Appendix I — 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 results
- 100 Days MissionCited in: mRNA, RNA, and Vaccine Design
- Accurate structure prediction of biomolecular interactions with AlphaFold 3Josh Abramson; Jonas Adler; Jack Dunger; et al. 2024. Naturejournal articleCited in: Scope and Limitations; AI for the Life Sciences; History of AI in the Life Sciences (passage 1); History of AI in the Life Sciences (passage 2); Foundation Models for Biology; Evaluation Principles for Life Sciences AI; Protein Structure Prediction (passage 1); Protein Structure Prediction (passage 2); Protein Structure Prediction (passage 3); Protein Structure Prediction (passage 4); Protein Structure Prediction (passage 5); Protein Design and Engineering; Antibody and Biologic Design (passage 1); Antibody and Biologic Design (passage 2); mRNA, RNA, and Vaccine Design (passage 1); mRNA, RNA, and Vaccine Design (passage 2); Toolkit for AI-Augmented Bio Research; Information Hazards in Capability Research; Emerging Frontiers in AI for the Life Sciences; Quick Reference: All Chapter Summaries (passage 1); Quick Reference: All Chapter Summaries (passage 2); Case Studies; Glossary (passage 1); Glossary (passage 2); Model and Dataset IndexRecommended in: References
- Addendum: Accurate structure prediction of biomolecular interactions with AlphaFold 3Josh Abramson; Jonas Adler; Jack Dunger; et al. 2024. Naturejournal articleCited in: AI for the Life Sciences; History of AI in the Life Sciences; Protein Structure Prediction; Toolkit for AI-Augmented Bio Research; Quick Reference: All Chapter Summaries (passage 1); Quick Reference: All Chapter Summaries (passage 2); Case Studies; Model and Dataset Index
- AI and the Future of Medical Countermeasures to Protect Against Biological ThreatsAmesh A Adalja; Jaspreet Pannu; Thomas V Inglesby. 2026. Open Forum Infectious Diseasesjournal articleCited in: mRNA, RNA, and Vaccine Design
- A Multiplexed Single-Cell CRISPR Screening Platform Enables Systematic Dissection of the Unfolded Protein ResponseBritt Adamson; Thomas M. Norman; Marco Jost; et al. 2016. Celljournal articleCited in: Perturbation Prediction and Virtual CellsRecommended in: References
- Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselinesConstantin Ahlmann-Eltze; Wolfgang Huber; Simon Anders. 2025. Nature Methodsjournal articleCited in: Preface; Foundation Models for Biology; Single-Cell Foundation Models; Perturbation Prediction and Virtual Cells; Translational Evidence and Failure Modes (passage 1); Translational Evidence and Failure Modes (passage 2); Quick Reference: All Chapter Summaries (passage 1); Quick Reference: All Chapter Summaries (passage 2); Quick Reference: All Chapter Summaries (passage 3); Case Studies (passage 1); Case Studies (passage 2); Glossary; Model and Dataset IndexRecommended in: References
- Ahmad et al., 2022, preprintCited in: Small Molecule Generation and ADMETRecommended in: References
- SCENIC: single-cell regulatory network inference and clusteringSara Aibar; Carmen Bravo González-Blas; Thomas Moerman; et al. 2017. Nature Methodsjournal articleCited in: Systems Biology and Multiscale Modeling (passage 1); Systems Biology and Multiscale Modeling (passage 2); Systems Biology and Multiscale Modeling (passage 3)
- Aiforia, 2026Cited in: Histopathology AIRecommended in: References
- Enhancing randomized clinical trials with digital twinsHossein Akbarialiabad; Amirmohammad Pasdar; Dédée F. Murrell; et al. 2025. npj Systems Biology and Applicationsjournal articleCited in: Virtual Organisms and Digital BiologyRecommended in: References
- A structural biology community assessment of AlphaFold2 applicationsMehmet Akdel; Douglas E. V. Pires; Eduard Porta Pardo; et al. 2022. Nature Structural & Molecular Biologyjournal articleCited in: History of AI in the Life Sciences; Protein Structure Prediction
- Automated synthetic cell-based screening for designed proteins with emergent functionsKareem Al Nahas; Béla P. Frohn; Aleksandra Šakanović; et al. 2026. Nature Communicationsjournal articleCited in: Synthetic Biology Design Tools
- Protein generation with evolutionary diffusion: sequence is all you needSarah Alamdari; Nitya Thakkar; Rianne van den Berg; et al. 2023preprintCited in: Protein Design and Engineering; Model and Dataset IndexRecommended in: References
- CellVoyager: AI CompBio agent generates new insights by autonomously analyzing biological dataSamuel Alber; Bowen Chen; Eric Sun; et al. 2026. Nature Methodsjournal articleCited in: Agentic Science Workflows (passage 1); Agentic Science Workflows (passage 2); Quick Reference: All Chapter Summaries; Model and Dataset IndexRecommended in: References
- Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learningBabak Alipanahi; Andrew Delong; Matthew T Weirauch; et al. 2015. Nature Biotechnologyjournal articleCited in: History of AI in the Life Sciences (passage 1); History of AI in the Life Sciences (passage 2)
- AlphaFold Protein Structure Database, 2026Cited in: Biological Data Infrastructure; Reproducibility and Open Science; Case Studies; Model and Dataset IndexRecommended in: References
- Basic local alignment search toolStephen F. Altschul; Warren Gish; Webb Miller; et al. 1990. Journal of Molecular Biologyjournal articleCited in: History of AI in the Life Sciences (passage 1); History of AI in the Life Sciences (passage 2); Toolkit for AI-Augmented Bio Research
- Anthropic, 2026Cited in: Foundation Models for Biology
- Anthropic, 2026Cited in: Self-Driving Laboratories
- Anthropic, 2026, system cardCited in: Benchmarks for Bio AI
- Anthropic, August 2026Cited in: Protein Design and EngineeringRecommended in: References (passage 1); References (passage 2)
- Anthropic, June 2026Cited in: AI for the Life Sciences; Toolkit for AI-Augmented Bio Research; Workforce, Compute, and Institutional ReadinessRecommended in: References
- Anthropic, October 2025Cited in: AI for the Life SciencesRecommended in: References
- Search-and-replace genome editing without double-strand breaks or donor DNAAndrew V. Anzalone; Peyton B. Randolph; Jessie R. Davis; et al. 2019. Naturejournal articleCited in: Cell and Gene Therapy AI; Glossary
- ApexGOCited in: Small Molecule Generation and ADMET
- Field high-throughput phenotyping: the new crop breeding frontierJosé Luis Araus; Jill E. Cairns. 2014. Trends in Plant Sciencejournal articleCited in: Plant, Crop, and Agricultural AI (passage 1)Recommended in: Plant, Crop, and Agricultural AI (passage 2)
- Standards for distribution models in biodiversity assessmentsMiguel B. Araújo; Robert P. Anderson; A. Márcia Barbosa; et al. 2019. Science Advancesjournal articleCited in: Environmental and Ecological AI (passage 1)Recommended in: Environmental and Ecological AI (passage 2)
- Arc Institute, 2025Cited in: Perturbation Prediction and Virtual Cells (passage 1); Perturbation Prediction and Virtual Cells (passage 2); Benchmarks for Bio AI; Emerging Frontiers in AI for the Life SciencesRecommended in: References
- Segment Anything for MicroscopyAnwai Archit; Luca Freckmann; Sushmita Nair; et al. 2025. Nature Methodsjournal articleCited in: Microscopy and Cryo-EM AI; Glossary; Model and Dataset IndexRecommended in: References
- Evaluating batch correction methods for image-based cell profilingJohn Arevalo; Ellen Su; Jessica D. Ewald; et al. 2024. Nature Communicationsjournal articleCited in: Cell Painting and Image-Based Phenotyping (passage 1); Cell Painting and Image-Based Phenotyping (passage 2)
- MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell dataRicard Argelaguet; Damien Arnol; Danila Bredikhin; et al. 2020. Genome Biologyjournal articleCited in: Microbiome and Multi-Omics AI
- ARPA-H IGoR, 2026Cited in: Scope and Limitations; Robotic Lab Automation and Cloud Labs; Agentic Science Workflows (passage 1); Agentic Science Workflows (passage 2); Reproducibility and Open Science; Information Hazards in Capability Research; Workforce, Compute, and Institutional Readiness; Quick Reference: All Chapter Summaries (passage 1); Quick Reference: All Chapter Summaries (passage 2); Case Studies; Glossary; Model and Dataset IndexRecommended in: References
- ARPA-H programs pageCited in: AI for the Life Sciences
- The promise and peril of chemical probesCheryl H Arrowsmith; James E Audia; Christopher Austin; et al. 2015. Nature Chemical Biologyjournal articleCited in: Chemical Biology and Target Engagement
- OpenEvidence clinical question-answering platform: systematic review of early evaluationsYaara Artsi; Vera Sorin; Benjamin S. Glicksberg; et al. 2026. npj Digital Medicinejournal articleCited in: Knowledge Graphs and Literature AI
- Effective gene expression prediction from sequence by integrating long-range interactionsŽiga Avsec; Vikram Agarwal; Daniel Visentin; et al. 2021. Nature Methodsjournal articleCited in: Nucleic Acid and Genome Models (passage 1); Nucleic Acid and Genome Models (passage 2); Variant Effect Prediction; Glossary; Model and Dataset IndexRecommended in: References
- Advancing regulatory variant effect prediction with AlphaGenomeŽiga Avsec; Natasha Latysheva; Jun Cheng; et al. 2026. Naturejournal articleCited in: Nucleic Acid and Genome Models (passage 1); Nucleic Acid and Genome Models (passage 2); Nucleic Acid and Genome Models (passage 3); Variant Effect Prediction; mRNA, RNA, and Vaccine Design; Quick Reference: All Chapter Summaries; Glossary; Model and Dataset IndexRecommended in: References
- Azabou et al., 2023Cited in: Neuroscience AI and Brain Foundation Models (passage 1); Neuroscience AI and Brain Foundation Models (passage 2); Neuroscience AI and Brain Foundation Models (passage 3)
- Gene regulatory network inference in the era of single-cell multi-omicsPau Badia-i-Mompel; Lorna Wessels; Sophia Müller-Dott; et al. 2023. Nature Reviews Geneticsjournal articleCited in: Systems Biology and Multiscale Modeling; Quick Reference: All Chapter SummariesRecommended in: References
- Accurate prediction of protein structures and interactions using a three-track neural networkMinkyung Baek; Frank DiMaio; Ivan Anishchenko; et al. 2021. Sciencejournal articleCited in: History of AI in the Life Sciences; Protein Structure Prediction; Glossary; Model and Dataset IndexRecommended in: References
- Synthetic biology open language (SBOL) version 3.0.0Hasan Baig; Pedro Fontanarrosa; Vishwesh Kulkarni; et al. 2020. Journal of Integrative Bioinformaticsjournal articleCited in: Robotic Lab Automation and Cloud Labs; Model and Dataset IndexRecommended in: References
- Protein design meets biosecurityDavid Baker; George Church. 2024. Sciencejournal articleCited in: Synthetic Biology Design Tools; Information Hazards in Capability ResearchRecommended in: References
- DNA methylation aging clocks: challenges and recommendationsChristopher G. Bell; Robert Lowe; Peter D. Adams; et al. 2019. Genome Biologyjournal articleCited in: Aging and Longevity Biology AI; Quick Reference: All Chapter SummariesRecommended in: References
- DunedinPACE, a DNA methylation biomarker of the pace of agingDaniel W Belsky; Avshalom Caspi; David L Corcoran; et al. 2022. eLifejournal articleCited in: Aging and Longevity Biology AI (passage 1); Aging and Longevity Biology AI (passage 2); Aging and Longevity Biology AI (passage 3); Aging and Longevity Biology AI (passage 4)
- SciBERT: A Pretrained Language Model for Scientific TextIz 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; Glossary; Model and Dataset IndexRecommended in: References
- A DNA language model based on multispecies alignment predicts the effects of genome-wide variantsGonzalo Benegas; Carlos Albors; Alan J. Aw; et al. 2025. Nature Biotechnologyjournal articleCited in: Variant Effect Prediction; Quick Reference: All Chapter Summaries; Glossary; Model and Dataset IndexRecommended in: References
- Atomically accurate de novo design of antibodies with RFdiffusionNathaniel R. Bennett; Joseph L. Watson; Robert J. Ragotte; et al. 2025. Naturejournal articleCited in: Protein Design and Engineering (passage 1); Protein Design and Engineering (passage 2); Antibody and Biologic Design (passage 1); Antibody and Biologic Design (passage 2); Quick Reference: All Chapter Summaries; Glossary; Model and Dataset IndexRecommended in: References
- Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographsTristan Bepler; Andrew Morin; Micah Rapp; et al. 2019. Nature Methodsjournal articleCited in: Microscopy and Cryo-EM AI; Glossary; Model and Dataset IndexRecommended in: References
- The Protein Data BankH. M. Berman. 2000. Nucleic Acids Researchjournal articleCited in: Biological Data InfrastructureRecommended in: References
- Deep learning and alignment of spatially resolved single-cell transcriptomes with TangramTommaso Biancalani; Gabriele Scalia; Lorenzo Buffoni; et al. 2021. Nature Methodsjournal articleCited in: Spatial Omics and Tissue Models
- BICAN, NIH BRAIN InitiativeCited in: Neuroscience AI and Brain Foundation Models
- BICAN, NIH BRAIN InitiativeCited in: Neuroscience AI and Brain Foundation Models (passage 1); Neuroscience AI and Brain Foundation Models (passage 2)
- Biomarkers Definitions Working Group, 2001Cited in: Real-World Evidence and Biomarker AI
- BirdNET publicationsCited in: Environmental and Ecological AI
- Novae: a graph-based foundation model for spatial transcriptomics dataQuentin Blampey; Hakim Benkirane; Nadège Bercovici; et al. 2025. Nature Methodsjournal articleCited in: Spatial Omics and Tissue Models (passage 1); Spatial Omics and Tissue Models (passage 2); Quick Reference: All Chapter Summaries; Glossary; Model and Dataset IndexRecommended in: References
- REINVENT 2.0: An AI Tool for De Novo Drug DesignThomas Blaschke; Josep Arús-Pous; Hongming Chen; et al. 2020. Journal of Chemical Information and Modelingjournal articleCited in: Small Molecule Generation and ADMETRecommended in: References
- AI and biosecurity: The need for governanceDoni Bloomfield; Jaspreet Pannu; Alex W. Zhu; et al. 2024. Sciencejournal articleCited in: Information Hazards in Capability ResearchRecommended in: References
- The Unified Medical Language System (UMLS): integrating biomedical terminologyO. Bodenreider. 2004. Nucleic Acids Researchjournal articleCited in: Knowledge Graphs and Literature AI
- Deeper evaluation of a single-cell foundation modelRebecca Boiarsky; Nalini M. Singh; Alejandro Buendia; et al. 2024. Nature Machine Intelligencejournal articleCited in: Preface; Foundation Models for Biology; Single-Cell Foundation Models; Quick Reference: All Chapter Summaries; Model and Dataset IndexRecommended in: References
- Autonomous chemical research with large language modelsDaniil A. Boiko; Robert MacKnight; Ben Kline; et al. 2023. Naturejournal articleCited in: Scope and Limitations; AI for the Life Sciences (passage 1); AI for the Life Sciences (passage 2); Self-Driving Laboratories (passage 1); Self-Driving Laboratories (passage 2); Self-Driving Laboratories (passage 3); Agentic Science Workflows (passage 1); Agentic Science Workflows (passage 2); Emerging Frontiers in AI for the Life Sciences; Quick Reference: All Chapter Summaries (passage 1); Quick Reference: All Chapter Summaries (passage 2); Case Studies; Glossary (passage 1); Glossary (passage 2); Model and Dataset IndexRecommended in: References
- STARD 2015: an updated list of essential items for reporting diagnostic accuracy studiesPatrick M Bossuyt; Johannes B Reitsma; David E Bruns; et al. 2015. BMJjournal articleCited in: Diagnostics and Biomarker Translation
- Brain-ScoreCited in: Neuroscience AI and Brain Foundation Models
- BrAPICited in: Plant, Crop, and Agricultural AI (passage 1); Plant, Crop, and Agricultural AI (passage 2)
- Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyesMark-Anthony Bray; Shantanu Singh; Han Han; et al. 2016. Nature Protocolsjournal articleCited in: Cell Painting and Image-Based Phenotyping (passage 1); Cell Painting and Image-Based Phenotyping (passage 2); Chemical Biology and Target Engagement (passage 1); Chemical Biology and Target Engagement (passage 2); Quick Reference: All Chapter Summaries; Glossary; Model and Dataset IndexRecommended in: References
- BreedbaseRecommended in: Plant, Crop, and Agricultural AI
- Breedbase AboutCited in: Plant, Crop, and Agricultural AI
- Bridge2AI ConsortiumCited in: AI for the Life SciencesRecommended in: References
- Genome modelling and design across all domains of life with Evo 2Garyk Brixi; Matthew G. Durrant; Jerome Ku; et al. 2026. Naturejournal articleCited in: AI for the Life Sciences; Foundation Models for Biology (passage 1); Foundation Models for Biology (passage 2); Nucleic Acid and Genome Models (passage 1); Nucleic Acid and Genome Models (passage 2); Synthetic Biology Design Tools; Quick Reference: All Chapter Summaries; Glossary; Model and Dataset IndexRecommended in: References
- Brnich et al., 2020Cited in: Variant Effect Prediction
- AI-enabled discovery and biochemical optimization of minibinders targeting cancer cell-surface proteinsBianca Broske; Benjamin A. McEnroe; Sophie C. Frechen; et al. 2026. Nature Communicationsjournal articleCited in: Protein Design and Engineering
