journal of Chemical Information and Modelingjournal articleRecommended in: [References](references.html#sec-references-small-molecules)
- [scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data](https://doi.org/10.1038/s42256-022-00534-z)Fan Yang; Wenchuan Wang; Fang Wang; et al. 2022. Nature Machine Intelligencejournal articleRecommended in: [References](references.html#sec-references-cells)
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- [An interpretable RNA foundation model for exploring functional RNA motifs in plants](https://doi.org/10.1038/s42256-024-00946-z)Haopeng Yu; Heng Yang; Wenqing Sun; et al. 2024. Nature Machine Intelligencejournal articleRecommended in: [References](references.html#sec-references-organisms)
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- [High-throughput target trial emulation for Alzheimer’s disease drug repurposing with real-world data](https://doi.org/10.1038/s41467-023-43929-1)Chengxi Zang; Hao Zhang; Jie Xu; et al. 2023. Nature Communicationsjournal articleRecommended in: [References](references.html#sec-references-rwe-biomarkers)
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### Quick Reference: All Chapter Summaries

#### References

- [Accurate structure prediction of biomolecular interactions with AlphaFold 3](https://doi.org/10.1038/s41586-024-07487-w)Josh Abramson; Jonas Adler; Jack Dunger; et al. 2024. Naturejournal articleCited in: [Quick Reference: All Chapter Summaries (passage 1)](appendices/tldr-compilation.html#summary-protein-structure); [Quick Reference: All Chapter Summaries (passage 2)](appendices/tldr-compilation.html#summary-reproducibility)
- [Addendum: Accurate structure prediction of biomolecular interactions with AlphaFold 3](https://doi.org/10.1038/s41586-024-08416-7)Josh Abramson; Jonas Adler; Jack Dunger; et al. 2024. Naturejournal articleCited in: [Quick Reference: All Chapter Summaries (passage 1)](appendices/tldr-compilation.html#summary-protein-structure); [Quick Reference: All Chapter Summaries (passage 2)](appendices/tldr-compilation.html#summary-reproducibility)
- [Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines](https://doi.org/10.1038/s41592-025-02772-6)Constantin Ahlmann-Eltze; Wolfgang Huber; Simon Anders. 2025. Nature Methodsjournal articleCited in: [Quick Reference: All Chapter Summaries (passage 1)](appendices/tldr-compilation.html#summary-single-cell-models); [Quick Reference: All Chapter Summaries (passage 2)](appendices/tldr-compilation.html#summary-virtual-cells); [Quick Reference: All Chapter Summaries (passage 3)](appendices/tldr-compilation.html#summary-translation-failures)
- [CellVoyager: AI CompBio agent generates new insights by autonomously analyzing biological data](https://doi.org/10.1038/s41592-026-03029-6)Samuel Alber; Bowen Chen; Eric Sun; et al. 2026. Nature Methodsjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-agentic-workflows)
- [ARPA-H IGoR, 2026](https://arpa-h.gov/explore-funding/programs/igor)Cited in: [Quick Reference: All Chapter Summaries (passage 1)](appendices/tldr-compilation.html#summary-cloud-labs); [Quick Reference: All Chapter Summaries (passage 2)](appendices/tldr-compilation.html#summary-workforce-compute)
- [Advancing regulatory variant effect prediction with AlphaGenome](https://doi.org/10.1038/s41586-025-10014-0)Žiga Avsec; Natasha Latysheva; Jun Cheng; et al. 2026. Naturejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-genome-models)
- [Gene regulatory network inference in the era of single-cell multi-omics](https://doi.org/10.1038/s41576-023-00618-5)Pau Badia-i-Mompel; Lorna Wessels; Sophia Müller-Dott; et al. 2023. Nature Reviews Geneticsjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-systems-biology)
- [DNA methylation aging clocks: challenges and recommendations](https://doi.org/10.1186/s13059-019-1824-y)Christopher G. Bell; Robert Lowe; Peter D. Adams; et al. 2019. Genome Biologyjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-aging-longevity)
- [A DNA language model based on multispecies alignment predicts the effects of genome-wide variants](https://doi.org/10.1038/s41587-024-02511-w)Gonzalo Benegas; Carlos Albors; Alan J. Aw; et al. 2025. Nature Biotechnologyjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-variant-effects)
- [Atomically accurate de novo design of antibodies with RFdiffusion](https://doi.org/10.1038/s41586-025-09721-5)Nathaniel R. Bennett; Joseph L. Watson; Robert J. Ragotte; et al. 2025. Naturejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-antibodies)
- [Novae: a graph-based foundation model for spatial transcriptomics data](https://doi.org/10.1038/s41592-025-02899-6)Quentin Blampey; Hakim Benkirane; Nadège Bercovici; et al. 2025. Nature Methodsjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-spatial-omics)
- [Deeper evaluation of a single-cell foundation model](https://doi.org/10.1038/s42256-024-00949-w)Rebecca Boiarsky; Nalini M. Singh; Alejandro Buendia; et al. 2024. Nature Machine Intelligencejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-single-cell-models)
- [Autonomous chemical research with large language models](https://doi.org/10.1038/s41586-023-06792-0)Daniil A. Boiko; Robert MacKnight; Ben Kline; et al. 2023. Naturejournal articleCited in: [Quick Reference: All Chapter Summaries (passage 1)](appendices/tldr-compilation.html#summary-self-driving-labs); [Quick Reference: All Chapter Summaries (passage 2)](appendices/tldr-compilation.html#summary-agentic-workflows)
- [Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes](https://doi.org/10.1038/nprot.2016.105)Mark-Anthony Bray; Shantanu Singh; Han Han; et al. 2016. Nature Protocolsjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-cell-painting)
- [Genome modelling and design across all domains of life with Evo 2](https://doi.org/10.1038/s41586-026-10176-5)Garyk Brixi; Matthew G. Durrant; Jerome Ku; et al. 2026. Naturejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-genome-models)
- [Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery](https://doi.org/10.1093/nar/gkae1128)Annalisa Buniello; Daniel Suveges; Carlos Cruz-Castillo; et al. 2024. Nucleic Acids Researchjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-targets)
- [How to build the virtual cell with artificial intelligence: Priorities and opportunities](https://doi.org/10.1016/j.cell.2024.11.015)Charlotte Bunne; Yusuf Roohani; Yanay Rosen; et al. 2024. Celljournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-virtual-organisms)
- [A mobile robotic chemist](https://doi.org/10.1038/s41586-020-2442-2)Benjamin Burger; Phillip M. Maffettone; Vladimir V. Gusev; et al. 2020. Naturejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-cloud-labs)
- [PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences](https://doi.org/10.1039/D3SC04185A)Martin Buttenschoen; Garrett M. Morris; Charlotte M. Deane. 2024. Chemical Sciencejournal articleCited in: [Quick Reference: All Chapter Summaries (passage 1)](appendices/tldr-compilation.html#summary-evaluation-principles); [Quick Reference: All Chapter Summaries (passage 2)](appendices/tldr-compilation.html#summary-translation-failures); [Quick Reference: All Chapter Summaries (passage 3)](appendices/tldr-compilation.html#summary-benchmarks)
- [CAMEO, 2026](https://www.expasy.org/resources/cameo)Cited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-benchmarks)
- [CellProfiler: image analysis software for identifying and quantifying cell phenotypes](https://doi.org/10.1186/gb-2006-7-10-r100)Anne E Carpenter; Thouis R Jones; Michael R Lamprecht; et al. 2006. Genome Biologyjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-cell-painting)
- [Chai-1: Decoding the molecular interactions of life](https://doi.org/10.1101/2024.10.10.615955)Chai Discovery; Jacques Boitreaud; Jack Dent; et al. 2024preprintCited in: [Quick Reference: All Chapter Summaries (passage 1)](appendices/tldr-compilation.html#summary-protein-structure); [Quick Reference: All Chapter Summaries (passage 2)](appendices/tldr-compilation.html#summary-reproducibility)
- [Three million images and morphological profiles of cells treated with matched chemical and genetic perturbations](https://doi.org/10.1038/s41592-024-02241-6)Srinivas Niranj Chandrasekaran; Beth A. Cimini; Amy Goodale; et al. 2024. Nature Methodsjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-cell-painting)
- [Morphological map of under- and overexpression of genes in human cells](https://doi.org/10.1038/s41592-025-02753-9)Srinivas Niranj Chandrasekaran; Eric Alix; John Arevalo; et al. 2025. Nature Methodsjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-cell-painting)
- [Towards a general-purpose foundation model for computational pathology](https://doi.org/10.1038/s41591-024-02857-3)Richard J. Chen; Tong Ding; Ming Y. Lu; et al. 2024. Nature Medicinejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-histopathology)
- [Accurate proteome-wide missense variant effect prediction with AlphaMissense](https://doi.org/10.1126/science.adg7492)Jun Cheng; Guido Novati; Joshua Pan; et al. 2023. Sciencejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-variant-effects)
- [Optimizing the Cell Painting assay for image-based profiling](https://doi.org/10.1038/s41596-023-00840-9)Beth A. Cimini; Srinivas Niranj Chandrasekaran; Maria Kost-Alimova; et al. 2023. Nature Protocolsjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-cell-painting)
- [scGPT: toward building a foundation model for single-cell multi-omics using generative AI](https://doi.org/10.1038/s41592-024-02201-0)Haotian Cui; Chloe Wang; Hassaan Maan; et al. 2024. Nature Methodsjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-single-cell-models)
- [Robust deep learning–based protein sequence design using ProteinMPNN](https://doi.org/10.1126/science.add2187)J. Dauparas; I. Anishchenko; N. Bennett; et al. 2022. Sciencejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-protein-design)
- [Atomic context-conditioned protein sequence design using LigandMPNN](https://doi.org/10.1038/s41592-025-02626-1)Justas Dauparas; Gyu Rie Lee; Robert Pecoraro; et al. 2025. Nature Methodsjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-protein-design)
- [A multimodal conversational agent for DNA, RNA and protein tasks](https://doi.org/10.1038/s42256-025-01047-1)Bernardo P. de Almeida; Guillaume Richard; Hugo Dalla-Torre; et al. 2025. Nature Machine Intelligencejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-emerging-frontiers)
- [A multimodal whole-slide foundation model for pathology](https://doi.org/10.1038/s41591-025-03982-3)Tong Ding; Sophia J. Wagner; Andrew H. Song; et al. 2025. Nature Medicinejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-histopathology)
- [Protein–ligand data at scale to support machine learning](https://doi.org/10.1038/s41570-025-00737-z)Aled M. Edwards; Dafydd R. Owen; The Structural Genomics Consortium Target 2035 Working Group; et al. 2025. Nature Reviews Chemistryjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-chemical-biology)
- [FDA, 2026](https://www.fda.gov/drugs/biomarker-qualification-program/about-biomarkers-and-qualification)Cited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-diagnostics-biomarkers)
- [FDA, 2026](https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development)Cited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-clinical-trials)
- [FDA, 2026](https://www.fda.gov/vaccines-blood-biologics/cellular-gene-therapy-products)Cited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-cell-gene-therapy)
- [Orthrus: toward evolutionary and functional RNA foundation models](https://doi.org/10.1038/s41592-026-03064-3)Philip Fradkin; Ruian “Ian” Shi; Taykhoom Dalal; et al. 2026. Nature Methodsjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-genome-models)
- [Disease variant prediction with deep generative models of evolutionary data](https://doi.org/10.1038/s41586-021-04043-8)Jonathan Frazer; Pascal Notin; Mafalda Dias; et al. 2021. Naturejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-variant-effects)
- [A foundation model of transcription across human cell types](https://doi.org/10.1038/s41586-024-08391-z)Xi Fu; Shentong Mo; Alejandro Buendia; et al. 2025. Naturejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-genome-models)
- [Datasheets for datasets](https://doi.org/10.1145/3458723)Timnit Gebru; Jamie Morgenstern; Briana Vecchione; et al. 2021. Communications of the ACMjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-data-infrastructure)
- [AlphaFold 3 Source Code License (Apache License 2.0, June 2026)](https://github.com/google-deepmind/alphafold3/blob/main/LICENSE)Google DeepMind. 2026. GitHubdocumentationCited in: [Quick Reference: All Chapter Summaries (passage 1)](appendices/tldr-compilation.html#summary-protein-structure); [Quick Reference: All Chapter Summaries (passage 2)](appendices/tldr-compilation.html#summary-reproducibility)
- [Google DeepMind, AlphaFold 3 Model Parameters Terms of Use, November 2024 release](https://github.com/google-deepmind/alphafold3/blob/v3.0.0/WEIGHTS_TERMS_OF_USE.md)Cited in: [Quick Reference: All Chapter Summaries (passage 1)](appendices/tldr-compilation.html#summary-protein-structure); [Quick Reference: All Chapter Summaries (passage 2)](appendices/tldr-compilation.html#summary-reproducibility)
- [Large-scale foundation model on single-cell transcriptomics](https://doi.org/10.1038/s41592-024-02305-7)Minsheng Hao; Jing Gong; Xin Zeng; et al. 2024. Nature Methodsjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-single-cell-models)
- [Generalized biological foundation model with unified nucleic acid and protein language](https://doi.org/10.1038/s42256-025-01044-4)Yong He; Pan Fang; Yongtao Shan; et al. 2025. Nature Machine Intelligencejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-emerging-frontiers)
- [Target Trial Emulation](https://doi.org/10.1001/jama.2022.21383)Miguel A. Hernán; Wei Wang; David E. Leaf. 2022. JAMAjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-real-world-evidence)
- [Learning the language of viral evolution and escape](https://doi.org/10.1126/science.abd7331)Brian Hie; Ellen D. Zhong; Bonnie Berger; et al. 2021. Sciencejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-vaccines)
- [Efficient evolution of human antibodies from general protein language models](https://doi.org/10.1038/s41587-023-01763-2)Brian L. Hie; Varun R. Shanker; Duo Xu; et al. 2023. Nature Biotechnologyjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-antibodies)
- [DNA methylation age of human tissues and cell types](https://doi.org/10.1186/gb-2013-14-10-r115)Steve Horvath. 2013. Genome Biologyjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-aging-longevity)
- [Artificial intelligence foundation for therapeutic science](https://doi.org/10.1038/s41589-022-01131-2)Kexin Huang; Tianfan Fu; Wenhao Gao; et al. 2022. Nature Chemical Biologyjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-benchmarks)
- [A foundation model for clinician-centered drug repurposing](https://doi.org/10.1038/s41591-024-03233-x)Kexin Huang; Payal Chandak; Qianwen Wang; et al. 2024. Nature Medicinejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-repurposing-combinations)