- NicheNet: modeling intercellular communication by linking ligands to target genesRobin Browaeys; Wouter Saelens; Yvan Saeys. 2019. Nature Methodsjournal articleCited in: Systems Biology and Multiscale Modeling
- GuacaMol: Benchmarking Models for de Novo Molecular DesignNathan Brown; Marco Fiscato; Marwin H.S. Segler; et al. 2019. Journal of Chemical Information and Modelingjournal articleCited in: Evaluation Principles for Life Sciences AI; Small Molecule Generation and ADMET; Toolkit for AI-Augmented Bio Research; Benchmarks for Bio AI
- Deep diversification of an AAV capsid protein by machine learningDrew H. Bryant; Ali Bashir; Sam Sinai; et al. 2021. Nature Biotechnologyjournal articleCited in: Cell and Gene Therapy AI (passage 1); Cell and Gene Therapy AI (passage 2)
- Improved prediction of protein-protein interactions using AlphaFold2Patrick Bryant; Gabriele Pozzati; Arne Elofsson. 2022. Nature Communicationsjournal articleCited in: Protein Structure Prediction
- Fast and sensitive protein alignment using DIAMONDBenjamin Buchfink; Chao Xie; Daniel H Huson. 2014. Nature Methodsjournal articleCited in: Toolkit for AI-Augmented Bio Research
- Open Targets Platform: facilitating therapeutic hypotheses building in drug discoveryAnnalisa Buniello; Daniel Suveges; Carlos Cruz-Castillo; et al. 2024. Nucleic Acids Researchjournal articleCited in: Target Identification and Prioritization; Quick Reference: All Chapter SummariesRecommended in: References
- Learning single-cell perturbation responses using neural optimal transportCharlotte Bunne; Stefan G. Stark; Gabriele Gut; et al. 2023. Nature Methodsjournal articleCited in: Perturbation Prediction and Virtual Cells; Glossary; Model and Dataset IndexRecommended in: References
- How to build the virtual cell with artificial intelligence: Priorities and opportunitiesCharlotte Bunne; Yusuf Roohani; Yanay Rosen; et al. 2024. Celljournal articleCited in: Perturbation Prediction and Virtual Cells; Virtual Organisms and Digital Biology (passage 1); Virtual Organisms and Digital Biology (passage 2); Emerging Frontiers in AI for the Life Sciences; Quick Reference: All Chapter SummariesRecommended in: References (passage 1); References (passage 2)
- Real‐world evidence to support regulatory decision‐making for medicines: Considerations for external control armsMehmet Burcu; Nancy A. Dreyer; Jessica M. Franklin; et al. 2020. Pharmacoepidemiology and Drug Safetyjournal articleRecommended in: References
- Optknock: A bilevel programming framework for identifying gene knockout strategies for microbial strain optimizationAnthony P. Burgard; Priti Pharkya; Costas D. Maranas. 2003. Biotechnology and Bioengineeringjournal articleCited in: Synthetic Biology Design Tools
- Prediction of complete gene structures in human genomic DNAChris Burge; Samuel Karlin. 1997. Journal of Molecular Biologyjournal articleCited in: History of AI in the Life Sciences (passage 1); History of AI in the Life Sciences (passage 2)
- A mobile robotic chemistBenjamin Burger; Phillip M. Maffettone; Vladimir V. Gusev; et al. 2020. Naturejournal articleCited in: Self-Driving Laboratories (passage 1); Self-Driving Laboratories (passage 2); Robotic Lab Automation and Cloud Labs (passage 1); Robotic Lab Automation and Cloud Labs (passage 2); Emerging Frontiers in AI for the Life Sciences; Quick Reference: All Chapter Summaries; Case Studies; Model and Dataset IndexRecommended in: References
- De novo Design of All-atom Biomolecular Interactions with RFdiffusion3Jasper Butcher; Rohith Krishna; Raktim Mitra; et al. 2025preprintCited in: Protein Design and Engineering; Model and Dataset IndexRecommended in: References
- PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequencesMartin Buttenschoen; Garrett M. Morris; Charlotte M. Deane. 2024. Chemical Sciencejournal articleCited in: Preface; How to Read This Handbook; Scope and Limitations; Small Molecule Generation and ADMET; Translational Evidence and Failure Modes (passage 1); Translational Evidence and Failure Modes (passage 2); Benchmarks for Bio AI (passage 1); Benchmarks for Bio AI (passage 2); Quick Reference: All Chapter Summaries (passage 1); Quick Reference: All Chapter Summaries (passage 2); Quick Reference: All Chapter Summaries (passage 3); Case Studies; Glossary; Model and Dataset Index (passage 1); Model and Dataset Index (passage 2)Recommended in: References
- Buttenschoen et al., 2024Cited in: Evaluation Principles for Life Sciences AI
- Data-analysis strategies for image-based cell profilingJuan C Caicedo; Sam Cooper; Florian Heigwer; et al. 2017. Nature Methodsjournal articleCited in: Cell Painting and Image-Based Phenotyping
- Real-world validation of a multimodal LLM-powered pipeline for high-accuracy clinical trial patient matchingAnatole Callies; Quentin Bodinier; Philippe Ravaud; et al. 2025. Communications Medicinejournal articleCited in: Clinical Trial AI for Translational Research
- CAMEO, 2026Cited in: Evaluation Principles for Life Sciences AI; Quick Reference: All Chapter Summaries; Model and Dataset IndexRecommended in: References
- Clinical-grade computational pathology using weakly supervised deep learning on whole slide imagesGabriele Campanella; Matthew G. Hanna; Luke Geneslaw; et al. 2019. Nature Medicinejournal articleCited in: Histopathology AI
- Real-world deployment of a fine-tuned pathology foundation model for lung cancer biomarker detectionGabriele Campanella; Neeraj Kumar; Swaraj Nanda; et al. 2025. Nature Medicinejournal articleCited in: Histopathology AI
- CAMPERCited in: Small Molecule Generation and ADMET
- The past, present and future of self-driving laboratoriesRichard B. Canty; Milad Abolhasani. 2026. Nature Reviews Chemistryjournal articleCited in: Self-Driving Laboratories (passage 1); Self-Driving Laboratories (passage 2); Emerging Frontiers in AI for the Life SciencesRecommended in: References
- CellProfiler: image analysis software for identifying and quantifying cell phenotypesAnne E Carpenter; Thouis R Jones; Michael R Lamprecht; et al. 2006. Genome Biologyjournal articleCited in: Cell Painting and Image-Based Phenotyping (passage 1); Cell Painting and Image-Based Phenotyping (passage 2); Quick Reference: All Chapter Summaries; Glossary; Model and Dataset IndexRecommended in: References
- Causaly, 2026Cited in: Knowledge Graphs and Literature AIRecommended in: References
- CellAge, Human Ageing Genomic ResourcesCited in: Aging and Longevity Biology AI (passage 1); Aging and Longevity Biology AI (passage 2); Aging and Longevity Biology AI (passage 3)
- CGIAR EBS implementation noteCited in: Plant, Crop, and Agricultural AI (passage 1); Plant, Crop, and Agricultural AI (passage 2)
- CGIAR Open Access and Open DataCited in: Plant, Crop, and Agricultural AI (passage 1); Plant, Crop, and Agricultural AI (passage 2)
- Chai-1: Decoding the molecular interactions of lifeChai Discovery; Jacques Boitreaud; Jack Dent; et al. 2024preprintCited in: Protein Structure Prediction (passage 1); Protein Structure Prediction (passage 2); Protein Structure Prediction (passage 3); Protein Structure Prediction (passage 4); Protein Structure Prediction (passage 5); Reproducibility and Open Science; Quick Reference: All Chapter Summaries (passage 1); Quick Reference: All Chapter Summaries (passage 2); Case Studies; Model and Dataset IndexRecommended in: References
- Building a knowledge graph to enable precision medicinePayal Chandak; Kexin Huang; Marinka Zitnik. 2023. Scientific Datajournal articleCited in: Knowledge Graphs and Literature AI; Drug Repurposing and Combination Therapy; Glossary; Model and Dataset IndexRecommended in: References
- Three million images and morphological profiles of cells treated with matched chemical and genetic perturbationsSrinivas Niranj Chandrasekaran; Beth A. Cimini; Amy Goodale; et al. 2024. Nature Methodsjournal articleCited in: Cell Painting and Image-Based Phenotyping; Quick Reference: All Chapter Summaries; Model and Dataset IndexRecommended in: References
- Morphological map of under- and overexpression of genes in human cellsSrinivas Niranj Chandrasekaran; Eric Alix; John Arevalo; et al. 2025. Nature Methodsjournal articleCited in: Cell Painting and Image-Based Phenotyping; Quick Reference: All Chapter Summaries; Model and Dataset IndexRecommended in: References
- Spatially resolved, highly multiplexed RNA profiling in single cellsKok Hao Chen; Alistair N. Boettiger; Jeffrey R. Moffitt; et al. 2015. Sciencejournal articleCited in: Spatial Omics and Tissue Models
- Digital Twins in Pharmaceutical and Biopharmaceutical Manufacturing: A Literature ReviewYingjie Chen; Ou Yang; Chaitanya Sampat; et al. 2020. Processesjournal articleCited in: AI for Biomanufacturing; Model and Dataset IndexRecommended in: References
- Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arraysAo Chen; Sha Liao; Mengnan Cheng; et al. 2022. Celljournal articleCited in: Spatial Omics and Tissue Models
- Humans or LLMs as the Judge? A Study on Judgement BiasGuiming 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
- Towards a general-purpose foundation model for computational pathologyRichard J. Chen; Tong Ding; Ming Y. Lu; et al. 2024. Nature Medicinejournal articleCited in: Histopathology AI; Quick Reference: All Chapter Summaries; Glossary; Model and Dataset IndexRecommended in: References
- Quantitative and interface-aware prediction of peptide–protein interactions by VITALWei-Hao Chen; Qi-Wen Wang; Zhi-Yi Li; et al. 2026. Nature Machine Intelligencejournal articleCited in: Chemical Biology and Target Engagement
- scE2TM improves single-cell embedding interpretability and reveals cellular perturbation signaturesHegang Chen; Yuyin Lu; Yifan Zhao; et al. 2026. Nature Communicationsjournal articleCited in: Single-Cell Foundation Models
- Network-based approach to prediction and population-based validation of in silico drug repurposingFeixiong Cheng; Rishi J. Desai; Diane E. Handy; et al. 2018. Nature Communicationsjournal articleCited in: Drug Repurposing and Combination Therapy
- Network-based prediction of drug combinationsFeixiong Cheng; István A. Kovács; Albert-László Barabási. 2019. Nature Communicationsjournal articleCited in: Drug Repurposing and Combination Therapy
- Accurate proteome-wide missense variant effect prediction with AlphaMissenseJun Cheng; Guido Novati; Joshua Pan; et al. 2023. Sciencejournal articleCited in: History of AI in the Life Sciences; Evaluation Principles for Life Sciences AI; Protein Structure Prediction; Variant Effect Prediction; Toolkit for AI-Augmented Bio Research; Emerging Frontiers in AI for the Life Sciences; Quick Reference: All Chapter Summaries; Case Studies; Glossary; Model and Dataset IndexRecommended in: References
- In Silico Labeling: Predicting Fluorescent Labels in Unlabeled ImagesEric M. Christiansen; Samuel J. Yang; D. Michael Ando; et al. 2018. Celljournal articleCited in: Microscopy and Cryo-EM AI; Model and Dataset IndexRecommended in: References
- Optimizing the Cell Painting assay for image-based profilingBeth A. Cimini; Srinivas Niranj Chandrasekaran; Maria Kost-Alimova; et al. 2023. Nature Protocolsjournal articleCited in: Cell Painting and Image-Based Phenotyping (passage 1); Cell Painting and Image-Based Phenotyping (passage 2); Chemical Biology and Target Engagement; Quick Reference: All Chapter Summaries; GlossaryRecommended in: References
- ClinicalTrials.gov, NCT05279755Cited in: Clinical Trial AI for Translational Research
- Codreanu, Imas, Mateos-Garcia et al., 2026Cited in: Agentic Science Workflows
- Detection and localization of surgically resectable cancers with a multi-analyte blood testJoshua D. Cohen; Lu Li; Yuxuan Wang; et al. 2018. Sciencejournal articleCited in: Diagnostics and Biomarker Translation (passage 1); Diagnostics and Biomarker Translation (passage 2)
- Real-World Evidence and Real-World Data for Evaluating Drug Safety and EffectivenessJacqueline Corrigan-Curay; Leonard Sacks; Janet Woodcock. 2018. JAMAjournal articleCited in: Real-World Evidence and Biomarker AIRecommended in: References
- Corso et al., 2023, preprintCited in: Small Molecule Generation and ADMET; Glossary; Model and Dataset IndexRecommended in: References
- Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learningNicolas Coudray; Paolo Santiago Ocampo; Theodore Sakellaropoulos; et al. 2018. Nature Medicinejournal articleCited in: Histopathology AI
- Genome-wide cell-free DNA fragmentation in patients with cancerStephen Cristiano; Alessandro Leal; Jillian Phallen; et al. 2019. Naturejournal articleCited in: Diagnostics and Biomarker Translation (passage 1); Diagnostics and Biomarker Translation (passage 2)
- Genomic Selection in Plant Breeding: Methods, Models, and PerspectivesJosé Crossa; Paulino Pérez-Rodríguez; Jaime Cuevas; et al. 2017. Trends in Plant Sciencejournal articleCited in: Plant, Crop, and Agricultural AI (passage 1); Plant, Crop, and Agricultural AI (passage 2)Recommended in: Plant, Crop, and Agricultural AI (passage 3)
- Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extensionSamantha Cruz Rivera; Xiaoxuan Liu; An-Wen Chan; et al. 2020. Nature Medicinejournal articleCited in: Clinical Trial AI for Translational Research; Glossary
- scGPT: toward building a foundation model for single-cell multi-omics using generative AIHaotian Cui; Chloe Wang; Hassaan Maan; et al. 2024. Nature Methodsjournal articleCited in: AI for the Life Sciences; Foundation Models for Biology (passage 1); Foundation Models for Biology (passage 2); Single-Cell Foundation Models (passage 1); Single-Cell Foundation Models (passage 2); Toolkit for AI-Augmented Bio Research; Quick Reference: All Chapter Summaries; Case Studies; Glossary (passage 1); Glossary (passage 2); Model and Dataset IndexRecommended in: References
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Preface
References
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How to Read This Handbook
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Scope and Limitations
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Part I: Foundations
AI for the Life Sciences
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- CZICited in: AI for the Life Sciences
- Simulating 500 million years of evolution with a language modelThomas Hayes; Roshan Rao; Halil Akin; et al. 2025. Sciencejournal articleCited in: AI for the Life Sciences
- HHS press release, May 2026Cited in: AI for the Life Sciences (passage 1); AI for the Life Sciences (passage 2)
- Isomorphic Labs, January 2024Cited in: AI for the Life Sciences
- Highly accurate protein structure prediction with AlphaFoldJohn Jumper; Richard Evans; Alexander Pritzel; et al. 2021. Naturejournal articleCited in: AI for the Life Sciences
- Evolutionary-scale prediction of atomic-level protein structure with a language modelZeming Lin; Halil Akin; Roshan Rao; et al. 2023. Sciencejournal articleCited in: AI for the Life Sciences
- NCI CRDCCited in: AI for the Life Sciences
- Sequence modeling and design from molecular to genome scale with EvoEric Nguyen; Michael Poli; Matthew G. Durrant; et al. 2024. Sciencejournal articleCited in: AI for the Life Sciences
- NIH Common FundCited in: AI for the Life Sciences
- Nobel Prize, 2024Cited in: AI for the Life Sciences
- OpenAI, April 2024Cited in: AI for the Life Sciences
- OpenAI, June 2024Cited in: AI for the Life Sciences
- OpenAI, September 2024Cited in: AI for the Life Sciences (passage 1); AI for the Life Sciences (passage 2)
- Recursion pipelineCited in: AI for the Life Sciences
- A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical modelsFeng Ren; Alex Aliper; Jian Chen; et al. 2024. Nature Biotechnologyjournal articleCited in: AI for the Life Sciences
- Transfer learning enables predictions in network biologyChristina V. Theodoris; Ling Xiao; Anant Chopra; et al. 2023. Naturejournal articleCited in: AI for the Life Sciences
- AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequencesMihaly Varadi; Damian Bertoni; Paulyna Magana; et al. 2023. Nucleic Acids Researchjournal articleCited in: AI for the Life Sciences
- De novo design of protein structure and function with RFdiffusionJoseph L. Watson; David Juergens; Nathaniel R. Bennett; et al. 2023. Naturejournal articleCited in: AI for the Life Sciences
- A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trialZuojun Xu; Feng Ren; Ping Wang; et al. 2025. Nature Medicinejournal articleCited in: AI for the Life Sciences
- Zambaldi et al., 2024, preprintCited in: AI for the Life Sciences
History of AI in the Life Sciences
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- Basic local alignment search toolStephen F. Altschul; Warren Gish; Webb Miller; et al. 1990. Journal of Molecular Biologyjournal articleCited in: History of AI in the Life Sciences (passage 1); History of AI in the Life Sciences (passage 2)
- Accurate prediction of protein structures and interactions using a three-track neural networkMinkyung Baek; Frank DiMaio; Ivan Anishchenko; et al. 2021. Sciencejournal articleCited in: History of AI in the Life Sciences
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- Sequential regulatory activity prediction across chromosomes with convolutional neural networksDavid R. Kelley; Yakir A. Reshef; Maxwell Bileschi; et al. 2018. Genome Researchjournal articleCited in: History of AI in the Life Sciences