- [“Target Trial Emulation” for Observational Studies — Potential and Pitfalls](https://doi.org/10.1056/NEJMp2407586)Rebecca A. Hubbard; Constantine A. Gatsonis; Joseph W. Hogan; et al. 2024. New England Journal of Medicinejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-real-world-evidence)
- [Rapid in silico directed evolution by a protein language model with EVOLVEpro](https://doi.org/10.1126/science.adr6006)Kaiyi Jiang; Zhaoqing Yan; Matteo Di Bernardo; et al. 2025. Sciencejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-protein-design)
- [Computational scoring and experimental evaluation of enzymes generated by neural networks](https://doi.org/10.1038/s41587-024-02214-2)Sean R. Johnson; Xiaozhi Fu; Sandra Viknander; et al. 2024. Nature Biotechnologyjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-protein-design)
- [Highly accurate protein structure prediction with AlphaFold](https://doi.org/10.1038/s41586-021-03819-2)John Jumper; Richard Evans; Alexander Pritzel; et al. 2021. Naturejournal articleCited in: [Quick Reference: All Chapter Summaries (passage 1)](appendices/tldr-compilation.html#summary-history); [Quick Reference: All Chapter Summaries (passage 2)](appendices/tldr-compilation.html#summary-protein-structure)
- [A Whole-Cell Computational Model Predicts Phenotype from Genotype](https://doi.org/10.1016/j.cell.2012.05.044)Jonathan R. Karr; Jayodita C. Sanghvi; Derek N. Macklin; et al. 2012. Celljournal articleCited in: [Quick Reference: All Chapter Summaries (passage 1)](appendices/tldr-compilation.html#summary-systems-biology); [Quick Reference: All Chapter Summaries (passage 2)](appendices/tldr-compilation.html#summary-virtual-organisms)
- [Kryshtafovych et al., 2024](https://pmc.ncbi.nlm.nih.gov/articles/PMC10843301/)Cited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-benchmarks)
- [Challenges in High-Throughput Inorganic Materials Prediction and Autonomous Synthesis](https://doi.org/10.1103/PRXEnergy.3.011002)Josh Leeman; Yuhan Liu; Joseph Stiles; et al. 2024. PRX Energyjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-self-driving-labs)
- [Benchmarking atlas-level data integration in single-cell genomics](https://doi.org/10.1038/s41592-021-01336-8)Malte D. Luecken; M. Büttner; K. Chaichoompu; et al. 2021. Nature Methodsjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-benchmarks)
- [Defining and benchmarking open problems in single-cell analysis](https://doi.org/10.1038/s41587-025-02694-w)Malte D. Luecken; Scott Gigante; Daniel B. Burkhardt; et al. 2025. Nature Biotechnologyjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-benchmarks)
- [Augmenting large language models with chemistry tools](https://doi.org/10.1038/s42256-024-00832-8)Andres M. Bran; Sam Cox; Oliver Schilter; et al. 2024. Nature Machine Intelligencejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-agentic-workflows)
- [SpatialData: an open and universal data framework for spatial omics](https://doi.org/10.1038/s41592-024-02212-x)Luca Marconato; Giovanni Palla; Kevin A. Yamauchi; et al. 2024. Nature Methodsjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-spatial-omics)
- [CellProfiler 3.0: Next-generation image processing for biology](https://doi.org/10.1371/journal.pbio.2005970)Claire McQuin; Allen Goodman; Vasiliy Chernyshev; et al. 2018. PLOS Biologyjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-cell-painting)
- [A foundational large language model for edible plant genomes](https://doi.org/10.1038/s42003-024-06465-2)Javier Mendoza-Revilla; Evan Trop; Liam Gonzalez; et al. 2024. Communications Biologyjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-agriculture-ai)
- [Semantic design of functional de novo genes from a genomic language model](https://doi.org/10.1038/s41586-025-09749-7)Aditi T. Merchant; Samuel H. King; Eric Nguyen; et al. 2025. Naturejournal articleCited in: [Quick Reference: All Chapter Summaries (passage 1)](appendices/tldr-compilation.html#summary-genome-models); [Quick Reference: All Chapter Summaries (passage 2)](appendices/tldr-compilation.html#summary-synbio-tools)
- [Refining the impact of genetic evidence on clinical success](https://doi.org/10.1038/s41586-024-07316-0)Eric Vallabh Minikel; Jeffery L. Painter; Coco Chengliang Dong; et al. 2024. Naturejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-targets)
- [Sequence modeling and design from molecular to genome scale with Evo](https://doi.org/10.1126/science.ado9336)Eric Nguyen; Michael Poli; Matthew G. Durrant; et al. 2024. Sciencejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-genome-models)
- [NIH Common Fund](https://commonfund.nih.gov/bridge2ai)Cited in: [Quick Reference: All Chapter Summaries (passage 1)](appendices/tldr-compilation.html#summary-data-infrastructure); [Quick Reference: All Chapter Summaries (passage 2)](appendices/tldr-compilation.html#summary-workforce-compute)
- [NIH NOT-OD-26-101](https://grants.nih.gov/grants/guide/notice-files/NOT-OD-26-101.html)Cited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-information-hazards)
- [Harnessing artificial intelligence to fill global shortfalls in biodiversity knowledge](https://doi.org/10.1038/s44358-025-00022-3)Laura J. Pollock; Justin Kitzes; Sara Beery; et al. 2025. Nature Reviews Biodiversityjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-environmental-ecology-ai)
- [Learning structural heterogeneity from cryo-electron sub-tomograms with tomoDRGN](https://doi.org/10.1038/s41592-024-02210-z)Barrett M. Powell; Joseph H. Davis. 2024. Nature Methodsjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-microscopy-cryo-em)
- [DeepSynergy: predicting anti-cancer drug synergy with Deep Learning](https://doi.org/10.1093/bioinformatics/btx806)Kristina Preuer; Richard P I Lewis; Sepp Hochreiter; et al. 2017. Bioinformaticsjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-repurposing-combinations)
- [Generative AI as a tool to accelerate the field of ecology](https://doi.org/10.1038/s41559-024-02623-1)Kasim Rafiq; Sara Beery; Meredith S. Palmer; et al. 2025. Nature Ecology & Evolutionjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-environmental-ecology-ai)
- [A genome-wide atlas of human cell morphology](https://doi.org/10.1038/s41592-024-02537-7)Meraj Ramezani; Erin Weisbart; Julia Bauman; et al. 2025. Nature Methodsjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-cell-painting)
- [Genetic factors associated with reasons for clinical trial stoppage](https://doi.org/10.1038/s41588-024-01854-z)Olesya Razuvayevskaya; Irene Lopez; Ian Dunham; et al. 2024. Nature Geneticsjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-targets)
- [A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models](https://doi.org/10.1038/s41587-024-02143-0)Feng Ren; Alex Aliper; Jian Chen; et al. 2024. Nature Biotechnologyjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-small-molecules)
- [Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology](https://doi.org/10.1038/gim.2015.30)Sue Richards; Nazneen Aziz; Sherri Bale; et al. 2015. Genetics in Medicinejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-variant-effects)
- [Virtual Cell Challenge: Toward a Turing test for the virtual cell](https://doi.org/10.1016/j.cell.2025.06.008)Yusuf H. Roohani; Tony J. Hua; Po-Yuan Tung; et al. 2025. Celljournal articleCited in: [Quick Reference: All Chapter Summaries (passage 1)](appendices/tldr-compilation.html#summary-virtual-organisms); [Quick Reference: All Chapter Summaries (passage 2)](appendices/tldr-compilation.html#summary-benchmarks)
- [Discovery of antimicrobial peptides in the global microbiome with machine learning](https://doi.org/10.1016/j.cell.2024.05.013)Célio Dias Santos-Júnior; Marcelo D.T. Torres; Yiqian Duan; et al. 2024. Celljournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-multiomics)
- [DynaMight: estimating molecular motions with improved reconstruction from cryo-EM images](https://doi.org/10.1038/s41592-024-02377-5)Johannes Schwab; Dari Kimanius; Alister Burt; et al. 2024. Nature Methodsjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-microscopy-cryo-em)
- [Unsupervised evolution of protein and antibody complexes with a structure-informed language model](https://doi.org/10.1126/science.adk8946)Varun R. Shanker; Theodora U. J. Bruun; Brian L. Hie; et al. 2024. Sciencejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-antibodies)
- [Cellpose3: one-click image restoration for improved cellular segmentation](https://doi.org/10.1038/s41592-025-02595-5)Carsen Stringer; Marius Pachitariu. 2025. Nature Methodsjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-microscopy-cryo-em)
- [A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles](https://doi.org/10.1016/j.cell.2017.10.049)Aravind Subramanian; Rajiv Narayan; Steven M. Corsello; et al. 2017. Celljournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-repurposing-combinations)
- [The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodies](https://doi.org/10.1038/s41586-025-09442-9)Kyle Swanson; Wesley Wu; Nash L. Bulaong; et al. 2025. Naturejournal articleCited in: [Quick Reference: All Chapter Summaries (passage 1)](appendices/tldr-compilation.html#summary-self-driving-labs); [Quick Reference: All Chapter Summaries (passage 2)](appendices/tldr-compilation.html#summary-agentic-workflows)
- [An autonomous laboratory for the accelerated synthesis of inorganic materials](https://doi.org/10.1038/s41586-023-06734-w)Nathan J. Szymanski; Bernardus Rendy; Yuxing Fei; et al. 2023. Naturejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-self-driving-labs)
- [Nicheformer: a foundation model for single-cell and spatial omics](https://doi.org/10.1038/s41592-025-02814-z)Alejandro Tejada-Lapuerta; Anna C. Schaar; Robert Gutgesell; et al. 2025. Nature Methodsjournal articleCited in: [Quick Reference: All Chapter Summaries (passage 1)](appendices/tldr-compilation.html#summary-single-cell-models); [Quick Reference: All Chapter Summaries (passage 2)](appendices/tldr-compilation.html#summary-spatial-omics)
- [Transfer learning enables predictions in network biology](https://doi.org/10.1038/s41586-023-06139-9)Christina V. Theodoris; Ling Xiao; Anant Chopra; et al. 2023. Naturejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-single-cell-models)
- [From target discovery to clinical drug development with human genetics](https://doi.org/10.1038/s41586-023-06388-8)Katerina Trajanoska; Claude Bhérer; Daniel Taliun; et al. 2023. Naturejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-targets)
- [DOME: recommendations for supervised machine learning validation in biology](https://doi.org/10.1038/s41592-021-01205-4)Ian Walsh; Dmytro Fishman; Dario Garcia-Gasulla; et al. 2021. Nature Methodsjournal articleCited in: [Quick Reference: All Chapter Summaries (passage 1)](appendices/tldr-compilation.html#summary-evaluation-principles); [Quick Reference: All Chapter Summaries (passage 2)](appendices/tldr-compilation.html#summary-reproducibility)
- [A pathology foundation model for cancer diagnosis and prognosis prediction](https://doi.org/10.1038/s41586-024-07894-z)Xiyue Wang; Junhan Zhao; Eliana Marostica; et al. 2024. Naturejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-histopathology)
- [Foundation model of neural activity predicts response to new stimulus types](https://doi.org/10.1038/s41586-025-08829-y)Eric Y. Wang; Paul G. Fahey; Zhuokun Ding; et al. 2025. Naturejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-neuroscience-ai)
- [FDA Perspective on the Regulation of Artificial Intelligence in Health Care and Biomedicine](https://doi.org/10.1001/jama.2024.21451)Haider J. Warraich; Troy Tazbaz; Robert M. Califf. 2025. JAMAjournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-clinical-trials)
- [De novo design of protein structure and function with RFdiffusion](https://doi.org/10.1038/s41586-023-06415-8)Joseph L. Watson; David Juergens; Nathaniel R. Bennett; et al. 2023. Naturejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-protein-design)
- [PyLabRobot: An open-source, hardware-agnostic interface for liquid-handling robots and accessories](https://doi.org/10.1016/j.device.2023.100111)Rick P. Wierenga; Stefan M. Golas; Wilson Ho; et al. 2023. Devicejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-cloud-labs)
- [The FAIR Guiding Principles for scientific data management and stewardship](https://doi.org/10.1038/sdata.2016.18)Mark D. Wilkinson; Michel Dumontier; IJsbrand Jan Aalbersberg; et al. 2016. Scientific Datajournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-data-infrastructure)
- [Boltz-1 Democratizing Biomolecular Interaction Modeling](https://doi.org/10.1101/2024.11.19.624167)Jeremy Wohlwend; Gabriele Corso; Saro Passaro; et al. 2024preprintCited in: [Quick Reference: All Chapter Summaries (passage 1)](appendices/tldr-compilation.html#summary-protein-structure); [Quick Reference: All Chapter Summaries (passage 2)](appendices/tldr-compilation.html#summary-reproducibility)
- [MoleculeNet: a benchmark for molecular machine learning](https://doi.org/10.1039/C7SC02664A)Zhenqin Wu; Bharath Ramsundar; Evan N. Feinberg; et al. 2018. Chemical Sciencejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-benchmarks)
- [A vision–language foundation model for precision oncology](https://doi.org/10.1038/s41586-024-08378-w)Jinxi Xiang; Xiyue Wang; Xiaoming Zhang; et al. 2025. Naturejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-histopathology)
- [A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial](https://doi.org/10.1038/s41591-025-03743-2)Zuojun Xu; Feng Ren; Ping Wang; et al. 2025. Nature Medicinejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-small-molecules)
- [scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data](https://doi.org/10.1038/s42256-022-00534-z)Fan Yang; Wenchuan Wang; Fang Wang; et al. 2022. Nature Machine Intelligencejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-single-cell-models)
- [An interpretable RNA foundation model for exploring functional RNA motifs in plants](https://doi.org/10.1038/s42256-024-00946-z)Haopeng Yu; Heng Yang; Wenqing Sun; et al. 2024. Nature Machine Intelligencejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-agriculture-ai)
- [A disease-specific language model for variant pathogenicity in cardiac and regulatory genomics](https://doi.org/10.1038/s42256-025-01016-8)Huixin Zhan; Jason H. Moore; Zijun Zhang. 2025. Nature Machine Intelligencejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-variant-effects)
- [Algorithm for optimized mRNA design improves stability and immunogenicity](https://doi.org/10.1038/s41586-023-06127-z)He Zhang; Liang Zhang; Ang Lin; et al. 2023. Naturejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-vaccines)
- [Artificial intelligence in drug development](https://doi.org/10.1038/s41591-024-03434-4)Kang Zhang; Xin Yang; Yifei Wang; et al. 2025. Nature Medicinejournal articleCited in: [Quick Reference: All Chapter Summaries](appendices/tldr-compilation.html#summary-clinical-trials)