- Evolutionary-scale prediction of atomic-level protein structure with a language modelZeming Lin; Halil Akin; Roshan Rao; et al. 2023. Sciencejournal articleCited in: History of AI in the Life Sciences
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- Nobel Prize, 2024Cited in: History of AI in the Life Sciences (passage 1); History of AI in the Life Sciences (passage 2)
- A universal SNP and small-indel variant caller using deep neural networksRyan Poplin; Pi-Chuan Chang; David Alexander; et al. 2018. Nature Biotechnologyjournal articleCited in: History of AI in the Life Sciences (passage 1); History of AI in the Life Sciences (passage 2)
- DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequencesDaniel Quang; Xiaohui Xie. 2016. Nucleic Acids Researchjournal articleCited in: History of AI in the Life Sciences
- Improved protein structure prediction using potentials from deep learningAndrew W. Senior; Richard Evans; John Jumper; et al. 2020. Naturejournal articleCited in: History of AI in the Life Sciences (passage 1); History of AI in the Life Sciences (passage 2)
- Identification of common molecular subsequencesT.F. Smith; M.S. Waterman. 1981. Journal of Molecular Biologyjournal articleCited in: History of AI in the Life Sciences (passage 1); History of AI in the Life Sciences (passage 2)
- Transfer learning enables predictions in network biologyChristina V. Theodoris; Ling Xiao; Anant Chopra; et al. 2023. Naturejournal articleCited in: History of AI in the Life Sciences
- AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequencesMihaly Varadi; Damian Bertoni; Paulyna Magana; et al. 2023. Nucleic Acids Researchjournal articleCited in: History of AI in the Life Sciences
- De novo design of protein structure and function with RFdiffusionJoseph L. Watson; David Juergens; Nathaniel R. Bennett; et al. 2023. Naturejournal articleCited in: History of AI in the Life Sciences (passage 1); History of AI in the Life Sciences (passage 2)
- Predicting effects of noncoding variants with deep learning–based sequence modelJian Zhou; Olga G Troyanskaya. 2015. Nature Methodsjournal articleCited in: History of AI in the Life Sciences (passage 1); History of AI in the Life Sciences (passage 2)
Biological Data Infrastructure
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Knowledge Graphs and Literature AI
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- An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competitionGeorge Tsatsaronis; Georgios Balikas; Prodromos Malakasiotis; et al. 2015. BMC Bioinformaticsjournal articleCited in: Knowledge Graphs and Literature AI
- Fabrication and errors in the bibliographic citations generated by ChatGPTWilliam H. Walters; Esther Isabelle Wilder. 2023. Scientific Reportsjournal articleCited in: Knowledge Graphs and Literature AI
- PubMind: literature-based genetic variant extraction and functional annotation using large language modelsPeng Wang; Kai Wang. 2026. Nature Communicationsjournal articleCited in: Knowledge Graphs and Literature AI
- PubTator central: automated concept annotation for biomedical full text articlesChih-Hsuan Wei; Alexis Allot; Robert Leaman; et al. 2019. Nucleic Acids Researchjournal articleCited in: Knowledge Graphs and Literature AI
Foundation Models for Biology
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- Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselinesConstantin Ahlmann-Eltze; Wolfgang Huber; Simon Anders. 2025. Nature Methodsjournal articleCited in: Foundation Models for Biology
- Anthropic, 2026Cited in: Foundation Models for Biology
- Deeper evaluation of a single-cell foundation modelRebecca Boiarsky; Nalini M. Singh; Alejandro Buendia; et al. 2024. Nature Machine Intelligencejournal articleCited in: Foundation Models for Biology
- Genome modelling and design across all domains of life with Evo 2Garyk Brixi; Matthew G. Durrant; Jerome Ku; et al. 2026. Naturejournal articleCited in: Foundation Models for Biology (passage 1); Foundation Models for Biology (passage 2)
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- Nucleotide Transformer: building and evaluating robust foundation models for human genomicsHugo Dalla-Torre; Liam Gonzalez; Javier Mendoza-Revilla; et al. 2024. Nature Methodsjournal articleCited in: Foundation Models for Biology (passage 1); Foundation Models for Biology (passage 2)
- ProtTrans: Toward Understanding the Language of Life Through Self-Supervised LearningAhmed Elnaggar; Michael Heinzinger; Christian Dallago; et al. 2022. IEEE Transactions on Pattern Analysis and Machine Intelligencejournal articleCited in: Foundation Models for Biology
- Orthrus: toward evolutionary and functional RNA foundation modelsPhilip Fradkin; Ruian “Ian” Shi; Taykhoom Dalal; et al. 2026. Nature Methodsjournal articleCited in: Foundation Models for Biology (passage 1); Foundation Models for Biology (passage 2)
- A foundation model of transcription across human cell typesXi Fu; Shentong Mo; Alejandro Buendia; et al. 2025. Naturejournal articleCited in: Foundation Models for Biology (passage 1); Foundation Models for Biology (passage 2)
- Google DeepMind, 2026Cited in: Foundation Models for Biology
- Large-scale foundation model on single-cell transcriptomicsMinsheng Hao; Jing Gong; Xin Zeng; et al. 2024. Nature Methodsjournal articleCited in: Foundation Models for Biology (passage 1); Foundation Models for Biology (passage 2)
- Simulating 500 million years of evolution with a language modelThomas Hayes; Roshan Rao; Halil Akin; et al. 2025. Sciencejournal articleCited in: Foundation Models for Biology (passage 1); Foundation Models for Biology (passage 2)
- A cell atlas foundation model for scalable search of similar human cellsGraham Heimberg; Tony Kuo; Daryle J. DePianto; et al. 2024. Naturejournal articleCited in: Foundation Models for Biology
- DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genomeYanrong Ji; Zhihan Zhou; Han Liu; et al. 2021. Bioinformaticsjournal articleCited in: Foundation Models for Biology
- Highly accurate protein structure prediction with AlphaFoldJohn Jumper; Richard Evans; Alexander Pritzel; et al. 2021. Naturejournal articleCited in: Foundation Models for Biology
- Evolutionary-scale prediction of atomic-level protein structure with a language modelZeming Lin; Halil Akin; Roshan Rao; et al. 2023. Sciencejournal articleCited in: Foundation Models for Biology (passage 1); Foundation Models for Biology (passage 2)
- Sequence modeling and design from molecular to genome scale with EvoEric Nguyen; Michael Poli; Matthew G. Durrant; et al. 2024. Sciencejournal articleCited in: Foundation Models for Biology (passage 1); Foundation Models for Biology (passage 2)
- ProGen2: Exploring the boundaries of protein language modelsErik Nijkamp; Jeffrey A. Ruffolo; Eli N. Weinstein; et al. 2023. Cell Systemsjournal articleCited in: Foundation Models for Biology
- OpenAI, 2025Cited in: Foundation Models for Biology
- Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequencesAlexander Rives; Joshua Meier; Tom Sercu; et al. 2021. Proceedings of the National Academy of Sciencesjournal articleCited in: Foundation Models for Biology (passage 1); Foundation Models for Biology (passage 2)
- Nicheformer: a foundation model for single-cell and spatial omicsAlejandro Tejada-Lapuerta; Anna C. Schaar; Robert Gutgesell; et al. 2025. Nature Methodsjournal articleCited in: Foundation Models for Biology (passage 1); Foundation Models for Biology (passage 2)
- Transfer learning enables predictions in network biologyChristina V. Theodoris; Ling Xiao; Anant Chopra; et al. 2023. Naturejournal articleCited in: Foundation Models for Biology (passage 1); Foundation Models for Biology (passage 2)
Evaluation Principles for Life Sciences AI
References
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- GuacaMol: Benchmarking Models for de Novo Molecular DesignNathan Brown; Marco Fiscato; Marwin H.S. Segler; et al. 2019. Journal of Chemical Information and Modelingjournal articleCited in: Evaluation Principles for Life Sciences AI
- Buttenschoen et al., 2024Cited in: Evaluation Principles for Life Sciences AI
- CAMEO, 2026Cited in: Evaluation Principles for Life Sciences AI
- Accurate proteome-wide missense variant effect prediction with AlphaMissenseJun Cheng; Guido Novati; Joshua Pan; et al. 2023. Sciencejournal articleCited in: Evaluation Principles for Life Sciences AI
- Highly accurate protein structure prediction with AlphaFoldJohn Jumper; Richard Evans; Alexander Pritzel; et al. 2021. Naturejournal articleCited in: Evaluation Principles for Life Sciences AI
- Kryshtafovych et al., 2024Cited in: Evaluation Principles for Life Sciences AI
- Miller, 2024, preprintCited in: Evaluation Principles for Life Sciences AI
- World models for biomedicineAyush Noori; Nic Fishman; Ada Fang; Lukas Fesser; Marinka Zitnik. 2026. Celljournal articleCited in: Evaluation Principles for Life Sciences AI
- Intrinsic dataset features drive mutational effect prediction by protein language modelsLuiz C. Vieira; Sophia Lin; Claus O. Wilke. 2026preprintCited in: Evaluation Principles for Life Sciences AI
- Most Ligand-Based Classification Benchmarks Reward Memorization Rather than GeneralizationIzhar Wallach; Abraham Heifets. 2018. Journal of Chemical Information and Modelingjournal articleCited in: Evaluation Principles for Life Sciences AI
- DOME: recommendations for supervised machine learning validation in biologyIan Walsh; Dmytro Fishman; Dario Garcia-Gasulla; et al. 2021. Nature Methodsjournal articleCited in: Evaluation Principles for Life Sciences AI
- MoleculeNet: a benchmark for molecular machine learningZhenqin Wu; Bharath Ramsundar; Evan N. Feinberg; et al. 2018. Chemical Sciencejournal articleCited in: Evaluation Principles for Life Sciences AI
Part II: Molecular Discovery and Design
Protein Structure Prediction
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- Addendum: Accurate structure prediction of biomolecular interactions with AlphaFold 3Josh Abramson; Jonas Adler; Jack Dunger; et al. 2024. Naturejournal articleCited in: Protein Structure Prediction
- A structural biology community assessment of AlphaFold2 applicationsMehmet Akdel; Douglas E. V. Pires; Eduard Porta Pardo; et al. 2022. Nature Structural & Molecular Biologyjournal articleCited in: Protein Structure Prediction
- Accurate prediction of protein structures and interactions using a three-track neural networkMinkyung Baek; Frank DiMaio; Ivan Anishchenko; et al. 2021. Sciencejournal articleCited in: Protein Structure Prediction
- Improved prediction of protein-protein interactions using AlphaFold2Patrick Bryant; Gabriele Pozzati; Arne Elofsson. 2022. Nature Communicationsjournal articleCited in: Protein Structure Prediction
- Chai-1: Decoding the molecular interactions of lifeChai Discovery; Jacques Boitreaud; Jack Dent; et al. 2024preprintCited in: Protein Structure Prediction (passage 1); Protein Structure Prediction (passage 2); Protein Structure Prediction (passage 3); Protein Structure Prediction (passage 4); Protein Structure Prediction (passage 5)
- Accurate proteome-wide missense variant effect prediction with AlphaMissenseJun Cheng; Guido Novati; Joshua Pan; et al. 2023. Sciencejournal articleCited in: Protein Structure Prediction
- EMBL-EBI AlphaFold trainingCited in: Protein Structure Prediction
- Protein complex prediction with AlphaFold-MultimerRichard Evans; Michael O’Neill; Alexander Pritzel; et al. 2021preprintCited in: Protein Structure Prediction
- Simulating 500 million years of evolution with a language modelThomas Hayes; Roshan Rao; Halil Akin; et al. 2025. Sciencejournal articleCited in: Protein Structure Prediction
- AlphaFill: enriching AlphaFold models with ligands and cofactorsMaarten L. Hekkelman; Ida de Vries; Robbie P. Joosten; et al. 2022. Nature Methodsjournal articleCited in: Protein Structure Prediction
- Highly accurate protein structure prediction with AlphaFoldJohn Jumper; Richard Evans; Alexander Pritzel; et al. 2021. Naturejournal articleCited in: Protein Structure Prediction (passage 1); Protein Structure Prediction (passage 2); Protein Structure Prediction (passage 3); Protein Structure Prediction (passage 4)
- ESMDynamic: Fast and accurate prediction of protein dynamic contact maps from single sequencesDiego E. Kleiman; Jiangyan Feng; Zhengyuan Xue; et al. 2026. Nature Communicationsjournal articleCited in: Protein Structure Prediction
- Evolutionary-scale prediction of atomic-level protein structure with a language modelZeming Lin; Halil Akin; Roshan Rao; et al. 2023. Sciencejournal articleCited in: Protein Structure Prediction
- Nobel Prize, 2024Cited in: Protein Structure Prediction
- Boltz-2: Towards Accurate and Efficient Binding Affinity PredictionSaro Passaro; Gabriele Corso; Jeremy Wohlwend; et al. 2025preprintCited in: Protein Structure Prediction (passage 1); Protein Structure Prediction (passage 2); Protein Structure Prediction (passage 3)
- Highly accurate protein structure prediction for the human proteomeKathryn Tunyasuvunakool; Jonas Adler; Zachary Wu; et al. 2021. Naturejournal articleCited in: Protein Structure Prediction
- AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy modelsMihaly Varadi; Stephen Anyango; Mandar Deshpande; et al. 2021. Nucleic Acids Researchjournal articleCited in: Protein Structure Prediction
- AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequencesMihaly Varadi; Damian Bertoni; Paulyna Magana; et al. 2023. Nucleic Acids Researchjournal articleCited in: Protein Structure Prediction (passage 1); Protein Structure Prediction (passage 2); Protein Structure Prediction (passage 3)
- De novo design of protein structure and function with RFdiffusionJoseph L. Watson; David Juergens; Nathaniel R. Bennett; et al. 2023. Naturejournal articleCited in: Protein Structure Prediction
- Learning millisecond protein dynamics from what is missing in NMR spectraHannah K. Wayment-Steele; Gina El Nesr; Ramith Hettiarachchi; et al. 2026. Naturejournal articleCited in: Protein Structure Prediction
- Boltz-1 Democratizing Biomolecular Interaction ModelingJeremy Wohlwend; Gabriele Corso; Saro Passaro; et al. 2024preprintCited in: Protein Structure Prediction (passage 1); Protein Structure Prediction (passage 2); Protein Structure Prediction (passage 3); Protein Structure Prediction (passage 4); Protein Structure Prediction (passage 5)
- High-resolution de novo structure prediction from primary sequenceRuidong Wu; Fan Ding; Rui Wang; et al. 2022preprintCited in: Protein Structure Prediction
Protein Design and Engineering
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- De Novo Computational Design of Retro-Aldol EnzymesLin Jiang; Eric A. Althoff; Fernando R. Clemente; et al. 2008. Sciencejournal articleCited in: Protein Design and Engineering
- Rapid in silico directed evolution by a protein language model with EVOLVEproKaiyi Jiang; Zhaoqing Yan; Matteo Di Bernardo; et al. 2025. Sciencejournal articleCited in: Protein Design and Engineering
- Computational scoring and experimental evaluation of enzymes generated by neural networksSean R. Johnson; Xiaozhi Fu; Sandra Viknander; et al. 2024. Nature Biotechnologyjournal articleCited in: Protein Design and Engineering
- Rosetta3Andrew Leaver-Fay; Michael Tyka; Steven M. Lewis; et al. 2011. Methods in Enzymologybook chapterCited in: Protein Design and Engineering
- Multistate and functional protein design using RoseTTAFold sequence space diffusionSidney Lyayuga Lisanza; Jacob Merle Gershon; Samuel W. K. Tipps; et al. 2024. Nature Biotechnologyjournal articleCited in: Protein Design and Engineering
- Efficient generation of epitope-targeted antibodies with GerminalLuis S. Mille-Fragoso; Claudia L. Driscoll; John N. Wang; et al. 2026. Nature Biotechnologyjournal articleCited in: Protein Design and Engineering
- ProGen2: Exploring the boundaries of protein language modelsErik Nijkamp; Jeffrey A. Ruffolo; Eli N. Weinstein; et al. 2023. Cell Systemsjournal articleCited in: Protein Design and Engineering
- Nobel Prize, 2024Cited in: Protein Design and Engineering
- AbLang: an antibody language model for completing antibody sequencesTobias H Olsen; Iain H Moal; Charlotte M Deane. 2022. Bioinformatics Advancesjournal articleCited in: Protein Design and Engineering
- Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodiesJeffrey A. Ruffolo; Lee-Shin Chu; Sai Pooja Mahajan; et al. 2023. Nature Communicationsjournal articleCited in: Protein Design and Engineering
- Kemp elimination catalysts by computational enzyme designDaniela Röthlisberger; Olga Khersonsky; Andrew M. Wollacott; et al. 2008. Naturejournal articleCited in: Protein Design and Engineering
- Adaptive model-guided protein evolution with sparse data optimizes compact eukaryotic genome editorsShijie Wan; Jackson Gold; Pranay Vure; et al. 2026. Nature Biotechnologyjournal articleCited in: Protein Design and Engineering
- De novo design of protein structure and function with RFdiffusionJoseph L. Watson; David Juergens; Nathaniel R. Bennett; et al. 2023. Naturejournal articleCited in: Protein Design and Engineering (passage 1); Protein Design and Engineering (passage 2)
- Aligning protein-generative models to experimental fitness with ProteinDPOTalal Widatalla; Ashir A. Borah; Samuel H. King; et al. 2026. Nature Methodsjournal articleCited in: Protein Design and Engineering
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Antibody and Biologic Design
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- Atomically accurate de novo design of antibodies with RFdiffusionNathaniel R. Bennett; Joseph L. Watson; Robert J. Ragotte; et al. 2025. Naturejournal articleCited in: Antibody and Biologic Design (passage 1); Antibody and Biologic Design (passage 2)
- A blinded, prospective benchmark of in silico antibody discovery anchored to experimental affinity and developabilityM. Frank Erasmus; Daniel Bedinger; Elizabeth Hopkins; et al. 2026. Nature Biotechnologyjournal articleCited in: Antibody and Biologic Design
- Efficient evolution of human antibodies from general protein language modelsBrian L. Hie; Varun R. Shanker; Duo Xu; et al. 2023. Nature Biotechnologyjournal articleCited in: Antibody and Biologic Design
- A strategy for risk mitigation of antibodies with fast clearanceIsidro Hötzel; Frank-Peter Theil; Lisa J. Bernstein; et al. 2012. mAbsjournal articleCited in: Antibody and Biologic Design (passage 1); Antibody and Biologic Design (passage 2)
- Biophysical properties of the clinical-stage antibody landscapeTushar Jain; Tingwan Sun; Stéphanie Durand; et al. 2017. Proceedings of the National Academy of Sciencesjournal articleCited in: Antibody and Biologic Design (passage 1); Antibody and Biologic Design (passage 2)
- Five computational developability guidelines for therapeutic antibody profilingMatthew 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); Antibody and Biologic Design (passage 2)
- Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodiesJeffrey A. Ruffolo; Lee-Shin Chu; Sai Pooja Mahajan; et al. 2023. Nature Communicationsjournal articleCited in: Antibody and Biologic Design
- Unsupervised evolution of protein and antibody complexes with a structure-informed language modelVarun R. Shanker; Theodora U. J. Bruun; Brian L. Hie; et al. 2024. Sciencejournal articleCited in: Antibody and Biologic Design
- De novo design of protein structure and function with RFdiffusionJoseph L. Watson; David Juergens; Nathaniel R. Bennett; et al. 2023. Naturejournal articleCited in: Antibody and Biologic Design (passage 1); Antibody and Biologic Design (passage 2)
Nucleic Acid and Genome Models
References
- Effective gene expression prediction from sequence by integrating long-range interactionsŽiga Avsec; Vikram Agarwal; Daniel Visentin; et al. 2021. Nature Methodsjournal articleCited in: Nucleic Acid and Genome Models (passage 1); Nucleic Acid and Genome Models (passage 2)
- Advancing regulatory variant effect prediction with AlphaGenomeŽiga Avsec; Natasha Latysheva; Jun Cheng; et al. 2026. Naturejournal articleCited in: Nucleic Acid and Genome Models (passage 1); Nucleic Acid and Genome Models (passage 2); Nucleic Acid and Genome Models (passage 3)
- Genome modelling and design across all domains of life with Evo 2Garyk Brixi; Matthew G. Durrant; Jerome Ku; et al. 2026. Naturejournal articleCited in: Nucleic Acid and Genome Models (passage 1); Nucleic Acid and Genome Models (passage 2)
- Nucleotide Transformer: building and evaluating robust foundation models for human genomicsHugo Dalla-Torre; Liam Gonzalez; Javier Mendoza-Revilla; et al. 2024. Nature Methodsjournal articleCited in: Nucleic Acid and Genome Models
- Orthrus: toward evolutionary and functional RNA foundation modelsPhilip Fradkin; Ruian “Ian” Shi; Taykhoom Dalal; et al. 2026. Nature Methodsjournal articleCited in: Nucleic Acid and Genome Models
- A foundation model of transcription across human cell typesXi Fu; Shentong Mo; Alejandro Buendia; et al. 2025. Naturejournal articleCited in: Nucleic Acid and Genome Models (passage 1); Nucleic Acid and Genome Models (passage 2)
- Systematic mapping of functional enhancer–promoter connections with CRISPR interferenceCharles P. Fulco; Mathias Munschauer; Rockwell Anyoha; et al. 2016. Sciencejournal articleCited in: Nucleic Acid and Genome Models
- Decoding the sequence determinants of locus-specific DNA methylation across human tissuesJunru Jin; Ding Wang; Jianbo Qiao; et al. 2026. Nature Communicationsjournal articleCited in: Nucleic Acid and Genome Models
- Generative design of bacteriophages with genome language modelsSamuel H. King; Claudia L. Driscoll; David B. Li; et al. 2026. Sciencejournal articleCited in: Nucleic Acid and Genome Models
- Semantic design of functional de novo genes from a genomic language modelAditi T. Merchant; Samuel H. King; Eric Nguyen; et al. 2025. Naturejournal articleCited in: Nucleic Acid and Genome Models (passage 1); Nucleic Acid and Genome Models (passage 2)
- Sequence modeling and design from molecular to genome scale with EvoEric Nguyen; Michael Poli; Matthew G. Durrant; et al. 2024. Sciencejournal articleCited in: Nucleic Acid and Genome Models (passage 1); Nucleic Acid and Genome Models (passage 2)
- Accurate RNA 3D structure prediction using a language model-based deep learning approachTao Shen; Zhihang Hu; Siqi Sun; et al. 2024. Nature Methodsjournal articleCited in: Nucleic Acid and Genome Models
- Direct Identification of Hundreds of Expression-Modulating Variants using a Multiplexed Reporter AssayRyan Tewhey; Dylan Kotliar; Daniel S. Park; et al. 2016. Celljournal articleCited in: Nucleic Acid and Genome Models
- De novo design of RNA pseudoknots with deep learningJill Townley; Wipapat Kladwang; David Baker; et al. 2026. Sciencejournal articleCited in: Nucleic Acid and Genome Models
- A generalizable Hi-C foundation model for chromatin architecture, single-cell and multiomics analysis across speciesXiao Wang; Yuanyuan Zhang; Suhita Ray; et al. 2026. Nature Methodsjournal articleCited in: Nucleic Acid and Genome Models
- Predicting effects of noncoding variants with deep learning–based sequence modelJian Zhou; Olga G Troyanskaya. 2015. Nature Methodsjournal articleCited in: Nucleic Acid and Genome Models (passage 1); Nucleic Acid and Genome Models (passage 2)
Variant Effect Prediction
References
- Effective gene expression prediction from sequence by integrating long-range interactionsŽiga Avsec; Vikram Agarwal; Daniel Visentin; et al. 2021. Nature Methodsjournal articleCited in: Variant Effect Prediction
- Advancing regulatory variant effect prediction with AlphaGenomeŽiga Avsec; Natasha Latysheva; Jun Cheng; et al. 2026. Naturejournal articleCited in: Variant Effect Prediction
- A DNA language model based on multispecies alignment predicts the effects of genome-wide variantsGonzalo Benegas; Carlos Albors; Alan J. Aw; et al. 2025. Nature Biotechnologyjournal articleCited in: Variant Effect Prediction
- Brnich et al., 2020Cited in: Variant Effect Prediction
- Accurate proteome-wide missense variant effect prediction with AlphaMissenseJun Cheng; Guido Novati; Joshua Pan; et al. 2023. Sciencejournal articleCited in: Variant Effect Prediction
- Nucleotide Transformer: building and evaluating robust foundation models for human genomicsHugo Dalla-Torre; Liam Gonzalez; Javier Mendoza-Revilla; et al. 2024. Nature Methodsjournal articleCited in: Variant Effect Prediction
- DeepMind, 2026Cited in: Variant Effect Prediction
- Accurate classification of BRCA1 variants with saturation genome editingGregory M. Findlay; Riza M. Daza; Beth Martin; et al. 2018. Naturejournal articleCited in: Variant Effect Prediction
- Disease variant prediction with deep generative models of evolutionary dataJonathan Frazer; Pascal Notin; Mafalda Dias; et al. 2021. Naturejournal articleCited in: Variant Effect Prediction
- REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense VariantsNilah M. Ioannidis; Joseph H. Rothstein; Vikas Pejaver; et al. 2016. The American Journal of Human Geneticsjournal articleCited in: Variant Effect Prediction
- Predicting Splicing from Primary Sequence with Deep LearningKishore Jaganathan; Sofia Kyriazopoulou Panagiotopoulou; Jeremy F. McRae; et al. 2019. Celljournal articleCited in: Variant Effect Prediction
- The mutational constraint spectrum quantified from variation in 141,456 humansKonrad J. Karczewski; Laurent C. Francioli; Grace Tiao; et al. 2020. Naturejournal articleCited in: Variant Effect Prediction
- A general framework for estimating the relative pathogenicity of human genetic variantsMartin Kircher; Daniela M Witten; Preti Jain; et al. 2014. Nature Geneticsjournal articleCited in: Variant Effect Prediction
- Meier et al., 2021, preprintCited in: Variant Effect Prediction
- Rare variant effect estimation and polygenic risk predictionKisung Nam; Minjung Kho; Wei Zhou; et al. 2026. Nature Geneticsjournal articleCited in: Variant Effect Prediction
- NatureCited in: Variant Effect Prediction
- Nature newsCited in: Variant Effect Prediction
- Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteriaVikas Pejaver; Alicia B. Byrne; Bing-Jian Feng; et al. 2022. The American Journal of Human Geneticsjournal articleCited in: Variant Effect Prediction (passage 1); Variant Effect Prediction (passage 2)
- 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 PathologySue Richards; Nazneen Aziz; Sherri Bale; et al. 2015. Genetics in Medicinejournal articleCited in: Variant Effect Prediction (passage 1); Variant Effect Prediction (passage 2)
- Predicting the clinical impact of human mutation with deep neural networksLaksshman Sundaram; Hong Gao; Samskruthi Reddy Padigepati; et al. 2018. Nature Geneticsjournal articleCited in: Variant Effect Prediction
- High-resolution reconstruction of cell-type-specific transcriptional regulatory processes from bulk sequencing samplesLi Yao; Sagar R. Shah; Abdullah Ozer; et al. 2026. Nature Biotechnologyjournal articleCited in: Variant Effect Prediction
- A disease-specific language model for variant pathogenicity in cardiac and regulatory genomicsHuixin Zhan; Jason H. Moore; Zijun Zhang. 2025. Nature Machine Intelligencejournal articleCited in: Variant Effect Prediction
- Predicting effects of noncoding variants with deep learning–based sequence modelJian Zhou; Olga G Troyanskaya. 2015. Nature Methodsjournal articleCited in: Variant Effect Prediction
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 baselinesConstantin Ahlmann-Eltze; Wolfgang Huber; Simon Anders. 2025. Nature Methodsjournal articleCited in: Single-Cell Foundation Models
- Deeper evaluation of a single-cell foundation modelRebecca Boiarsky; Nalini M. Singh; Alejandro Buendia; et al. 2024. Nature Machine Intelligencejournal articleCited in: Single-Cell Foundation Models
- scE2TM improves single-cell embedding interpretability and reveals cellular perturbation signaturesHegang Chen; Yuyin Lu; Yifan Zhao; et al. 2026. Nature Communicationsjournal articleCited in: Single-Cell Foundation Models
- scGPT: toward building a foundation model for single-cell multi-omics using generative AIHaotian Cui; Chloe Wang; Hassaan Maan; et al. 2024. Nature Methodsjournal articleCited in: Single-Cell Foundation Models (passage 1); Single-Cell Foundation Models (passage 2)
- CZ CELLxGENE team, 2023, preprintCited in: Single-Cell Foundation Models
- Bonsai reconstructs tree representations for distortion-free visualization and exploration of high-dimensional dataDaan H. de Groot; Sarah X. Morillo Leonardo; Mikhail Pachkov; et al. 2026. Nature Biotechnologyjournal articleCited in: Single-Cell Foundation Models
- Evaluating the role of pretraining dataset size and diversity on single-cell foundation model performanceAlan DenAdel; Madeline Hughes; Akshaya Thoutam; et al. 2026. Nature Methodsjournal articleCited in: Single-Cell Foundation Models
- Scoring gene importance by interpreting single-cell foundation modelsMaxwell P. Gold; Miguel Reyes; Nathaniel Diamant; et al. 2026. Nature Biotechnologyjournal articleCited in: Single-Cell Foundation Models
- Large-scale foundation model on single-cell transcriptomicsMinsheng Hao; Jing Gong; Xin Zeng; et al. 2024. Nature Methodsjournal articleCited in: Single-Cell Foundation Models (passage 1); Single-Cell Foundation Models (passage 2)
- A cell atlas foundation model for scalable search of similar human cellsGraham Heimberg; Tony Kuo; Daryle J. DePianto; et al. 2024. Naturejournal articleCited in: Single-Cell Foundation Models (passage 1); Single-Cell Foundation Models (passage 2)
- scPRAM accurately predicts single-cell gene expression perturbation response based on attention mechanismQun Jiang; Shengquan Chen; Xiaoyang Chen; et al. 2024. Bioinformaticsjournal articleCited in: Single-Cell Foundation Models
- Fast, sensitive and accurate integration of single-cell data with HarmonyIlya Korsunsky; Nghia Millard; Jean Fan; et al. 2019. Nature Methodsjournal articleCited in: Single-Cell Foundation Models
- Deep generative modeling for single-cell transcriptomicsRomain Lopez; Jeffrey Regier; Michael B. Cole; et al. 2018. Nature Methodsjournal articleCited in: Single-Cell Foundation Models
- scGen predicts single-cell perturbation responsesMohammad Lotfollahi; F. Alexander Wolf; Fabian J. Theis. 2019. Nature Methodsjournal articleCited in: Single-Cell Foundation Models
- Predicting cellular responses to complex perturbations in high‐throughput screensMohammad Lotfollahi; Anna Klimovskaia Susmelj; Carlo De Donno; et al. 2023. Molecular Systems Biologyjournal articleCited in: Single-Cell Foundation Models
- Benchmarking atlas-level data integration in single-cell genomicsMalte D. Luecken; M. Büttner; K. Chaichoompu; et al. 2021. Nature Methodsjournal articleCited in: Single-Cell Foundation Models (passage 1); Single-Cell Foundation Models (passage 2)
- Defining and benchmarking open problems in single-cell analysisMalte D. Luecken; Scott Gigante; Daniel B. Burkhardt; et al. 2025. Nature Biotechnologyjournal articleCited in: Single-Cell Foundation Models
- The Human Cell AtlasAviv Regev; Sarah A Teichmann; Eric S Lander; et al. 2017. eLifejournal articleCited in: Single-Cell Foundation Models
- The Human Cell Atlas from a cell census to a unified foundation modelJennifer E. Rood; Samantha Wynne; Lucia Robson; et al. 2024. Naturejournal articleCited in: Single-Cell Foundation Models (passage 1); Single-Cell Foundation Models (passage 2)
- Predicting transcriptional outcomes of novel multigene perturbations with GEARSYusuf Roohani; Kexin Huang; Jure Leskovec. 2023. Nature Biotechnologyjournal articleCited in: Single-Cell Foundation Models
- Universal cell embedding provides a foundation model for cell biologyYanay Rosen; Yusuf Roohani; Ayush Agrawal; et al. 2026. Naturejournal articleCited in: Single-Cell Foundation Models (passage 1); Single-Cell Foundation Models (passage 2); Single-Cell Foundation Models (passage 3); Single-Cell Foundation Models (passage 4)
- Comprehensive Integration of Single-Cell DataTim Stuart; Andrew Butler; Paul Hoffman; et al. 2019. Celljournal articleCited in: Single-Cell Foundation Models
- Tabula Sapiens Consortium, 2022Cited in: Single-Cell Foundation Models
- Nicheformer: a foundation model for single-cell and spatial omicsAlejandro Tejada-Lapuerta; Anna C. Schaar; Robert Gutgesell; et al. 2025. Nature Methodsjournal articleCited in: Single-Cell Foundation Models (passage 1); Single-Cell Foundation Models (passage 2); Single-Cell Foundation Models (passage 3)
- Transfer learning enables predictions in network biologyChristina V. Theodoris; Ling Xiao; Anant Chopra; et al. 2023. Naturejournal articleCited in: Single-Cell Foundation Models (passage 1); Single-Cell Foundation Models (passage 2)
- Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative modelsChenling Xu; Romain Lopez; Edouard Mehlman; et al. 2021. Molecular Systems Biologyjournal articleCited in: Single-Cell Foundation Models
- scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq dataFan Yang; Wenchuan Wang; Fang Wang; et al. 2022. Nature Machine Intelligencejournal articleCited in: Single-Cell Foundation Models
Spatial Omics and Tissue Models
References
- Deep learning and alignment of spatially resolved single-cell transcriptomes with TangramTommaso Biancalani; Gabriele Scalia; Lorenzo Buffoni; et al. 2021. Nature Methodsjournal articleCited in: Spatial Omics and Tissue Models
- Novae: a graph-based foundation model for spatial transcriptomics dataQuentin Blampey; Hakim Benkirane; Nadège Bercovici; et al. 2025. Nature Methodsjournal articleCited in: Spatial Omics and Tissue Models (passage 1); Spatial Omics and Tissue Models (passage 2)
- Spatially resolved, highly multiplexed RNA profiling in single cellsKok Hao Chen; Alistair N. Boettiger; Jeffrey R. Moffitt; et al. 2015. Sciencejournal articleCited in: Spatial Omics and Tissue Models
- Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arraysAo Chen; Sha Liao; Mengnan Cheng; et al. 2022. Celljournal articleCited in: Spatial Omics and Tissue Models
- Profiling cell identity and tissue architecture with single-cell and spatial transcriptomicsGunsagar 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); Spatial Omics and Tissue Models (passage 2)
- High-plex imaging of RNA and proteins at subcellular resolution in fixed tissue by spatial molecular imagingShanshan He; Ruchir Bhatt; Carl Brown; et al. 2022. Nature Biotechnologyjournal articleCited in: Spatial Omics and Tissue Models