### Case Studies

#### References

- [Accurate structure prediction of biomolecular interactions with AlphaFold 3](https://doi.org/10.1038/s41586-024-07487-w)Josh Abramson; Jonas Adler; Jack Dunger; et al. 2024. Naturejournal articleCited in: [Case Studies](appendices/case-studies.html#sec-case-alphafold3-access)
- [Addendum: Accurate structure prediction of biomolecular interactions with AlphaFold 3](https://doi.org/10.1038/s41586-024-08416-7)Josh Abramson; Jonas Adler; Jack Dunger; et al. 2024. Naturejournal articleCited in: [Case Studies](appendices/case-studies.html#sec-case-alphafold3-access)
- [Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines](https://doi.org/10.1038/s41592-025-02772-6)Constantin Ahlmann-Eltze; Wolfgang Huber; Simon Anders. 2025. Nature Methodsjournal articleCited in: [Case Studies (passage 1)](appendices/case-studies.html#sec-case-single-cell-perturbation); [Case Studies (passage 2)](appendices/case-studies.html#sec-case-single-cell-foundation-model)
- [AlphaFold Protein Structure Database, 2026](https://alphafold.ebi.ac.uk/about)Cited in: [Case Studies](appendices/case-studies.html#sec-case-structure-prediction)
- [ARPA-H IGoR, 2026](https://arpa-h.gov/explore-funding/programs/igor)Cited in: [Case Studies](appendices/case-studies.html#sec-case-agentic-research-planning)
- [Autonomous chemical research with large language models](https://doi.org/10.1038/s41586-023-06792-0)Daniil A. Boiko; Robert MacKnight; Ben Kline; et al. 2023. Naturejournal articleCited in: [Case Studies](appendices/case-studies.html#sec-case-closed-loop-chemistry)
- [A mobile robotic chemist](https://doi.org/10.1038/s41586-020-2442-2)Benjamin Burger; Phillip M. Maffettone; Vladimir V. Gusev; et al. 2020. Naturejournal articleCited in: [Case Studies](appendices/case-studies.html#sec-case-closed-loop-chemistry)
- [PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences](https://doi.org/10.1039/D3SC04185A)Martin Buttenschoen; Garrett M. Morris; Charlotte M. Deane. 2024. Chemical Sciencejournal articleCited in: [Case Studies](appendices/case-studies.html#sec-case-docking-benchmark)
- [Chai-1: Decoding the molecular interactions of life](https://doi.org/10.1101/2024.10.10.615955)Chai Discovery; Jacques Boitreaud; Jack Dent; et al. 2024preprintCited in: [Case Studies](appendices/case-studies.html#sec-case-alphafold3-access)
- [Accurate proteome-wide missense variant effect prediction with AlphaMissense](https://doi.org/10.1126/science.adg7492)Jun Cheng; Guido Novati; Joshua Pan; et al. 2023. Sciencejournal articleCited in: [Case Studies](appendices/case-studies.html#sec-case-acmg-variant-interpretation)
- [ClinicalTrials.gov, NCT07687459](https://clinicaltrials.gov/study/NCT07687459)Cited in: [Case Studies](appendices/case-studies.html#sec-case-rentosertib-phase-2a)
- [scGPT: toward building a foundation model for single-cell multi-omics using generative AI](https://doi.org/10.1038/s41592-024-02201-0)Haotian Cui; Chloe Wang; Hassaan Maan; et al. 2024. Nature Methodsjournal articleCited in: [Case Studies](appendices/case-studies.html#sec-case-single-cell-foundation-model)
- [Robust deep learning–based protein sequence design using ProteinMPNN](https://doi.org/10.1126/science.add2187)J. Dauparas; I. Anishchenko; N. Bennett; et al. 2022. Sciencejournal articleCited in: [Case Studies](appendices/case-studies.html#sec-case-protein-binder-design)
- [Atomic context-conditioned protein sequence design using LigandMPNN](https://doi.org/10.1038/s41592-025-02626-1)Justas Dauparas; Gyu Rie Lee; Robert Pecoraro; et al. 2025. Nature Methodsjournal articleCited in: [Case Studies](appendices/case-studies.html#sec-case-protein-binder-design)
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- [DeepSynergy: predicting anti-cancer drug synergy with Deep Learning](https://doi.org/10.1093/bioinformatics/btx806)Kristina Preuer; Richard P I Lewis; Sepp Hochreiter; et al. 2017. Bioinformaticsjournal articleCited in: [Glossary](appendices/glossary.html#deepsynergy)
- [cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination](https://doi.org/10.1038/nmeth.4169)Ali Punjani; John L Rubinstein; David J Fleet; et al. 2017. Nature Methodsjournal articleCited in: [Glossary](appendices/glossary.html#cryosparc)
- [Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology](https://doi.org/10.1038/gim.2015.30)Sue Richards; Nazneen Aziz; Sherri Bale; et al. 2015. Genetics in Medicinejournal articleCited in: [Glossary](appendices/glossary.html#acmg-amp)
- [Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences](https://doi.org/10.1073/pnas.2016239118)Alexander Rives; Joshua Meier; Tom Sercu; et al. 2021. Proceedings of the National Academy of Sciencesjournal articleCited in: [Glossary](appendices/glossary.html#esm)
- [Predicting transcriptional outcomes of novel multigene perturbations with GEARS](https://doi.org/10.1038/s41587-023-01905-6)Yusuf Roohani; Kexin Huang; Jure Leskovec. 2023. Nature Biotechnologyjournal articleCited in: [Glossary (passage 1)](appendices/glossary.html#gears); [Glossary (passage 2)](appendices/glossary.html#perturbation-prediction)
- [Virtual Cell Challenge: Toward a Turing test for the virtual cell](https://doi.org/10.1016/j.cell.2025.06.008)Yusuf H. Roohani; Tony J. Hua; Po-Yuan Tung; et al. 2025. Celljournal articleCited in: [Glossary](appendices/glossary.html#virtual-cell-challenge)
- [Learnable latent embeddings for joint behavioural and neural analysis](https://doi.org/10.1038/s41586-023-06031-6)Steffen Schneider; Jin Hwa Lee; Mackenzie Weygandt Mathis. 2023. Naturejournal articleCited in: [Glossary](appendices/glossary.html#cebra)
- [Cellpose: a generalist algorithm for cellular segmentation](https://doi.org/10.1038/s41592-020-01018-x)Carsen Stringer; Tim Wang; Michalis Michaelos; et al. 2020. Nature Methodsjournal articleCited in: [Glossary](appendices/glossary.html#cellpose)
- [Cellpose3: one-click image restoration for improved cellular segmentation](https://doi.org/10.1038/s41592-025-02595-5)Carsen Stringer; Marius Pachitariu. 2025. Nature Methodsjournal articleCited in: [Glossary](appendices/glossary.html#cellpose3)
- [A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles](https://doi.org/10.1016/j.cell.2017.10.049)Aravind Subramanian; Rajiv Narayan; Steven M. Corsello; et al. 2017. Celljournal articleCited in: [Glossary](appendices/glossary.html#connectivity-map)
- [ADMET-AI: a machine learning ADMET platform for evaluation of large-scale chemical libraries](https://doi.org/10.1093/bioinformatics/btae416)Kyle Swanson; Parker Walther; Jeremy Leitz; et al. 2024. Bioinformaticsjournal articleCited in: [Glossary](appendices/glossary.html#admet)
- [The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodies](https://doi.org/10.1038/s41586-025-09442-9)Kyle Swanson; Wesley Wu; Nash L. Bulaong; et al. 2025. Naturejournal articleCited in: [Glossary (passage 1)](appendices/glossary.html#agentic-workflow); [Glossary (passage 2)](appendices/glossary.html#virtual-lab)
- [An autonomous laboratory for the accelerated synthesis of inorganic materials](https://doi.org/10.1038/s41586-023-06734-w)Nathan J. Szymanski; Bernardus Rendy; Yuxing Fei; et al. 2023. Naturejournal articleCited in: [Glossary](appendices/glossary.html#a-lab)
- [Nicheformer: a foundation model for single-cell and spatial omics](https://doi.org/10.1038/s41592-025-02814-z)Alejandro Tejada-Lapuerta; Anna C. Schaar; Robert Gutgesell; et al. 2025. Nature Methodsjournal articleCited in: [Glossary](appendices/glossary.html#nicheformer)
- [Transfer learning enables predictions in network biology](https://doi.org/10.1038/s41586-023-06139-9)Christina V. Theodoris; Ling Xiao; Anant Chopra; et al. 2023. Naturejournal articleCited in: [Glossary (passage 1)](appendices/glossary.html#foundation-model); [Glossary (passage 2)](appendices/glossary.html#geneformer)
- [GUIDE-seq enables genome-wide profiling of off-target cleavage by CRISPR-Cas nucleases](https://doi.org/10.1038/nbt.3117)Shengdar Q Tsai; Zongli Zheng; Nhu T Nguyen; et al. 2014. Nature Biotechnologyjournal articleCited in: [Glossary](appendices/glossary.html#guide-seq)
- [CIRCLE-seq: a highly sensitive in vitro screen for genome-wide CRISPR–Cas9 nuclease off-targets](https://doi.org/10.1038/nmeth.4278)Shengdar Q Tsai; Nhu T Nguyen; Jose Malagon-Lopez; et al. 2017. Nature Methodsjournal articleCited in: [Glossary](appendices/glossary.html#circle-seq)
- [USG Policy for Stopping High-Risk Life Sciences Research](https://www.whitehouse.gov/wp-content/uploads/2026/07/USG-Policy-for-Stopping-High-Risk-Life-Sciences-Research_July-2026.pdf)Cited in: [Glossary](appendices/glossary.html#durc)
- [AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences](https://doi.org/10.1093/nar/gkad1011)Mihaly Varadi; Damian Bertoni; Paulyna Magana; et al. 2023. Nucleic Acids Researchjournal articleCited in: [Glossary](appendices/glossary.html#alphafold-db)
- [A foundation model for clinical-grade computational pathology and rare cancers detection](https://doi.org/10.1038/s41591-024-03141-0)Eugene Vorontsov; Alican Bozkurt; Adam Casson; et al. 2024. Nature Medicinejournal articleCited in: [Glossary](appendices/glossary.html#virchow)
- [SPHIRE-crYOLO is a fast and accurate fully automated particle picker for cryo-EM](https://doi.org/10.1038/s42003-019-0437-z)Thorsten Wagner; Felipe Merino; Markus Stabrin; et al. 2019. Communications Biologyjournal articleCited in: [Glossary](appendices/glossary.html#cryolo)
- [DOME: recommendations for supervised machine learning validation in biology](https://doi.org/10.1038/s41592-021-01205-4)Ian Walsh; Dmytro Fishman; Dario Garcia-Gasulla; et al. 2021. Nature Methodsjournal articleCited in: [Glossary](appendices/glossary.html#dome)
- [De novo design of protein structure and function with RFdiffusion](https://doi.org/10.1038/s41586-023-06415-8)Joseph L. Watson; David Juergens; Nathaniel R. Bennett; et al. 2023. Naturejournal articleCited in: [Glossary (passage 1)](appendices/glossary.html#backbone-generation); [Glossary (passage 2)](appendices/glossary.html#rfdiffusion)
- [The FAIR Guiding Principles for scientific data management and stewardship](https://doi.org/10.1038/sdata.2016.18)Mark D. Wilkinson; Michel Dumontier; IJsbrand Jan Aalbersberg; et al. 2016. Scientific Datajournal articleCited in: [Glossary](appendices/glossary.html#fair-principles)
- [MoleculeNet: a benchmark for molecular machine learning](https://doi.org/10.1039/C7SC02664A)Zhenqin Wu; Bharath Ramsundar; Evan N. Feinberg; et al. 2018. Chemical Sciencejournal articleCited in: [Glossary (passage 1)](appendices/glossary.html#moleculenet); [Glossary (passage 2)](appendices/glossary.html#scaffold-split)
- [wwPDB, 2026](https://www.wwpdb.org/?lang=en)Cited in: [Glossary](appendices/glossary.html#pdb)
- [A whole-slide foundation model for digital pathology from real-world data](https://doi.org/10.1038/s41586-024-07441-w)Hanwen Xu; Naoto Usuyama; Jaspreet Bagga; et al. 2024. Naturejournal articleCited in: [Glossary](appendices/glossary.html#prov-gigapath)
- [Analyzing Learned Molecular Representations for Property Prediction](https://doi.org/10.1021/acs.jcim.9b00237)Kevin Yang; Kyle Swanson; Wengong Jin; et al. 2019. Journal of Chemical Information and Modelingjournal articleCited in: [Glossary](appendices/glossary.html#admet)
- [scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data](https://doi.org/10.1038/s42256-022-00534-z)Fan Yang; Wenchuan Wang; Fang Wang; et al. 2022. Nature Machine Intelligencejournal articleCited in: [Glossary](appendices/glossary.html#scbert)
- [Zambaldi et al., 2024, preprint](https://arxiv.org/abs/2409.08022)Cited in: [Glossary](appendices/glossary.html#alphaproteo)
- [Zdrazil et al., 2024](https://pubmed.ncbi.nlm.nih.gov/37933841/)Cited in: [Glossary](appendices/glossary.html#chembl)
- [CryoDRGN: reconstruction of heterogeneous cryo-EM structures using neural networks](https://doi.org/10.1038/s41592-020-01049-4)Ellen D. Zhong; Tristan Bepler; Bonnie Berger; et al. 2021. Nature Methodsjournal articleCited in: [Glossary](appendices/glossary.html#cryodrgn)