- Cell2location maps fine-grained cell types in spatial transcriptomicsVitalii Kleshchevnikov; Artem Shmatko; Emma Dann; et al. 2022. Nature Biotechnologyjournal articleCited in: Spatial Omics and Tissue Models
- SpatialData: an open and universal data framework for spatial omicsLuca Marconato; Giovanni Palla; Kevin A. Yamauchi; et al. 2024. Nature Methodsjournal articleCited in: Spatial Omics and Tissue Models (passage 1); Spatial Omics and Tissue Models (passage 2)
- Visualization and analysis of gene expression in tissue sections by spatial transcriptomicsPatrik L. Ståhl; Fredrik Salmén; Sanja Vickovic; et al. 2016. Sciencejournal articleCited in: Spatial Omics and Tissue Models
- Nicheformer: a foundation model for single-cell and spatial omicsAlejandro Tejada-Lapuerta; Anna C. Schaar; Robert Gutgesell; et al. 2025. Nature Methodsjournal articleCited in: Spatial Omics and Tissue Models (passage 1); Spatial Omics and Tissue Models (passage 2)
- The Virtual Tissues foundation model resolves spatial proteomics across scalesJohann Wenckstern; Eeshaan Jain; Benedikt von Querfurth; et al. 2026. Naturejournal articleCited in: Spatial Omics and Tissue Models
Cell Painting and Image-Based Phenotyping
References
- Evaluating batch correction methods for image-based cell profilingJohn Arevalo; Ellen Su; Jessica D. Ewald; et al. 2024. Nature Communicationsjournal articleCited in: Cell Painting and Image-Based Phenotyping (passage 1); Cell Painting and Image-Based Phenotyping (passage 2)
- Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyesMark-Anthony Bray; Shantanu Singh; Han Han; et al. 2016. Nature Protocolsjournal articleCited in: Cell Painting and Image-Based Phenotyping (passage 1); Cell Painting and Image-Based Phenotyping (passage 2)
- Data-analysis strategies for image-based cell profilingJuan C Caicedo; Sam Cooper; Florian Heigwer; et al. 2017. Nature Methodsjournal articleCited in: Cell Painting and Image-Based Phenotyping
- CellProfiler: image analysis software for identifying and quantifying cell phenotypesAnne E Carpenter; Thouis R Jones; Michael R Lamprecht; et al. 2006. Genome Biologyjournal articleCited in: Cell Painting and Image-Based Phenotyping (passage 1); Cell Painting and Image-Based Phenotyping (passage 2)
- Three million images and morphological profiles of cells treated with matched chemical and genetic perturbationsSrinivas Niranj Chandrasekaran; Beth A. Cimini; Amy Goodale; et al. 2024. Nature Methodsjournal articleCited in: Cell Painting and Image-Based Phenotyping
- Morphological map of under- and overexpression of genes in human cellsSrinivas Niranj Chandrasekaran; Eric Alix; John Arevalo; et al. 2025. Nature Methodsjournal articleCited in: Cell Painting and Image-Based Phenotyping
- Optimizing the Cell Painting assay for image-based profilingBeth A. Cimini; Srinivas Niranj Chandrasekaran; Maria Kost-Alimova; et al. 2023. Nature Protocolsjournal articleCited in: Cell Painting and Image-Based Phenotyping (passage 1); Cell Painting and Image-Based Phenotyping (passage 2)
- CellProfiler 3.0: Next-generation image processing for biologyClaire McQuin; Allen Goodman; Vasiliy Chernyshev; et al. 2018. PLOS Biologyjournal articleCited in: Cell Painting and Image-Based Phenotyping (passage 1); Cell Painting and Image-Based Phenotyping (passage 2)
- Learning representations for image-based profiling of perturbationsNikita Moshkov; Michael Bornholdt; Santiago Benoit; et al. 2024. Nature Communicationsjournal articleCited in: Cell Painting and Image-Based Phenotyping
- A genome-wide atlas of human cell morphologyMeraj Ramezani; Erin Weisbart; Julia Bauman; et al. 2025. Nature Methodsjournal articleCited in: Cell Painting and Image-Based Phenotyping
- Toward performance-diverse small-molecule libraries for cell-based phenotypic screening using multiplexed high-dimensional profilingMathias 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
- Cell Painting Gallery: an open resource for image-based profilingErin Weisbart; Ankur Kumar; John Arevalo; et al. 2024. Nature Methodsjournal articleCited in: Cell Painting and Image-Based Phenotyping
- Cell Painting in activated cells illuminates phenotypic dark space and drug mechanisms of actionMatylda A. Zietek; Akshar Lohith; Derfel Terciano; et al. 2026. Nature Communicationsjournal articleCited in: Cell Painting and Image-Based Phenotyping
Histopathology AI
References
- Aiforia, 2026Cited in: Histopathology AI
- Clinical-grade computational pathology using weakly supervised deep learning on whole slide imagesGabriele Campanella; Matthew G. Hanna; Luke Geneslaw; et al. 2019. Nature Medicinejournal articleCited in: Histopathology AI
- Real-world deployment of a fine-tuned pathology foundation model for lung cancer biomarker detectionGabriele Campanella; Neeraj Kumar; Swaraj Nanda; et al. 2025. Nature Medicinejournal articleCited in: Histopathology AI
- Towards a general-purpose foundation model for computational pathologyRichard J. Chen; Tong Ding; Ming Y. Lu; et al. 2024. Nature Medicinejournal articleCited in: Histopathology AI
- Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learningNicolas Coudray; Paolo Santiago Ocampo; Theodore Sakellaropoulos; et al. 2018. Nature Medicinejournal articleCited in: Histopathology AI
- A multimodal whole-slide foundation model for pathologyTong Ding; Sophia J. Wagner; Andrew H. Song; et al. 2025. Nature Medicinejournal articleCited in: Histopathology AI
- ALPaCA: Adapting Llama for Pathology Context Analysis to enable slide-level question answeringZeyu Gao; Kai He; Weiheng Su; et al. 2026. Nature Communicationsjournal articleCited in: Histopathology AI
- Deep-learning triage of three-dimensional pathology datasets for comprehensive and efficient pathologist assessmentsGan Gao; Renao Yan; Andrew H. Song; et al. 2026. Nature Biomedical Engineeringjournal articleCited in: Histopathology AI
- Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology imagesSimon Graham; Quoc Dang Vu; Shan E Ahmed Raza; et al. 2019. Medical Image Analysisjournal articleCited in: Histopathology AI
- The impact of site-specific digital histology signatures on deep learning model accuracy and biasFrederick M. Howard; James Dolezal; Sara Kochanny; et al. 2021. Nature Communicationsjournal articleCited in: Histopathology AI
- Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancerJakob Nikolas Kather; Alexander T. Pearson; Niels Halama; et al. 2019. Nature Medicinejournal articleCited in: Histopathology AI
- Pan-cancer image-based detection of clinically actionable genetic alterationsJakob Nikolas Kather; Lara R. Heij; Heike I. Grabsch; et al. 2020. Nature Cancerjournal articleCited in: Histopathology AI
- A visual-language foundation model for computational pathologyMing Y. Lu; Bowen Chen; Drew F. K. Williamson; et al. 2024. Nature Medicinejournal articleCited in: Histopathology AI
- nnMIL: a generalizable multiple instance learning framework for computational pathologyXiangde Luo; Jinxi Xiang; Yuanfeng Ji; et al. 2026. Nature Biomedical Engineeringjournal articleCited in: Histopathology AI
- An explainable biomedical foundation model via large-scale concept-enhanced vision–language pretrainingYuxiang Nie; Sunan He; Yequan Bie; et al. 2026. Nature Biomedical Engineeringjournal articleCited in: Histopathology AI
- Owkin, 2026Cited in: Histopathology AI
- Paige, 2026Cited in: Histopathology AI
- PathAI, 2026Cited in: Histopathology AI
- Tempus, 2026Cited in: Histopathology AI
- A foundation model for clinical-grade computational pathology and rare cancers detectionEugene Vorontsov; Alican Bozkurt; Adam Casson; et al. 2024. Nature Medicinejournal articleCited in: Histopathology AI
- A pathology foundation model for cancer diagnosis and prognosis predictionXiyue Wang; Junhan Zhao; Eliana Marostica; et al. 2024. Naturejournal articleCited in: Histopathology AI
- Foundation Model for Predicting Prognosis and Adjuvant Therapy Benefit From Digital Pathology in GI CancersXiyue Wang; Yuming Jiang; Sen Yang; et al. 2025. Journal of Clinical Oncologyjournal articleCited in: Histopathology AI
- A vision–language foundation model for precision oncologyJinxi Xiang; Xiyue Wang; Xiaoming Zhang; et al. 2025. Naturejournal articleCited in: Histopathology AI
- A whole-slide foundation model for digital pathology from real-world dataHanwen Xu; Naoto Usuyama; Jaspreet Bagga; et al. 2024. Naturejournal articleCited in: Histopathology AI
Microscopy and Cryo-EM AI
References
- Segment Anything for MicroscopyAnwai Archit; Luca Freckmann; Sushmita Nair; et al. 2025. Nature Methodsjournal articleCited in: Microscopy and Cryo-EM AI
- Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographsTristan Bepler; Andrew Morin; Micah Rapp; et al. 2019. Nature Methodsjournal articleCited in: Microscopy and Cryo-EM AI
- In Silico Labeling: Predicting Fluorescent Labels in Unlabeled ImagesEric M. Christiansen; Samuel J. Yang; D. Michael Ando; et al. 2018. Celljournal articleCited in: Microscopy and Cryo-EM AI
- Automated model building and protein identification in cryo-EM mapsKiarash Jamali; Lukas Käll; Rui Zhang; et al. 2024. Naturejournal articleCited in: Microscopy and Cryo-EM AI
- Noise2Void - Learning Denoising From Single Noisy ImagesAlexander 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
- Avoiding a replication crisis in deep-learning-based bioimage analysisRomain F. Laine; Ignacio Arganda-Carreras; Ricardo Henriques; et al. 2021. Nature Methodsjournal articleCited in: Microscopy and Cryo-EM AI
- Disruption of the research software landscape through AI software generationNelson D. Medina; Joergen M. R. Kornfeld. 2026. Nature Methodsjournal articleCited in: Microscopy and Cryo-EM AI
- Deep-STORM: super-resolution single-molecule microscopy by deep learningElias Nehme; Lucien E. Weiss; Tomer Michaeli; et al. 2018. Opticajournal articleCited in: Microscopy and Cryo-EM AI
- Deep learning massively accelerates super-resolution localization microscopyWei Ouyang; Andrey Aristov; Mickaël Lelek; et al. 2018. Nature Biotechnologyjournal articleCited in: Microscopy and Cryo-EM AI
- A realistic phantom dataset for benchmarking cryo-ET data annotationAriana Peck; Yue Yu; Jonathan Schwartz; et al. 2025. Nature Methodsjournal articleCited in: Microscopy and Cryo-EM AI
- Learning structural heterogeneity from cryo-electron sub-tomograms with tomoDRGNBarrett M. Powell; Joseph H. Davis. 2024. Nature Methodsjournal articleCited in: Microscopy and Cryo-EM AI
- cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determinationAli Punjani; John L Rubinstein; David J Fleet; et al. 2017. Nature Methodsjournal articleCited in: Microscopy and Cryo-EM AI
- RELION: Implementation of a Bayesian approach to cryo-EM structure determinationSjors H.W. Scheres. 2012. Journal of Structural Biologyjournal articleCited in: Microscopy and Cryo-EM AI
- Cell Detection with Star-Convex PolygonsUwe Schmidt; Martin Weigert; Coleman Broaddus; et al. 2018. Lecture Notes in Computer Sciencebook chapterCited in: Microscopy and Cryo-EM AI
- DynaMight: estimating molecular motions with improved reconstruction from cryo-EM imagesJohannes Schwab; Dari Kimanius; Alister Burt; et al. 2024. Nature Methodsjournal articleCited in: Microscopy and Cryo-EM AI
- Cellpose: a generalist algorithm for cellular segmentationCarsen Stringer; Tim Wang; Michalis Michaelos; et al. 2020. Nature Methodsjournal articleCited in: Microscopy and Cryo-EM AI
- Cellpose3: one-click image restoration for improved cellular segmentationCarsen Stringer; Marius Pachitariu. 2025. Nature Methodsjournal articleCited in: Microscopy and Cryo-EM AI
- Democratising deep learning for microscopy with ZeroCostDL4MicLucas von Chamier; Romain F. Laine; Johanna Jukkala; et al. 2021. Nature Communicationsjournal articleCited in: Microscopy and Cryo-EM AI
- SPHIRE-crYOLO is a fast and accurate fully automated particle picker for cryo-EMThorsten Wagner; Felipe Merino; Markus Stabrin; et al. 2019. Communications Biologyjournal articleCited in: Microscopy and Cryo-EM AI
- Content-aware image restoration: pushing the limits of fluorescence microscopyMartin Weigert; Uwe Schmidt; Tobias Boothe; et al. 2018. Nature Methodsjournal articleCited in: Microscopy and Cryo-EM AI
- CryoDRGN: reconstruction of heterogeneous cryo-EM structures using neural networksEllen D. Zhong; Tristan Bepler; Bonnie Berger; et al. 2021. Nature Methodsjournal articleCited in: Microscopy and Cryo-EM AI
Perturbation Prediction and Virtual Cells
References
- A Multiplexed Single-Cell CRISPR Screening Platform Enables Systematic Dissection of the Unfolded Protein ResponseBritt Adamson; Thomas M. Norman; Marco Jost; et al. 2016. Celljournal articleCited in: Perturbation Prediction and Virtual Cells
- Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselinesConstantin Ahlmann-Eltze; Wolfgang Huber; Simon Anders. 2025. Nature Methodsjournal articleCited in: Perturbation Prediction and Virtual Cells
- Arc Institute, 2025Cited in: Perturbation Prediction and Virtual Cells (passage 1); Perturbation Prediction and Virtual Cells (passage 2)
- Learning single-cell perturbation responses using neural optimal transportCharlotte Bunne; Stefan G. Stark; Gabriele Gut; et al. 2023. Nature Methodsjournal articleCited in: Perturbation Prediction and Virtual Cells
- How to build the virtual cell with artificial intelligence: Priorities and opportunitiesCharlotte Bunne; Yusuf Roohani; Yanay Rosen; et al. 2024. Celljournal articleCited in: Perturbation Prediction and Virtual Cells
- Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic ScreensAtray Dixit; Oren Parnas; Biyu Li; et al. 2016. Celljournal articleCited in: Perturbation Prediction and Virtual Cells
- Multimodal pooled Perturb-CITE-seq screens in patient models define mechanisms of cancer immune evasionChris J. Frangieh; Johannes C. Melms; Pratiksha I. Thakore; et al. 2021. Nature Geneticsjournal articleCited in: Perturbation Prediction and Virtual Cells
- scPRAM accurately predicts single-cell gene expression perturbation response based on attention mechanismQun Jiang; Shengquan Chen; Xiaoyang Chen; et al. 2024. Bioinformaticsjournal articleCited in: Perturbation Prediction and Virtual Cells
- scGen predicts single-cell perturbation responsesMohammad Lotfollahi; F. Alexander Wolf; Fabian J. Theis. 2019. Nature Methodsjournal articleCited in: Perturbation Prediction and Virtual Cells
- Predicting cellular responses to complex perturbations in high‐throughput screensMohammad Lotfollahi; Anna Klimovskaia Susmelj; Carlo De Donno; et al. 2023. Molecular Systems Biologyjournal articleCited in: Perturbation Prediction and Virtual Cells
- World models for biomedicineAyush Noori; Nic Fishman; Ada Fang; Lukas Fesser; Marinka Zitnik. 2026. Celljournal articleCited in: Perturbation Prediction and Virtual Cells
- Exploring genetic interaction manifolds constructed from rich single-cell phenotypesThomas M. Norman; Max A. Horlbeck; Joseph M. Replogle; et al. 2019. Sciencejournal articleCited in: Perturbation Prediction and Virtual Cells
- Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seqJoseph M. Replogle; Reuben A. Saunders; Angela N. Pogson; et al. 2022. Celljournal articleCited in: Perturbation Prediction and Virtual Cells
- Predicting transcriptional outcomes of novel multigene perturbations with GEARSYusuf Roohani; Kexin Huang; Jure Leskovec. 2023. Nature Biotechnologyjournal articleCited in: Perturbation Prediction and Virtual Cells (passage 1); Perturbation Prediction and Virtual Cells (passage 2)
- Virtual Cell Challenge: Toward a Turing test for the virtual cellYusuf H. Roohani; Tony J. Hua; Po-Yuan Tung; et al. 2025. Celljournal articleCited in: Perturbation Prediction and Virtual Cells (passage 1); Perturbation Prediction and Virtual Cells (passage 2)
- A world model of the virtual cellEric P. Xing; Le Song. 2026. Celljournal articleCited in: Perturbation Prediction and Virtual Cells
Microbiome and Multi-Omics AI
References
- MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell dataRicard Argelaguet; Damien Arnol; Danila Bredikhin; et al. 2020. Genome Biologyjournal articleCited in: Microbiome and Multi-Omics AI
- Human Microbiome Project Consortium, 2012Cited in: Microbiome and Multi-Omics AI
- Evolutionary-scale prediction of atomic-level protein structure with a language modelZeming Lin; Halil Akin; Roshan Rao; et al. 2023. Sciencejournal articleCited in: Microbiome and Multi-Omics AI
- Multi-omics of the gut microbial ecosystem in inflammatory bowel diseasesJason Lloyd-Price; Cesar Arze; Ashwin N. Ananthakrishnan; et al. 2019. Naturejournal articleCited in: Microbiome and Multi-Omics AI (passage 1); Microbiome and Multi-Omics AI (passage 2)
- NIH Common FundCited in: Microbiome and Multi-Omics AI (passage 1); Microbiome and Multi-Omics AI (passage 2)
- Qin et al., 2010Cited in: Microbiome and Multi-Omics AI
- mixOmics: An R package for ‘omics feature selection and multiple data integrationFlorian Rohart; Benoît Gautier; Amrit Singh; et al. 2017. PLOS Computational Biologyjournal articleCited in: Microbiome and Multi-Omics AI
- Discovery of antimicrobial peptides in the global microbiome with machine learningCélio Dias Santos-Júnior; Marcelo D.T. Torres; Yiqian Duan; et al. 2024. Celljournal articleCited in: Microbiome and Multi-Omics AI
- A generalizable Hi-C foundation model for chromatin architecture, single-cell and multiomics analysis across speciesXiao Wang; Yuanyuan Zhang; Suhita Ray; et al. 2026. Nature Methodsjournal articleCited in: Microbiome and Multi-Omics AI
- High-resolution reconstruction of cell-type-specific transcriptional regulatory processes from bulk sequencing samplesLi Yao; Sagar R. Shah; Abdullah Ozer; et al. 2026. Nature Biotechnologyjournal articleCited in: Microbiome and Multi-Omics AI
- Personalized Nutrition by Prediction of Glycemic ResponsesDavid Zeevi; Tal Korem; Niv Zmora; et al. 2015. Celljournal articleCited in: Microbiome and Multi-Omics AI
Systems Biology and Multiscale Modeling
References
- SCENIC: single-cell regulatory network inference and clusteringSara Aibar; Carmen Bravo González-Blas; Thomas Moerman; et al. 2017. Nature Methodsjournal articleCited in: Systems Biology and Multiscale Modeling (passage 1); Systems Biology and Multiscale Modeling (passage 2); Systems Biology and Multiscale Modeling (passage 3)
- Gene regulatory network inference in the era of single-cell multi-omicsPau Badia-i-Mompel; Lorna Wessels; Sophia Müller-Dott; et al. 2023. Nature Reviews Geneticsjournal articleCited in: Systems Biology and Multiscale Modeling
- NicheNet: modeling intercellular communication by linking ligands to target genesRobin Browaeys; Wouter Saelens; Yvan Saeys. 2019. Nature Methodsjournal articleCited in: Systems Biology and Multiscale Modeling
- Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic ScreensAtray Dixit; Oren Parnas; Biyu Li; et al. 2016. Celljournal articleCited in: Systems Biology and Multiscale Modeling (passage 1); Systems Biology and Multiscale Modeling (passage 2)
- PhysiCell: An open source physics-based cell simulator for 3-D multicellular systemsAhmadreza Ghaffarizadeh; Randy Heiland; Samuel H. Friedman; et al. 2018. PLOS Computational Biologyjournal articleCited in: Systems Biology and Multiscale Modeling (passage 1); Systems Biology and Multiscale Modeling (passage 2)
- Dissecting cell identity via network inference and in silico gene perturbationKenji Kamimoto; Blerta Stringa; Christy M. Hoffmann; et al. 2023. Naturejournal articleCited in: Systems Biology and Multiscale Modeling (passage 1); Systems Biology and Multiscale Modeling (passage 2)
- A Whole-Cell Computational Model Predicts Phenotype from GenotypeJonathan R. Karr; Jayodita C. Sanghvi; Derek N. Macklin; et al. 2012. Celljournal articleCited in: Systems Biology and Multiscale Modeling (passage 1); Systems Biology and Multiscale Modeling (passage 2); Systems Biology and Multiscale Modeling (passage 3)
- SBML Level 3: an extensible format for the exchange and reuse of biological modelsSarah M Keating; Dagmar Waltemath; Matthias König; et al. 2020. Molecular Systems Biologyjournal articleCited in: Systems Biology and Multiscale Modeling (passage 1); Systems Biology and Multiscale Modeling (passage 2)
- BiGG Models: A platform for integrating, standardizing and sharing genome-scale modelsZachary A. King; Justin Lu; Andreas Dräger; et al. 2016. Nucleic Acids Researchjournal articleCited in: Systems Biology and Multiscale Modeling (passage 1); Systems Biology and Multiscale Modeling (passage 2)
- BioModels Database: a free, centralized database of curated, published, quantitative kinetic models of biochemical and cellular systemsN. Le Novere. 2006. Nucleic Acids Researchjournal articleCited in: Systems Biology and Multiscale Modeling (passage 1); Systems Biology and Multiscale Modeling (passage 2)
- What is flux balance analysis?Jeffrey D Orth; Ines Thiele; Bernhard Ø Palsson. 2010. Nature Biotechnologyjournal articleCited in: Systems Biology and Multiscale Modeling (passage 1); Systems Biology and Multiscale Modeling (passage 2)
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic dataAditya Pratapa; Amogh P. Jalihal; Jeffrey N. Law; et al. 2020. Nature Methodsjournal articleCited in: Systems Biology and Multiscale Modeling (passage 1); Systems Biology and Multiscale Modeling (passage 2)
- Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seqJoseph M. Replogle; Reuben A. Saunders; Angela N. Pogson; et al. 2022. Celljournal articleCited in: Systems Biology and Multiscale Modeling (passage 1); Systems Biology and Multiscale Modeling (passage 2)
- Morpheus: a user-friendly modeling environment for multiscale and multicellular systems biologyJörn Starruß; Walter de Back; Lutz Brusch; et al. 2014. Bioinformaticsjournal articleCited in: Systems Biology and Multiscale Modeling (passage 1); Systems Biology and Multiscale Modeling (passage 2)
- A community-driven global reconstruction of human metabolismInes Thiele; Neil Swainston; Ronan M T Fleming; et al. 2013. Nature Biotechnologyjournal articleCited in: Systems Biology and Multiscale Modeling
Part IV: Organismal and Environmental Biology
Neuroscience AI and Brain Foundation Models
References
- Azabou et al., 2023Cited in: Neuroscience AI and Brain Foundation Models (passage 1); Neuroscience AI and Brain Foundation Models (passage 2); Neuroscience AI and Brain Foundation Models (passage 3)
- BICAN, NIH BRAIN InitiativeCited in: Neuroscience AI and Brain Foundation Models
- BICAN, NIH BRAIN InitiativeCited in: Neuroscience AI and Brain Foundation Models (passage 1); Neuroscience AI and Brain Foundation Models (passage 2)
- Brain-ScoreCited in: Neuroscience AI and Brain Foundation Models
- DANDI ArchiveCited in: Neuroscience AI and Brain Foundation Models (passage 1); Neuroscience AI and Brain Foundation Models (passage 2)
- A large-scale standardized physiological survey reveals functional organization of the mouse visual cortexSaskia 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); Neuroscience AI and Brain Foundation Models (passage 2)
- Functional connectomics reveals general wiring rule in mouse visual cortexZhuokun Ding; Paul G. Fahey; Stelios Papadopoulos; et al. 2025. Naturejournal articleCited in: Neuroscience AI and Brain Foundation Models (passage 1)Recommended in: Neuroscience AI and Brain Foundation Models (passage 2)
- A high-performance neuroprosthesis for speech decoding and avatar controlSean L. Metzger; Kaylo T. Littlejohn; Alexander B. Silva; et al. 2023. Naturejournal articleCited in: Neuroscience AI and Brain Foundation Models (passage 1); Neuroscience AI and Brain Foundation Models (passage 2)
- MICrONS Consortium, 2025Cited in: Neuroscience AI and Brain Foundation Models (passage 1); Neuroscience AI and Brain Foundation Models (passage 3)Recommended in: Neuroscience AI and Brain Foundation Models (passage 2)
- Ortega Caro et al., 2024Cited in: Neuroscience AI and Brain Foundation Models (passage 1); Neuroscience AI and Brain Foundation Models (passage 2); Neuroscience AI and Brain Foundation Models (passage 3)
- Inferring single-trial neural population dynamics using sequential auto-encodersChethan Pandarinath; Daniel J. O’Shea; Jasmine Collins; et al. 2018. Nature Methodsjournal articleCited in: Neuroscience AI and Brain Foundation Models (passage 1); Neuroscience AI and Brain Foundation Models (passage 2); Neuroscience AI and Brain Foundation Models (passage 3)
- Learnable latent embeddings for joint behavioural and neural analysisSteffen Schneider; Jin Hwa Lee; Mackenzie Weygandt Mathis. 2023. Naturejournal articleCited in: Neuroscience AI and Brain Foundation Models (passage 1); Neuroscience AI and Brain Foundation Models (passage 2); Neuroscience AI and Brain Foundation Models (passage 3)
- Semantic reconstruction of continuous language from non-invasive brain recordingsJerry Tang; Amanda LeBel; Shailee Jain; et al. 2023. Nature Neurosciencejournal articleCited in: Neuroscience AI and Brain Foundation Models (passage 1); Neuroscience AI and Brain Foundation Models (passage 2)
- The Allen Mouse Brain Common Coordinate Framework: A 3D Reference AtlasQuanxin Wang; Song-Lin Ding; Yang Li; et al. 2020. Celljournal articleCited in: Neuroscience AI and Brain Foundation Models (passage 1); Neuroscience AI and Brain Foundation Models (passage 2); Neuroscience AI and Brain Foundation Models (passage 3)
- Foundation model of neural activity predicts response to new stimulus typesEric Y. Wang; Paul G. Fahey; Zhuokun Ding; et al. 2025. Naturejournal articleCited in: Neuroscience AI and Brain Foundation Models (passage 1); Neuroscience AI and Brain Foundation Models (passage 2); Neuroscience AI and Brain Foundation Models (passage 3)
- A high-performance speech neuroprosthesisFrancis R. Willett; Erin M. Kunz; Chaofei Fan; et al. 2023. Naturejournal articleCited in: Neuroscience AI and Brain Foundation Models (passage 1); Neuroscience AI and Brain Foundation Models (passage 2)
Aging and Longevity Biology AI
References
- DNA methylation aging clocks: challenges and recommendationsChristopher G. Bell; Robert Lowe; Peter D. Adams; et al. 2019. Genome Biologyjournal articleCited in: Aging and Longevity Biology AI
- DunedinPACE, a DNA methylation biomarker of the pace of agingDaniel W Belsky; Avshalom Caspi; David L Corcoran; et al. 2022. eLifejournal articleCited in: Aging and Longevity Biology AI (passage 1); Aging and Longevity Biology AI (passage 2); Aging and Longevity Biology AI (passage 3); Aging and Longevity Biology AI (passage 4)
- CellAge, Human Ageing Genomic ResourcesCited in: Aging and Longevity Biology AI (passage 1); Aging and Longevity Biology AI (passage 2); Aging and Longevity Biology AI (passage 3)
- Genome-wide Methylation Profiles Reveal Quantitative Views of Human Aging RatesGregory Hannum; Justin Guinney; Ling Zhao; et al. 2013. Molecular Celljournal articleCited in: Aging and Longevity Biology AI (passage 1); Aging and Longevity Biology AI (passage 2); Aging and Longevity Biology AI (passage 3)
- Rapamycin fed late in life extends lifespan in genetically heterogeneous miceDavid E. Harrison; Randy Strong; Zelton Dave Sharp; et al. 2009. Naturejournal articleCited in: Aging and Longevity Biology AI
- DNA methylation age of human tissues and cell typesSteve Horvath. 2013. Genome Biologyjournal articleCited in: Aging and Longevity Biology AI (passage 1); Aging and Longevity Biology AI (passage 2); Aging and Longevity Biology AI (passage 3)
- Geroscience: Linking Aging to Chronic DiseaseBrian K. Kennedy; Shelley L. Berger; Anne Brunet; et al. 2014. Celljournal articleCited in: Aging and Longevity Biology AI
- Undulating changes in human plasma proteome profiles across the lifespanBenoit Lehallier; David Gate; Nicholas Schaum; et al. 2019. Nature Medicinejournal articleCited in: Aging and Longevity Biology AI (passage 1); Aging and Longevity Biology AI (passage 2)
- An epigenetic biomarker of aging for lifespan and healthspanMorgan E. Levine; Ake T. Lu; Austin Quach; et al. 2018. Agingjournal articleCited in: Aging and Longevity Biology AI (passage 1); Aging and Longevity Biology AI (passage 2); Aging and Longevity Biology AI (passage 3)
- DNA methylation GrimAge strongly predicts lifespan and healthspanAke T. Lu; Austin Quach; James G. Wilson; et al. 2019. Agingjournal articleCited in: Aging and Longevity Biology AI (passage 1); Aging and Longevity Biology AI (passage 2); Aging and Longevity Biology AI (passage 3)
- Hallmarks of aging: An expanding universeCarlos López-Otín; Maria A. Blasco; Linda Partridge; et al. 2023. Celljournal articleCited in: Aging and Longevity Biology AI
- Validation of biomarkers of agingMahdi Moqri; Chiara Herzog; Jesse R. Poganik; et al. 2024. Nature Medicinejournal articleCited in: Aging and Longevity Biology AI (passage 1); Aging and Longevity Biology AI (passage 2)
- NIA ITP, 2026Cited in: Aging and Longevity Biology AI (passage 1); Aging and Longevity Biology AI (passage 2)
- NIH SenNetCited in: Aging and Longevity Biology AI (passage 1); Aging and Longevity Biology AI (passage 2); Aging and Longevity Biology AI (passage 3)
- Organ aging signatures in the plasma proteome track health and diseaseHamilton Se-Hwee Oh; Jarod Rutledge; Daniel Nachun; et al. 2023. Naturejournal articleCited in: Aging and Longevity Biology AI (passage 1); Aging and Longevity Biology AI (passage 2); Aging and Longevity Biology AI (passage 3)
- Effect of long-term caloric restriction on DNA methylation measures of biological aging in healthy adults from the CALERIE trialR. Waziry; C. P. Ryan; D. L. Corcoran; et al. 2023. Nature Agingjournal articleCited in: Aging and Longevity Biology AI
- An open benchmark and language models for AI in aging biologyAlex 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
Plant, Crop, and Agricultural AI
References
- Field high-throughput phenotyping: the new crop breeding frontierJosé Luis Araus; Jill E. Cairns. 2014. Trends in Plant Sciencejournal articleCited in: Plant, Crop, and Agricultural AI (passage 1)Recommended in: Plant, Crop, and Agricultural AI (passage 2)
- BrAPICited in: Plant, Crop, and Agricultural AI (passage 1); Plant, Crop, and Agricultural AI (passage 2)
- Breedbase AboutCited in: Plant, Crop, and Agricultural AI
- CGIAR EBS implementation noteCited in: Plant, Crop, and Agricultural AI (passage 1); Plant, Crop, and Agricultural AI (passage 2)
- CGIAR Open Access and Open DataCited in: Plant, Crop, and Agricultural AI (passage 1); Plant, Crop, and Agricultural AI (passage 2)
- Genomic Selection in Plant Breeding: Methods, Models, and PerspectivesJosé Crossa; Paulino Pérez-Rodríguez; Jaime Cuevas; et al. 2017. Trends in Plant Sciencejournal articleCited in: Plant, Crop, and Agricultural AI (passage 1); Plant, Crop, and Agricultural AI (passage 2)Recommended in: Plant, Crop, and Agricultural AI (passage 3)
- Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection MethodsEtienne David; Simon Madec; Pouria Sadeghi-Tehran; et al. 2020. Plant Phenomicsjournal articleCited in: Plant, Crop, and Agricultural AI (passage 1); Plant, Crop, and Agricultural AI (passage 2)Recommended in: Plant, Crop, and Agricultural AI (passage 3)
- Global Wheat Head Detection Challenges: Winning Models and Application for Head CountingEtienne David; Franklin Ogidi; Daniel Smith; et al. 2023. Plant Phenomicsjournal articleCited in: Plant, Crop, and Agricultural AI (passage 1); Plant, Crop, and Agricultural AI (passage 2)
- Ensembl PlantsCited in: Plant, Crop, and Agricultural AI (passage 1); Plant, Crop, and Agricultural AI (passage 2); Plant, Crop, and Agricultural AI (passage 3)
- FAOSTATCited in: Plant, Crop, and Agricultural AI (passage 1); Plant, Crop, and Agricultural AI (passage 2); Plant, Crop, and Agricultural AI (passage 3)
- GrameneCited in: Plant, Crop, and Agricultural AI (passage 1); Plant, Crop, and Agricultural AI (passage 2); Plant, Crop, and Agricultural AI (passage 3)
- A foundational large language model for edible plant genomesJavier Mendoza-Revilla; Evan Trop; Liam Gonzalez; et al. 2024. Communications Biologyjournal articleCited in: Plant, Crop, and Agricultural AI (passage 1); Plant, Crop, and Agricultural AI (passage 2); Plant, Crop, and Agricultural AI (passage 3)Recommended in: Plant, Crop, and Agricultural AI (passage 4)
- Prediction of Total Genetic Value Using Genome-Wide Dense Marker MapsT H E Meuwissen; B J Hayes; M E Goddard. 2001. Geneticsjournal articleCited in: Plant, Crop, and Agricultural AI (passage 1); Plant, Crop, and Agricultural AI (passage 2)Recommended in: Plant, Crop, and Agricultural AI (passage 3)
- Breedbase: a digital ecosystem for modern plant breedingNicolas Morales; Alex C Ogbonna; Bryan J Ellerbrock; et al. 2022. G3 Genes|Genomes|Geneticsjournal articleCited in: Plant, Crop, and Agricultural AI (passage 1); Plant, Crop, and Agricultural AI (passage 2)
- BrAPI—an application programming interface for plant breeding applicationsPeter Selby; Rafael Abbeloos; Jan Erik Backlund; et al. 2019. Bioinformaticsjournal articleCited in: Plant, Crop, and Agricultural AI (passage 1); Plant, Crop, and Agricultural AI (passage 2)
- Tseng et al., 2021Cited in: Plant, Crop, and Agricultural AI (passage 1); Plant, Crop, and Agricultural AI (passage 2)Recommended in: Plant, Crop, and Agricultural AI (passage 3)
- USDA NASS Quick StatsCited in: Plant, Crop, and Agricultural AI (passage 1); Plant, Crop, and Agricultural AI (passage 2); Plant, Crop, and Agricultural AI (passage 3)
- USDA-ARS GRINCited in: Plant, Crop, and Agricultural AI (passage 1); Plant, Crop, and Agricultural AI (passage 2)
- An interpretable RNA foundation model for exploring functional RNA motifs in plantsHaopeng Yu; Heng Yang; Wenqing Sun; et al. 2024. Nature Machine Intelligencejournal articleCited in: Plant, Crop, and Agricultural AI (passage 1); Plant, Crop, and Agricultural AI (passage 2); Plant, Crop, and Agricultural AI (passage 3)Recommended in: Plant, Crop, and Agricultural AI (passage 4)
Recommended reading
- BreedbaseRecommended in: Plant, Crop, and Agricultural AI
- Breeding crops to feed 10 billionLee T. Hickey; Amber N. Hafeez; Hannah Robinson; et al. 2019. Nature Biotechnologyjournal articleRecommended in: Plant, Crop, and Agricultural AI
Environmental and Ecological AI
References