### Model and Dataset Index

#### References

- [Accurate structure prediction of biomolecular interactions with AlphaFold 3](https://doi.org/10.1038/s41586-024-07487-w)Josh Abramson; Jonas Adler; Jack Dunger; et al. 2024. Naturejournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-structure)
- [Addendum: Accurate structure prediction of biomolecular interactions with AlphaFold 3](https://doi.org/10.1038/s41586-024-08416-7)Josh Abramson; Jonas Adler; Jack Dunger; et al. 2024. Naturejournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-structure)
- [Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines](https://doi.org/10.1038/s41592-025-02772-6)Constantin Ahlmann-Eltze; Wolfgang Huber; Simon Anders. 2025. Nature Methodsjournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-critical-evaluations)
- [Protein generation with evolutionary diffusion: sequence is all you need](https://doi.org/10.1101/2023.09.11.556673)Sarah Alamdari; Nitya Thakkar; Rianne van den Berg; et al. 2023preprintCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-design)
- [CellVoyager: AI CompBio agent generates new insights by autonomously analyzing biological data](https://doi.org/10.1038/s41592-026-03029-6)Samuel Alber; Bowen Chen; Eric Sun; et al. 2026. Nature Methodsjournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-automation)
- [AlphaFold Protein Structure Database, 2026](https://alphafold.ebi.ac.uk/about)Cited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-infrastructure)
- [Segment Anything for Microscopy](https://doi.org/10.1038/s41592-024-02580-4)Anwai Archit; Luca Freckmann; Sushmita Nair; et al. 2025. Nature Methodsjournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-imaging)
- [ARPA-H IGoR, 2026](https://arpa-h.gov/explore-funding/programs/igor)Cited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-programs)
- [Effective gene expression prediction from sequence by integrating long-range interactions](https://doi.org/10.1038/s41592-021-01252-x)Žiga Avsec; Vikram Agarwal; Daniel Visentin; et al. 2021. Nature Methodsjournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-genome)
- [Advancing regulatory variant effect prediction with AlphaGenome](https://doi.org/10.1038/s41586-025-10014-0)Žiga Avsec; Natasha Latysheva; Jun Cheng; et al. 2026. Naturejournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-genome)
- [Accurate prediction of protein structures and interactions using a three-track neural network](https://doi.org/10.1126/science.abj8754)Minkyung Baek; Frank DiMaio; Ivan Anishchenko; et al. 2021. Sciencejournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-structure)
- [Synthetic biology open language (SBOL) version 3.0.0](https://doi.org/10.1515/jib-2020-0017)Hasan Baig; Pedro Fontanarrosa; Vishwesh Kulkarni; et al. 2020. Journal of Integrative Bioinformaticsjournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-automation)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://doi.org/10.18653/v1/D19-1371)Iz Beltagy; Kyle Lo; Arman Cohan. 2019. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)proceedings articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-knowledge-literature)
- [A DNA language model based on multispecies alignment predicts the effects of genome-wide variants](https://doi.org/10.1038/s41587-024-02511-w)Gonzalo Benegas; Carlos Albors; Alan J. Aw; et al. 2025. Nature Biotechnologyjournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-genome)
- [Atomically accurate de novo design of antibodies with RFdiffusion](https://doi.org/10.1038/s41586-025-09721-5)Nathaniel R. Bennett; Joseph L. Watson; Robert J. Ragotte; et al. 2025. Naturejournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-design)
- [Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs](https://doi.org/10.1038/s41592-019-0575-8)Tristan Bepler; Andrew Morin; Micah Rapp; et al. 2019. Nature Methodsjournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-imaging)
- [Novae: a graph-based foundation model for spatial transcriptomics data](https://doi.org/10.1038/s41592-025-02899-6)Quentin Blampey; Hakim Benkirane; Nadège Bercovici; et al. 2025. Nature Methodsjournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-cells)
- [Deeper evaluation of a single-cell foundation model](https://doi.org/10.1038/s42256-024-00949-w)Rebecca Boiarsky; Nalini M. Singh; Alejandro Buendia; et al. 2024. Nature Machine Intelligencejournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-critical-evaluations)
- [Autonomous chemical research with large language models](https://doi.org/10.1038/s41586-023-06792-0)Daniil A. Boiko; Robert MacKnight; Ben Kline; et al. 2023. Naturejournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-automation)
- [Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes](https://doi.org/10.1038/nprot.2016.105)Mark-Anthony Bray; Shantanu Singh; Han Han; et al. 2016. Nature Protocolsjournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-cells)
- [Genome modelling and design across all domains of life with Evo 2](https://doi.org/10.1038/s41586-026-10176-5)Garyk Brixi; Matthew G. Durrant; Jerome Ku; et al. 2026. Naturejournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-genome)
- [Learning single-cell perturbation responses using neural optimal transport](https://doi.org/10.1038/s41592-023-01969-x)Charlotte Bunne; Stefan G. Stark; Gabriele Gut; et al. 2023. Nature Methodsjournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-cells)
- [A mobile robotic chemist](https://doi.org/10.1038/s41586-020-2442-2)Benjamin Burger; Phillip M. Maffettone; Vladimir V. Gusev; et al. 2020. Naturejournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-automation)
- [De novo Design of All-atom Biomolecular Interactions with RFdiffusion3](https://doi.org/10.1101/2025.09.18.676967)Jasper Butcher; Rohith Krishna; Raktim Mitra; et al. 2025preprintCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-design)
- [PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences](https://doi.org/10.1039/D3SC04185A)Martin Buttenschoen; Garrett M. Morris; Charlotte M. Deane. 2024. Chemical Sciencejournal articleCited in: [Model and Dataset Index (passage 1)](appendices/model-dataset-index.html#sec-mdi-benchmarks); [Model and Dataset Index (passage 2)](appendices/model-dataset-index.html#sec-mdi-critical-evaluations)
- [CAMEO, 2026](https://www.expasy.org/resources/cameo)Cited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-benchmarks)
- [CellProfiler: image analysis software for identifying and quantifying cell phenotypes](https://doi.org/10.1186/gb-2006-7-10-r100)Anne E Carpenter; Thouis R Jones; Michael R Lamprecht; et al. 2006. Genome Biologyjournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-cells)
- [Chai-1: Decoding the molecular interactions of life](https://doi.org/10.1101/2024.10.10.615955)Chai Discovery; Jacques Boitreaud; Jack Dent; et al. 2024preprintCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-structure)
- [Building a knowledge graph to enable precision medicine](https://doi.org/10.1038/s41597-023-01960-3)Payal Chandak; Kexin Huang; Marinka Zitnik. 2023. Scientific Datajournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-knowledge-literature)
- [Three million images and morphological profiles of cells treated with matched chemical and genetic perturbations](https://doi.org/10.1038/s41592-024-02241-6)Srinivas Niranj Chandrasekaran; Beth A. Cimini; Amy Goodale; et al. 2024. Nature Methodsjournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-cells)
- [Morphological map of under- and overexpression of genes in human cells](https://doi.org/10.1038/s41592-025-02753-9)Srinivas Niranj Chandrasekaran; Eric Alix; John Arevalo; et al. 2025. Nature Methodsjournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-cells)
- [Digital Twins in Pharmaceutical and Biopharmaceutical Manufacturing: A Literature Review](https://doi.org/10.3390/pr8091088)Yingjie Chen; Ou Yang; Chaitanya Sampat; et al. 2020. Processesjournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-biomanufacturing)
- [Towards a general-purpose foundation model for computational pathology](https://doi.org/10.1038/s41591-024-02857-3)Richard J. Chen; Tong Ding; Ming Y. Lu; et al. 2024. Nature Medicinejournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-imaging)
- [Accurate proteome-wide missense variant effect prediction with AlphaMissense](https://doi.org/10.1126/science.adg7492)Jun Cheng; Guido Novati; Joshua Pan; et al. 2023. Sciencejournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-genome)
- [In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images](https://doi.org/10.1016/j.cell.2018.03.040)Eric M. Christiansen; Samuel J. Yang; D. Michael Ando; et al. 2018. Celljournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-imaging)
- [Corso et al., 2023](https://openreview.net/forum?id=kKF8_K-mBbS)Cited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-small-molecules)
- [scGPT: toward building a foundation model for single-cell multi-omics using generative AI](https://doi.org/10.1038/s41592-024-02201-0)Haotian Cui; Chloe Wang; Hassaan Maan; et al. 2024. Nature Methodsjournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-cells)
- [Cytiva, 2026](https://www.cytivalifesciences.com/en/us/)Cited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-biomanufacturing)
- [CZI](https://virtualcellmodels.cziscience.com/)Cited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-programs)
- [CZI Cell Science Program et al., 2025](https://doi.org/10.1093/nar/gkae1142)Cited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-cells)
- [Nucleotide Transformer: building and evaluating robust foundation models for human genomics](https://doi.org/10.1038/s41592-024-02523-z)Hugo Dalla-Torre; Liam Gonzalez; Javier Mendoza-Revilla; et al. 2024. Nature Methodsjournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-genome)
- [Datavant, 2026](https://www.datavant.com/)Cited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-rwe-biomarkers)
- [Robust deep learning–based protein sequence design using ProteinMPNN](https://doi.org/10.1126/science.add2187)J. Dauparas; I. Anishchenko; N. Bennett; et al. 2022. Sciencejournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-design)
- [Atomic context-conditioned protein sequence design using LigandMPNN](https://doi.org/10.1038/s41592-025-02626-1)Justas Dauparas; Gyu Rie Lee; Robert Pecoraro; et al. 2025. Nature Methodsjournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-design)
- [A multimodal conversational agent for DNA, RNA and protein tasks](https://doi.org/10.1038/s42256-025-01047-1)Bernardo P. de Almeida; Guillaume Richard; Hugo Dalla-Torre; et al. 2025. Nature Machine Intelligencejournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-genome)
- [A multimodal whole-slide foundation model for pathology](https://doi.org/10.1038/s41591-025-03982-3)Tong Ding; Sophia J. Wagner; Andrew H. Song; et al. 2025. Nature Medicinejournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-imaging)
- [ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning](https://doi.org/10.1109/TPAMI.2021.3095381)Ahmed Elnaggar; Michael Heinzinger; Christian Dallago; et al. 2022. IEEE Transactions on Pattern Analysis and Machine Intelligencejournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-protein-lm)
- [EMA, 2024](https://www.ema.europa.eu/en/use-artificial-intelligence-ai-medicinal-product-lifecycle)Cited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-programs)
- [ESM Metagenomic Atlas, 2026](https://esmatlas.com/about)Cited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-infrastructure)
- [Protein complex prediction with AlphaFold-Multimer](https://doi.org/10.1101/2021.10.04.463034)Richard Evans; Michael O’Neill; Alexander Pritzel; et al. 2021preprintCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-structure)
- [FDA, 2004](https://www.fda.gov/regulatory-information/search-fda-guidance-documents/pat-framework-innovative-pharmaceutical-development-manufacturing-and-quality-assurance)Cited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-programs)
- [FDA, 2026](https://www.fda.gov/drugs/biomarker-qualification-program/qualifying-biomarker-through-biomarker-qualification-program)Cited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-programs)
- [FDA, 2026](https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development)Cited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-programs)
- [FDA, 2026](https://www.fda.gov/science-research/science-and-research-special-topics/real-world-evidence)Cited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-programs)
- [Flatiron Health, 2026](https://www.flatiron.com/)Cited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-rwe-biomarkers)
- [Orthrus: toward evolutionary and functional RNA foundation models](https://doi.org/10.1038/s41592-026-03064-3)Philip Fradkin; Ruian “Ian” Shi; Taykhoom Dalal; et al. 2026. Nature Methodsjournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-genome)
- [Multimodal pooled Perturb-CITE-seq screens in patient models define mechanisms of cancer immune evasion](https://doi.org/10.1038/s41588-021-00779-1)Chris J. Frangieh; Johannes C. Melms; Pratiksha I. Thakore; et al. 2021. Nature Geneticsjournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-cells)
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- [Transfer learning enables predictions in network biology](https://doi.org/10.1038/s41586-023-06139-9)Christina V. Theodoris; Ling Xiao; Anant Chopra; et al. 2023. Naturejournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-cells)
- [AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading](https://doi.org/10.1002/jcc.21334)Oleg Trott; Arthur J. Olson. 2009. Journal of Computational Chemistryjournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-small-molecules)
- [AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences](https://doi.org/10.1093/nar/gkad1011)Mihaly Varadi; Damian Bertoni; Paulyna Magana; et al. 2023. Nucleic Acids Researchjournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-structure)
- [A foundation model for clinical-grade computational pathology and rare cancers detection](https://doi.org/10.1038/s41591-024-03141-0)Eugene Vorontsov; Alican Bozkurt; Adam Casson; et al. 2024. Nature Medicinejournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-imaging)
- [SPHIRE-crYOLO is a fast and accurate fully automated particle picker for cryo-EM](https://doi.org/10.1038/s42003-019-0437-z)Thorsten Wagner; Felipe Merino; Markus Stabrin; et al. 2019. Communications Biologyjournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-imaging)
- [DOME: recommendations for supervised machine learning validation in biology](https://doi.org/10.1038/s41592-021-01205-4)Ian Walsh; Dmytro Fishman; Dario Garcia-Gasulla; et al. 2021. Nature Methodsjournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-benchmarks)
- [A pathology foundation model for cancer diagnosis and prognosis prediction](https://doi.org/10.1038/s41586-024-07894-z)Xiyue Wang; Junhan Zhao; Eliana Marostica; et al. 2024. Naturejournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-imaging)
- [De novo design of protein structure and function with RFdiffusion](https://doi.org/10.1038/s41586-023-06415-8)Joseph L. Watson; David Juergens; Nathaniel R. Bennett; et al. 2023. Naturejournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-design)
- [Content-aware image restoration: pushing the limits of fluorescence microscopy](https://doi.org/10.1038/s41592-018-0216-7)Martin Weigert; Uwe Schmidt; Tobias Boothe; et al. 2018. Nature Methodsjournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-imaging)
- [PyLabRobot: An open-source, hardware-agnostic interface for liquid-handling robots and accessories](https://doi.org/10.1016/j.device.2023.100111)Rick P. Wierenga; Stefan M. Golas; Wilson Ho; et al. 2023. Devicejournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-automation)
- [Boltz-1 Democratizing Biomolecular Interaction Modeling](https://doi.org/10.1101/2024.11.19.624167)Jeremy Wohlwend; Gabriele Corso; Saro Passaro; et al. 2024preprintCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-structure)
- [MoleculeNet: a benchmark for molecular machine learning](https://doi.org/10.1039/C7SC02664A)Zhenqin Wu; Bharath Ramsundar; Evan N. Feinberg; et al. 2018. Chemical Sciencejournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-benchmarks)
- [High-resolution de novo structure prediction from primary sequence](https://doi.org/10.1101/2022.07.21.500999)Ruidong Wu; Fan Ding; Rui Wang; et al. 2022preprintCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-structure)
- [wwPDB, 2026](https://www.wwpdb.org/?lang=en)Cited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-infrastructure)
- [A vision–language foundation model for precision oncology](https://doi.org/10.1038/s41586-024-08378-w)Jinxi Xiang; Xiyue Wang; Xiaoming Zhang; et al. 2025. Naturejournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-imaging)
- [A whole-slide foundation model for digital pathology from real-world data](https://doi.org/10.1038/s41586-024-07441-w)Hanwen Xu; Naoto Usuyama; Jaspreet Bagga; et al. 2024. Naturejournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-imaging)
- [A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial](https://doi.org/10.1038/s41591-025-03743-2)Zuojun Xu; Feng Ren; Ping Wang; et al. 2025. Nature Medicinejournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-small-molecules)
- [Analyzing Learned Molecular Representations for Property Prediction](https://doi.org/10.1021/acs.jcim.9b00237)Kevin Yang; Kyle Swanson; Wengong Jin; et al. 2019. Journal of Chemical Information and Modelingjournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-small-molecules)
- [scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data](https://doi.org/10.1038/s42256-022-00534-z)Fan Yang; Wenchuan Wang; Fang Wang; et al. 2022. Nature Machine Intelligencejournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-cells)
- [Zambaldi et al., 2024, preprint](https://arxiv.org/abs/2409.08022)Cited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-design)
- [Zdrazil et al., 2024](https://pubmed.ncbi.nlm.nih.gov/37933841/)Cited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-infrastructure)
- [A disease-specific language model for variant pathogenicity in cardiac and regulatory genomics](https://doi.org/10.1038/s42256-025-01016-8)Huixin Zhan; Jason H. Moore; Zijun Zhang. 2025. Nature Machine Intelligencejournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-genome)
- [CryoDRGN: reconstruction of heterogeneous cryo-EM structures using neural networks](https://doi.org/10.1038/s41592-020-01049-4)Ellen D. Zhong; Tristan Bepler; Bonnie Berger; et al. 2021. Nature Methodsjournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-imaging)
- [Predicting effects of noncoding variants with deep learning–based sequence model](https://doi.org/10.1038/nmeth.3547)Jian Zhou; Olga G Troyanskaya. 2015. Nature Methodsjournal articleCited in: [Model and Dataset Index](appendices/model-dataset-index.html#sec-mdi-genome)