- Standards for distribution models in biodiversity assessmentsMiguel B. Araújo; Robert P. Anderson; A. Márcia Barbosa; et al. 2019. Science Advancesjournal articleCited in: Environmental and Ecological AI (passage 1)Recommended in: Environmental and Ecological AI (passage 2)
- BirdNET publicationsCited in: Environmental and Ecological AI
- Environmental DNA metabarcoding: Transforming how we survey animal and plant communitiesKristy Deiner; Holly M. Bik; Elvira Mächler; et al. 2017. Molecular Ecologyjournal articleCited in: Environmental and Ecological AI (passage 1)Recommended in: Environmental and Ecological AI (passage 2)
- Iterative near-term ecological forecasting: Needs, opportunities, and challengesMichael 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)Recommended in: Environmental and Ecological AI (passage 2)
- eBird data productsCited in: Environmental and Ecological AI
- Species Distribution Models: Ecological Explanation and Prediction Across Space and TimeJane Elith; John R. Leathwick. 2009. Annual Review of Ecology, Evolution, and Systematicsjournal articleCited in: Environmental and Ecological AI (passage 1); Environmental and Ecological AI (passage 2)Recommended in: Environmental and Ecological AI (passage 3)
- GBIFCited in: Environmental and Ecological AI (passage 1); Environmental and Ecological AI (passage 2); Environmental and Ecological AI (passage 3)
- iNaturalist Research GradeCited in: Environmental and Ecological AI (passage 1); Environmental and Ecological AI (passage 2)
- iNaturalist research useCited in: Environmental and Ecological AI (passage 1); Environmental and Ecological AI (passage 2)
- BirdNET: A deep learning solution for avian diversity monitoringStefan Kahl; Connor M. Wood; Maximilian Eibl; et al. 2021. Ecological Informaticsjournal articleCited in: Environmental and Ecological AI (passage 1); Environmental and Ecological AI (passage 2)Recommended in: Environmental and Ecological AI (passage 3)
- Map of LifeCited in: Environmental and Ecological AI (passage 1); Environmental and Ecological AI (passage 2)
- NASA Earth Science DataCited in: Environmental and Ecological AI (passage 1); Environmental and Ecological AI (passage 2)
- NOAA CoastWatchCited in: Environmental and Ecological AI (passage 1); Environmental and Ecological AI (passage 2)
- Automatically identifying, counting, and describing wild animals in camera-trap images with deep learningMohammad Sadegh Norouzzadeh; Anh Nguyen; Margaret Kosmala; et al. 2018. Proceedings of the National Academy of Sciencesjournal articleCited in: Environmental and Ecological AI (passage 1)Recommended in: Environmental and Ecological AI (passage 2)
- NSF NEONCited in: Environmental and Ecological AI (passage 1); Environmental and Ecological AI (passage 2); Environmental and Ecological AI (passage 3)
- NSF NEON overviewCited in: Environmental and Ecological AI
- OBIS data accessCited in: Environmental and Ecological AI (passage 1); Environmental and Ecological AI (passage 2)
- OBIS portalCited in: Environmental and Ecological AI (passage 1); Environmental and Ecological AI (passage 2)
- Maximum entropy modeling of species geographic distributionsSteven J. Phillips; Robert P. Anderson; Robert E. Schapire. 2006. Ecological Modellingjournal articleCited in: Environmental and Ecological AI (passage 1); Environmental and Ecological AI (passage 2)Recommended in: Environmental and Ecological AI (passage 3)
- Harnessing artificial intelligence to fill global shortfalls in biodiversity knowledgeLaura J. Pollock; Justin Kitzes; Sara Beery; et al. 2025. Nature Reviews Biodiversityjournal articleCited in: Environmental and Ecological AI (passage 1)Recommended in: Environmental and Ecological AI (passage 2)
- Generative AI as a tool to accelerate the field of ecologyKasim Rafiq; Sara Beery; Meredith S. Palmer; et al. 2025. Nature Ecology & Evolutionjournal articleCited in: Environmental and Ecological AI (passage 1)Recommended in: Environmental and Ecological AI (passage 2)
- Cross‐validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structureDavid R. Roberts; Volker Bahn; Simone Ciuti; et al. 2017. Ecographyjournal articleCited in: Environmental and Ecological AI
- Towards next‐generation biodiversity assessment using DNA metabarcodingPIERRE TABERLET; ERIC COISSAC; FRANÇOIS POMPANON; et al. 2012. Molecular Ecologyjournal articleCited in: Environmental and Ecological AI (passage 1)Recommended in: Environmental and Ecological AI (passage 2)
- USGS LandsatCited in: Environmental and Ecological AI (passage 1); Environmental and Ecological AI (passage 2)
- Wildlife InsightsCited in: Environmental and Ecological AI (passage 1); Environmental and Ecological AI (passage 2); Environmental and Ecological AI (passage 3)
Virtual Organisms and Digital Biology
References
- Enhancing randomized clinical trials with digital twinsHossein Akbarialiabad; Amirmohammad Pasdar; Dédée F. Murrell; et al. 2025. npj Systems Biology and Applicationsjournal articleCited in: Virtual Organisms and Digital Biology
- How to build the virtual cell with artificial intelligence: Priorities and opportunitiesCharlotte Bunne; Yusuf Roohani; Yanay Rosen; et al. 2024. Celljournal articleCited in: Virtual Organisms and Digital Biology (passage 1); Virtual Organisms and Digital Biology (passage 2)
- Single-cell reconstruction of developmental trajectories during zebrafish embryogenesisJeffrey A. Farrell; Yiqun Wang; Samantha J. Riesenfeld; et al. 2018. Sciencejournal articleCited in: Virtual Organisms and Digital Biology (passage 1); Virtual Organisms and Digital Biology (passage 2)
- FDA, 2025Cited in: Virtual Organisms and Digital Biology
- FDA, Computational Modeling and Simulation CredibilityCited in: Virtual Organisms and Digital Biology (passage 1); Virtual Organisms and Digital Biology (passage 2)
- FDA, M15 General Principles for Model-Informed Drug DevelopmentCited in: Virtual Organisms and Digital Biology
- FDA, Model-Informed Drug Development Paired Meeting ProgramCited in: Virtual Organisms and Digital Biology
- FDA, Model-Informed Product DevelopmentCited in: Virtual Organisms and Digital Biology
- PhysiCell: An open source physics-based cell simulator for 3-D multicellular systemsAhmadreza Ghaffarizadeh; Randy Heiland; Samuel H. Friedman; et al. 2018. PLOS Computational Biologyjournal articleCited in: Virtual Organisms and Digital Biology
- The International Mouse Phenotyping Consortium: comprehensive knockout phenotyping underpinning the study of human diseaseTudor Groza; Federico Lopez Gomez; Hamed Haseli Mashhadi; et al. 2022. Nucleic Acids Researchjournal articleCited in: Virtual Organisms and Digital Biology (passage 1); Virtual Organisms and Digital Biology (passage 2)
- IMPCCited in: Virtual Organisms and Digital Biology
- A Whole-Cell Computational Model Predicts Phenotype from GenotypeJonathan R. Karr; Jayodita C. Sanghvi; Derek N. Macklin; et al. 2012. Celljournal articleCited in: Virtual Organisms and Digital Biology (passage 1); Virtual Organisms and Digital Biology (passage 2)
- The Evolution and Future Directions of PBPK Modeling in FDA Regulatory ReviewYangkexin Li; Henry Sun; Zuoli Zhang. 2025. Pharmaceuticsjournal articleCited in: Virtual Organisms and Digital Biology
- MGICited in: Virtual Organisms and Digital Biology (passage 1); Virtual Organisms and Digital Biology (passage 2)
- OpenWormCited in: Virtual Organisms and Digital Biology (passage 1); Virtual Organisms and Digital Biology (passage 2)
- A lineage-resolved molecular atlas of C. elegans embryogenesis at single-cell resolutionJonathan S. Packer; Qin Zhu; Chau Huynh; et al. 2019. Sciencejournal articleCited in: Virtual Organisms and Digital Biology (passage 1); Virtual Organisms and Digital Biology (passage 2)
- Digital twins, synthetic patient data, and in-silico trials: can they empower paediatric clinical trials?Mohan Pammi; Prakesh S Shah; Liu K Yang; et al. 2025. The Lancet Digital Healthjournal articleCited in: Virtual Organisms and Digital Biology
- Credibility assessment of in silico clinical trials for medical devicesPras Pathmanathan; Kenneth Aycock; Andreu Badal; et al. 2024. PLOS Computational Biologyjournal articleCited in: Virtual Organisms and Digital Biology
- PlantCVCited in: Virtual Organisms and Digital Biology (passage 1); Virtual Organisms and Digital Biology (passage 2)
- Towards the construction of a virtual yeastLiujia Qian; Zizhuo Zhou; Peijie Zhou; et al. 2026. Naturejournal articleCited in: Virtual Organisms and Digital Biology
- Virtual Cell Challenge: Toward a Turing test for the virtual cellYusuf H. Roohani; Tony J. Hua; Po-Yuan Tung; et al. 2025. Celljournal articleCited in: Virtual Organisms and Digital Biology (passage 1); Virtual Organisms and Digital Biology (passage 2)
- Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model VerificationJennifer E. Sager; Jingjing Yu; Isabelle Ragueneau-Majlessi; et al. 2015. Drug Metabolism and Dispositionjournal articleCited in: Virtual Organisms and Digital Biology
- Embryo-scale reverse genetics at single-cell resolutionLauren M. Saunders; Sanjay R. Srivatsan; Madeleine Duran; et al. 2023. Naturejournal articleCited in: Virtual Organisms and Digital Biology (passage 1); Virtual Organisms and Digital Biology (passage 2)
- How to build an AI-driven digital organismLe Song; Eran Segal; Eric Xing. 2026. Nature Medicinejournal articleCited in: Virtual Organisms and Digital Biology
- Morpheus: a user-friendly modeling environment for multiscale and multicellular systems biologyJörn Starruß; Walter de Back; Lutz Brusch; et al. 2014. Bioinformaticsjournal articleCited in: Virtual Organisms and Digital Biology
- TAIRCited in: Virtual Organisms and Digital Biology (passage 1); Virtual Organisms and Digital Biology (passage 2)
- Bringing the genetically minimal cell to life on a computer in 4DZane R. Thornburg; Andrew Maytin; Jiwoong Kwon; et al. 2026. Celljournal articleCited in: Virtual Organisms and Digital Biology (passage 1); Virtual Organisms and Digital Biology (passage 2)
- The structure of the nervous system of the nematode Caenorhabditis elegansJohn 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); Virtual Organisms and Digital Biology (passage 2)
- WormBaseCited in: Virtual Organisms and Digital Biology (passage 1); Virtual Organisms and Digital Biology (passage 2)
- Yao et al., 2023Cited in: Virtual Organisms and Digital Biology (passage 1); Virtual Organisms and Digital Biology (passage 2)
- ZFINCited in: Virtual Organisms and Digital Biology (passage 1); Virtual Organisms and Digital Biology (passage 2)
Part V: Therapeutic Discovery and Translation
Target Identification and Prioritization
References
- Open Targets Platform: facilitating therapeutic hypotheses building in drug discoveryAnnalisa Buniello; Daniel Suveges; Carlos Cruz-Castillo; et al. 2024. Nucleic Acids Researchjournal articleCited in: Target Identification and Prioritization
- FDA, 2026Cited in: Target Identification and Prioritization
- The druggable genome and support for target identification and validation in drug developmentChris Finan; Anna Gaulton; Felix A. Kruger; et al. 2017. Science Translational Medicinejournal articleCited in: Target Identification and Prioritization
- XunZi, an AI biologist, reveals disease-modifying targetsXinhe Huang; Junhong Qin; Fei Tang; et al. 2026. Nature Biomedical Engineeringjournal articleCited in: Target Identification and Prioritization
- Advancing target discovery through disease-specific integration of multi-modal target identification models and comprehensive benchmarking systemHowell Leung; Chengchen Duan; Wenhao Gou; et al. 2026. Scientific Reportsjournal articleCited in: Target Identification and Prioritization
- Refining the impact of genetic evidence on clinical successEric Vallabh Minikel; Jeffery L. Painter; Coco Chengliang Dong; et al. 2024. Naturejournal articleCited in: Target Identification and Prioritization
- The support of human genetic evidence for approved drug indicationsMatthew R Nelson; Hannah Tipney; Jeffery L Painter; et al. 2015. Nature Geneticsjournal articleCited in: Target Identification and Prioritization
- Open Targets Platform: supporting systematic drug–target identification and prioritisationDavid Ochoa; Andrew Hercules; Miguel Carmona; et al. 2020. Nucleic Acids Researchjournal articleCited in: Target Identification and Prioritization
- Ochoa et al., 2021Cited in: Target Identification and Prioritization (passage 1); Target Identification and Prioritization (passage 2)
- Genetic factors associated with reasons for clinical trial stoppageOlesya Razuvayevskaya; Irene Lopez; Ian Dunham; et al. 2024. Nature Geneticsjournal articleCited in: Target Identification and Prioritization
- From target discovery to clinical drug development with human geneticsKaterina Trajanoska; Claude Bhérer; Daniel Taliun; et al. 2023. Naturejournal articleCited in: Target Identification and Prioritization
- Defining a Cancer Dependency MapAviad Tsherniak; Francisca Vazquez; Phil G. Montgomery; et al. 2017. Celljournal articleCited in: Target Identification and Prioritization
Small Molecule Generation and ADMET
References
- Ahmad et al., 2022, preprintCited in: Small Molecule Generation and ADMET
- ApexGOCited in: Small Molecule Generation and ADMET
- REINVENT 2.0: An AI Tool for De Novo Drug DesignThomas Blaschke; Josep Arús-Pous; Hongming Chen; et al. 2020. Journal of Chemical Information and Modelingjournal articleCited in: Small Molecule Generation and ADMET
- GuacaMol: Benchmarking Models for de Novo Molecular DesignNathan Brown; Marco Fiscato; Marwin H.S. Segler; et al. 2019. Journal of Chemical Information and Modelingjournal articleCited in: Small Molecule Generation and ADMET
- PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequencesMartin Buttenschoen; Garrett M. Morris; Charlotte M. Deane. 2024. Chemical Sciencejournal articleCited in: Small Molecule Generation and ADMET
- CAMPERCited in: Small Molecule Generation and ADMET
- Corso et al., 2023, preprintCited in: Small Molecule Generation and ADMET
- Pretraining a foundation model for small-molecule natural productsYuheng Ding; Bo Qiang; Shaoning Li; et al. 2026. Nature Machine Intelligencejournal articleCited in: Small Molecule Generation and ADMET
- Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributionsPeter Ertl; Ansgar Schuffenhauer. 2009. Journal of Cheminformaticsjournal articleCited in: Small Molecule Generation and ADMET
- ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision supportLi Fu; Shaohua Shi; Jiacai Yi; et al. 2024. Nucleic Acids Researchjournal articleCited in: Small Molecule Generation and ADMET
- Artificial intelligence foundation for therapeutic scienceKexin Huang; Tianfan Fu; Wenhao Gao; et al. 2022. Nature Chemical Biologyjournal articleCited in: Small Molecule Generation and ADMET
- Chemformer: a pre-trained transformer for computational chemistryRoss Irwin; Spyridon Dimitriadis; Jiazhen He; et al. 2022. Machine Learning: Science and Technologyjournal articleCited in: Small Molecule Generation and ADMET
- Jin et al., 2018, preprintCited in: Small Molecule Generation and ADMET
- Lessons Learned in Empirical Scoring with smina from the CSAR 2011 Benchmarking ExerciseDavid Ryan Koes; Matthew P. Baumgartner; Carlos J. Camacho. 2013. Journal of Chemical Information and Modelingjournal articleCited in: Small Molecule Generation and ADMET
- Reinvent 4: Modern AI–driven generative molecule designHannes H. Loeffler; Jiazhen He; Alessandro Tibo; et al. 2024. Journal of Cheminformaticsjournal articleCited in: Small Molecule Generation and ADMET
- Augmenting large language models with chemistry toolsAndres M. Bran; Sam Cox; Oliver Schilter; et al. 2024. Nature Machine Intelligencejournal articleCited in: Small Molecule Generation and ADMET
- Re-evaluating retrosynthesis algorithms with SyntheseusKrzysztof Maziarz; Austin Tripp; Guoqing Liu; et al. 2025. Faraday Discussionsjournal articleCited in: Small Molecule Generation and ADMET
- Parallel tempered genetic algorithm guided by deep neural networks for inverse molecular designAkshatKumar Nigam; Robert Pollice; Alán Aspuru-Guzik. 2022. Digital Discoveryjournal articleCited in: Small Molecule Generation and ADMET
- Molecular de-novo design through deep reinforcement learningMarcus Olivecrona; Thomas Blaschke; Ola Engkvist; et al. 2017. Journal of Cheminformaticsjournal articleCited in: Small Molecule Generation and ADMET
- Boltz-2: Towards Accurate and Efficient Binding Affinity PredictionSaro Passaro; Gabriele Corso; Jeremy Wohlwend; et al. 2025preprintCited in: Small Molecule Generation and ADMET
- Peng et al., 2022, preprintCited in: Small Molecule Generation and ADMET
- Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation ModelsDaniil Polykovskiy; Alexander Zhebrak; Benjamin Sanchez-Lengeling; et al. 2020. Frontiers in Pharmacologyjournal articleCited in: Small Molecule Generation and ADMET
- A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical modelsFeng Ren; Alex Aliper; Jian Chen; et al. 2024. Nature Biotechnologyjournal articleCited in: Small Molecule Generation and ADMET (passage 1); Small Molecule Generation and ADMET (passage 2); Small Molecule Generation and ADMET (passage 3)