### Course Syllabus Template

#### References

- [DOI: 10.5281/zenodo.22073136](https://doi.org/10.5281/zenodo.22073136)Cited in: [Course Syllabus Template](appendices/course-syllabus.html#sec-course-syllabus-overview)

### Teaching with the Life Sciences AI Handbook

#### References

- [Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines](https://doi.org/10.1038/s41592-025-02772-6)Constantin Ahlmann-Eltze; Wolfgang Huber; Simon Anders. 2025. Nature Methodsjournal articleCited in: [Teaching with the Life Sciences AI Handbook](teaching.html#sec-teaching-starter-papers)
- [Autonomous chemical research with large language models](https://doi.org/10.1038/s41586-023-06792-0)Daniil A. Boiko; Robert MacKnight; Ben Kline; et al. 2023. Naturejournal articleCited in: [Teaching with the Life Sciences AI Handbook](teaching.html#sec-teaching-starter-papers)
- [Brooksbank et al., 2024](https://doi.org/10.1093/bioadv/vbae166)Cited in: [Teaching with the Life Sciences AI Handbook](teaching.html#sec-teaching-competencies)
- [Highly accurate protein structure prediction with AlphaFold](https://doi.org/10.1038/s41586-021-03819-2)John Jumper; Richard Evans; Alexander Pritzel; et al. 2021. Naturejournal articleCited in: [Teaching with the Life Sciences AI Handbook](teaching.html#sec-teaching-starter-papers)
- [Evolutionary-scale prediction of atomic-level protein structure with a language model](https://doi.org/10.1126/science.ade2574)Zeming Lin; Halil Akin; Roshan Rao; et al. 2023. Sciencejournal articleCited in: [Teaching with the Life Sciences AI Handbook](teaching.html#sec-teaching-starter-papers)
- [Transfer learning enables predictions in network biology](https://doi.org/10.1038/s41586-023-06139-9)Christina V. Theodoris; Ling Xiao; Anant Chopra; et al. 2023. Naturejournal articleCited in: [Teaching with the Life Sciences AI Handbook](teaching.html#sec-teaching-starter-papers)
- [De novo design of protein structure and function with RFdiffusion](https://doi.org/10.1038/s41586-023-06415-8)Joseph L. Watson; David Juergens; Nathaniel R. Bennett; et al. 2023. Naturejournal articleCited in: [Teaching with the Life Sciences AI Handbook](teaching.html#sec-teaching-starter-papers)

### License

#### References

- [DOI: 10.5281/zenodo.22073136](https://doi.org/10.5281/zenodo.22073136)Cited in: [License](LICENSE.html)