- CAMPER: mechanistic artificial intelligence for designing peptides that target MRSA persistersFadi Shehadeh; Biswajit Mishra; Raquel Ferrer-Espada; et al. 2026. Nature Communicationsjournal articleCited in: Small Molecule Generation and ADMET
- Time-Split Cross-Validation as a Method for Estimating the Goodness of Prospective Prediction.Robert P. Sheridan. 2013. Journal of Chemical Information and Modelingjournal articleCited in: Small Molecule Generation and ADMET
- Stärk et al., 2022, preprintCited in: Small Molecule Generation and ADMET
- ADMET-AI: a machine learning ADMET platform for evaluation of large-scale chemical librariesKyle Swanson; Parker Walther; Jeremy Leitz; et al. 2024. Bioinformaticsjournal articleCited in: Small Molecule Generation and ADMET
- SyntheMol-RL: a flexible reinforcement learning framework for designing easily synthesizable antibioticsKyle Swanson; Gary Liu; Denise B Catacutan; et al. 2026. Molecular Systems Biologyjournal articleCited in: Small Molecule Generation and ADMET
- SyntheMol-RLCited in: Small Molecule Generation and ADMET
- A generative artificial intelligence approach for peptide antibiotic optimizationMarcelo D. T. Torres; Yimeng Zeng; Fangping Wan; et al. 2026. Nature Machine Intelligencejournal articleCited in: Small Molecule Generation and ADMET
- AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreadingOleg Trott; Arthur J. Olson. 2009. Journal of Computational Chemistryjournal articleCited in: Small Molecule Generation and ADMET
- MoleculeNet: a benchmark for molecular machine learningZhenqin Wu; Bharath Ramsundar; Evan N. Feinberg; et al. 2018. Chemical Sciencejournal articleCited in: Small Molecule Generation and ADMET
- A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trialZuojun Xu; Feng Ren; Ping Wang; et al. 2025. Nature Medicinejournal articleCited in: Small Molecule Generation and ADMET (passage 1); Small Molecule Generation and ADMET (passage 2); Small Molecule Generation and ADMET (passage 3)
- Analyzing Learned Molecular Representations for Property PredictionKevin Yang; Kyle Swanson; Wengong Jin; et al. 2019. Journal of Chemical Information and Modelingjournal articleCited in: Small Molecule Generation and ADMET
- Zagribelnyy et al., 2026, preprintCited in: Small Molecule Generation and ADMET
- Integration of proteomic aging clocks in a phase 2a clinical trial supports simultaneous geroprotective assessmentAlex Zhavoronkov; Fedor Galkin; Shan Chen; et al. 2026. Nature Biotechnologyjournal articleCited in: Small Molecule Generation and ADMET
Chemical Biology and Target Engagement
References
- The promise and peril of chemical probesCheryl H Arrowsmith; James E Audia; Christopher Austin; et al. 2015. Nature Chemical Biologyjournal articleCited in: Chemical Biology and Target Engagement
- Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyesMark-Anthony Bray; Shantanu Singh; Han Han; et al. 2016. Nature Protocolsjournal articleCited in: Chemical Biology and Target Engagement (passage 1); Chemical Biology and Target Engagement (passage 2)
- Quantitative and interface-aware prediction of peptide–protein interactions by VITALWei-Hao Chen; Qi-Wen Wang; Zhi-Yi Li; et al. 2026. Nature Machine Intelligencejournal articleCited in: Chemical Biology and Target Engagement
- Optimizing the Cell Painting assay for image-based profilingBeth A. Cimini; Srinivas Niranj Chandrasekaran; Maria Kost-Alimova; et al. 2023. Nature Protocolsjournal articleCited in: Chemical Biology and Target Engagement
- Protein–ligand data at scale to support machine learningAled M. Edwards; Dafydd R. Owen; The Structural Genomics Consortium Target 2035 Working Group; et al. 2025. Nature Reviews Chemistryjournal articleCited in: Chemical Biology and Target Engagement (passage 1)Recommended in: Chemical Biology and Target Engagement (passage 2)
- Structural basis of PROTAC cooperative recognition for selective protein degradationMorgan S Gadd; Andrea Testa; Xavier Lucas; et al. 2017. Nature Chemical Biologyjournal articleCited in: Chemical Biology and Target Engagement (passage 1); Chemical Biology and Target Engagement (passage 2); Chemical Biology and Target Engagement (passage 3)
- Proteome-wide identification of the druggable CRBN interactomePius Galli; Shuhao Xiao; Yanxiang Meng; et al. 2026. Nature Biotechnologyjournal articleCited in: Chemical Biology and Target Engagement
- BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacologyMichael K. Gilson; Tiqing Liu; Michael Baitaluk; et al. 2015. Nucleic Acids Researchjournal articleCited in: Chemical Biology and Target Engagement
- PubChem 2023 updateSunghwan Kim; Jie Chen; Tiejun Cheng; et al. 2022. Nucleic Acids Researchjournal articleCited in: Chemical Biology and Target Engagement
- Rational discovery of molecular glue degraders via scalable chemical profilingCristina Mayor-Ruiz; Sophie Bauer; Matthias Brand; et al. 2020. Nature Chemical Biologyjournal articleCited in: Chemical Biology and Target Engagement (passage 1); Chemical Biology and Target Engagement (passage 2)
- Monitoring Drug Target Engagement in Cells and Tissues Using the Cellular Thermal Shift AssayDaniel Martinez Molina; Rozbeh Jafari; Marina Ignatushchenko; et al. 2013. Sciencejournal articleCited in: Chemical Biology and Target Engagement (passage 1); Chemical Biology and Target Engagement (passage 2)
- Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better BenchmarkingMichael M. Mysinger; Michael Carchia; John. J. Irwin; et al. 2012. Journal of Medicinal Chemistryjournal articleCited in: Chemical Biology and Target Engagement (passage 1); Chemical Biology and Target Engagement (passage 2)
- Protacs: Chimeric molecules that target proteins to the Skp1–Cullin–F box complex for ubiquitination and degradationKathleen 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); Chemical Biology and Target Engagement (passage 2)
- Tracking cancer drugs in living cells by thermal profiling of the proteomeMikhail M. Savitski; Friedrich B. M. Reinhard; Holger Franken; et al. 2014. Sciencejournal articleCited in: Chemical Biology and Target Engagement (passage 1); Chemical Biology and Target Engagement (passage 2)
- A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 ProfilesAravind Subramanian; Rajiv Narayan; Steven M. Corsello; et al. 2017. Celljournal articleCited in: Chemical Biology and Target Engagement (passage 1); Chemical Biology and Target Engagement (passage 2)
Drug Repurposing and Combination Therapy
References
- Building a knowledge graph to enable precision medicinePayal Chandak; Kexin Huang; Marinka Zitnik. 2023. Scientific Datajournal articleCited in: Drug Repurposing and Combination Therapy
- Network-based approach to prediction and population-based validation of in silico drug repurposingFeixiong Cheng; Rishi J. Desai; Diane E. Handy; et al. 2018. Nature Communicationsjournal articleCited in: Drug Repurposing and Combination Therapy
- Network-based prediction of drug combinationsFeixiong Cheng; István A. Kovács; Albert-László Barabási. 2019. Nature Communicationsjournal articleCited in: Drug Repurposing and Combination Therapy
- A SARS-CoV-2 protein interaction map reveals targets for drug repurposingDavid E. Gordon; Gwendolyn M. Jang; Mehdi Bouhaddou; et al. 2020. Naturejournal articleCited in: Drug Repurposing and Combination Therapy
- Drug screen and machine learning predict neuroprotective agents in a preclinical human model of childhood dementiaZarina Greenberg; Ella McDonald; Alejandra Noreña Puerta; et al. 2026. Nature Communicationsjournal articleCited in: Drug Repurposing and Combination Therapy
- Network-based in silico drug efficacy screeningEmre Guney; Jörg Menche; Marc Vidal; et al. 2016. Nature Communicationsjournal articleCited in: Drug Repurposing and Combination Therapy
- Systematic integration of biomedical knowledge prioritizes drugs for repurposingDaniel Scott Himmelstein; Antoine Lizee; Christine Hessler; et al. 2017. eLifejournal articleCited in: Drug Repurposing and Combination Therapy
- A foundation model for clinician-centered drug repurposingKexin Huang; Payal Chandak; Qianwen Wang; et al. 2024. Nature Medicinejournal articleCited in: Drug Repurposing and Combination Therapy
- SynergyFinder 2.0: visual analytics of multi-drug combination synergiesAleksandr Ianevski; Anil K Giri; Tero Aittokallio. 2020. Nucleic Acids Researchjournal articleCited in: Drug Repurposing and Combination Therapy
- The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and DiseaseJustin Lamb; Emily D. Crawford; David Peck; et al. 2006. Sciencejournal articleCited in: Drug Repurposing and Combination Therapy
- DeepSynergy: predicting anti-cancer drug synergy with Deep LearningKristina Preuer; Richard P I Lewis; Sepp Hochreiter; et al. 2017. Bioinformaticsjournal articleCited in: Drug Repurposing and Combination Therapy
- Drug repurposing: progress, challenges and recommendationsSudeep Pushpakom; Francesco Iorio; Patrick A. Eyers; et al. 2018. Nature Reviews Drug Discoveryjournal articleCited in: Drug Repurposing and Combination Therapy
- RECOVERY Collaborative Group, 2021Cited in: Drug Repurposing and Combination Therapy
- A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 ProfilesAravind Subramanian; Rajiv Narayan; Steven M. Corsello; et al. 2017. Celljournal articleCited in: Drug Repurposing and Combination Therapy
- Repurposed Antiviral Drugs for Covid-19 — Interim WHO Solidarity Trial ResultsWHO Solidarity Trial Consortium. 2021. New England Journal of Medicinejournal articleCited in: Drug Repurposing and Combination Therapy
mRNA, RNA, and Vaccine Design
References
- 100 Days MissionCited in: mRNA, RNA, and Vaccine Design
- Accurate structure prediction of biomolecular interactions with AlphaFold 3Josh Abramson; Jonas Adler; Jack Dunger; et al. 2024. Naturejournal articleCited in: mRNA, RNA, and Vaccine Design (passage 1); mRNA, RNA, and Vaccine Design (passage 2)
- AI and the Future of Medical Countermeasures to Protect Against Biological ThreatsAmesh A Adalja; Jaspreet Pannu; Thomas V Inglesby. 2026. Open Forum Infectious Diseasesjournal articleCited in: mRNA, RNA, and Vaccine Design
- Advancing regulatory variant effect prediction with AlphaGenomeŽiga Avsec; Natasha Latysheva; Jun Cheng; et al. 2026. Naturejournal articleCited in: mRNA, RNA, and Vaccine Design
- Orthrus: toward evolutionary and functional RNA foundation modelsPhilip Fradkin; Ruian “Ian” Shi; Taykhoom Dalal; et al. 2026. Nature Methodsjournal articleCited in: mRNA, RNA, and Vaccine Design (passage 1); mRNA, RNA, and Vaccine Design (passage 2)
- good AI practice principlesCited in: mRNA, RNA, and Vaccine Design
- Learning the language of viral evolution and escapeBrian Hie; Ellen D. Zhong; Bonnie Berger; et al. 2021. Sciencejournal articleCited in: mRNA, RNA, and Vaccine Design
- Advancing development of medical countermeasures: Incorporating COVID-19 lessons learned into future pandemic preparedness planningRobert A. Johnson; Richard C. White; Gary L. Disbrow. 2022. Human Vaccines & Immunotherapeuticsjournal articleCited in: mRNA, RNA, and Vaccine Design
- Suppression of RNA Recognition by Toll-like Receptors: The Impact of Nucleoside Modification and the Evolutionary Origin of RNAKatalin Karikó; Michael Buckstein; Houping Ni; et al. 2005. Immunityjournal articleCited in: mRNA, RNA, and Vaccine Design
- Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeuticsKathrin Leppek; Gun Woo Byeon; Wipapat Kladwang; et al. 2022. Nature Communicationsjournal articleCited in: mRNA, RNA, and Vaccine Design
- Sequence modeling and design from molecular to genome scale with EvoEric Nguyen; Michael Poli; Matthew G. Durrant; et al. 2024. Sciencejournal articleCited in: mRNA, RNA, and Vaccine Design (passage 1); mRNA, RNA, and Vaccine Design (passage 2)
- OpenAI, April 2024Cited in: mRNA, RNA, and Vaccine Design
- Pandemic Preparedness EngineCited in: mRNA, RNA, and Vaccine Design
- mRNA vaccines — a new era in vaccinologyNorbert Pardi; Michael J. Hogan; Frederick W. Porter; et al. 2018. Nature Reviews Drug Discoveryjournal articleCited in: mRNA, RNA, and Vaccine Design
- NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand dataBirkir Reynisson; Bruno Alvarez; Sinu Paul; et al. 2020. Nucleic Acids Researchjournal articleCited in: mRNA, RNA, and Vaccine Design (passage 1); mRNA, RNA, and Vaccine Design (passage 2)
- Human 5′ UTR design and variant effect prediction from a massively parallel translation assayPaul J. Sample; Ban Wang; David W. Reid; et al. 2019. Nature Biotechnologyjournal articleCited in: mRNA, RNA, and Vaccine Design
- Algorithm for optimized mRNA design improves stability and immunogenicityHe Zhang; Liang Zhang; Ang Lin; et al. 2023. Naturejournal articleCited in: mRNA, RNA, and Vaccine Design
Cell and Gene Therapy AI
References
- Search-and-replace genome editing without double-strand breaks or donor DNAAndrew V. Anzalone; Peyton B. Randolph; Jessie R. Davis; et al. 2019. Naturejournal articleCited in: Cell and Gene Therapy AI
- Deep diversification of an AAV capsid protein by machine learningDrew H. Bryant; Ali Bashir; Sam Sinai; et al. 2021. Nature Biotechnologyjournal articleCited in: Cell and Gene Therapy AI (passage 1); Cell and Gene Therapy AI (passage 2)
- Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9John G Doench; Nicolo Fusi; Meagan Sullender; et al. 2016. Nature Biotechnologyjournal articleCited in: Cell and Gene Therapy AI (passage 1); Cell and Gene Therapy AI (passage 2)
- FDA, 2020Cited in: Cell and Gene Therapy AI
- FDA, 2024Cited in: Cell and Gene Therapy AI (passage 1)Recommended in: Cell and Gene Therapy AI (passage 2)
- FDA, 2024Cited in: Cell and Gene Therapy AI (passage 1)Recommended in: Cell and Gene Therapy AI (passage 2)
- FDA, 2026Cited in: Cell and Gene Therapy AI (passage 1); Cell and Gene Therapy AI (passage 2); Cell and Gene Therapy AI (passage 3)
- FDA, 2026Cited in: Cell and Gene Therapy AI (passage 1)Recommended in: Cell and Gene Therapy AI (passage 2)
- Programmable base editing of A•T to G•C in genomic DNA without DNA cleavageNicole M. Gaudelli; Alexis C. Komor; Holly A. Rees; et al. 2017. Naturejournal articleCited in: Cell and Gene Therapy AI
- Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPORMaximilian Haeussler; Kai Schönig; Hélène Eckert; et al. 2016. Genome Biologyjournal articleCited in: Cell and Gene Therapy AI (passage 1); Cell and Gene Therapy AI (passage 2)
- Mechanistic machine learning for prediction of prime editing outcomesAlvin Hsu; Peter J. Chen; Angus H. Li; et al. 2026. Nature Biotechnologyjournal articleCited in: Cell and Gene Therapy AI (passage 1); Cell and Gene Therapy AI (passage 2)
- Programmable editing of a target base in genomic DNA without double-stranded DNA cleavageAlexis C. Komor; Yongjoo B. Kim; Michael S. Packer; et al. 2016. Naturejournal articleCited in: Cell and Gene Therapy AI
- CHANGE-seq reveals genetic and epigenetic effects on CRISPR–Cas9 genome-wide activityCicera R. Lazzarotto; Nikolay L. Malinin; Yichao Li; et al. 2020. Nature Biotechnologyjournal articleCited in: Cell and Gene Therapy AI (passage 1); Cell and Gene Therapy AI (passage 2)
- Precise DNA base editing using AlphaFold3-based contact modellingHaowei Meng; Zhixin Lei; Yongchang Yan; et al. 2026. Naturejournal articleCited in: Cell and Gene Therapy AI
- Comprehensive AAV capsid fitness landscape reveals a viral gene and enables machine-guided designPierce J. Ogden; Eric D. Kelsic; Sam Sinai; et al. 2019. Sciencejournal articleCited in: Cell and Gene Therapy AI (passage 1); Cell and Gene Therapy AI (passage 2)
- GUIDE-seq enables genome-wide profiling of off-target cleavage by CRISPR-Cas nucleasesShengdar Q Tsai; Zongli Zheng; Nhu T Nguyen; et al. 2014. Nature Biotechnologyjournal articleCited in: Cell and Gene Therapy AI (passage 1); Cell and Gene Therapy AI (passage 2)
- CIRCLE-seq: a highly sensitive in vitro screen for genome-wide CRISPR–Cas9 nuclease off-targetsShengdar Q Tsai; Nhu T Nguyen; Jose Malagon-Lopez; et al. 2017. Nature Methodsjournal articleCited in: Cell and Gene Therapy AI (passage 1); Cell and Gene Therapy AI (passage 2)
Diagnostics and Biomarker Translation
References
- STARD 2015: an updated list of essential items for reporting diagnostic accuracy studiesPatrick M Bossuyt; Johannes B Reitsma; David E Bruns; et al. 2015. BMJjournal articleCited in: Diagnostics and Biomarker Translation
- Detection and localization of surgically resectable cancers with a multi-analyte blood testJoshua D. Cohen; Lu Li; Yuxuan Wang; et al. 2018. Sciencejournal articleCited in: Diagnostics and Biomarker Translation (passage 1); Diagnostics and Biomarker Translation (passage 2)
- Genome-wide cell-free DNA fragmentation in patients with cancerStephen Cristiano; Alessandro Leal; Jillian Phallen; et al. 2019. Naturejournal articleCited in: Diagnostics and Biomarker Translation (passage 1); Diagnostics and Biomarker Translation (passage 2)
- Dermatologist-level classification of skin cancer with deep neural networksAndre Esteva; Brett Kuprel; Roberto A. Novoa; et al. 2017. Naturejournal articleCited in: Diagnostics and Biomarker Translation
- FDA, 2023Cited in: Diagnostics and Biomarker Translation (passage 1); Diagnostics and Biomarker Translation (passage 2)Recommended in: Diagnostics and Biomarker Translation (passage 3)
- FDA, 2026Cited in: Diagnostics and Biomarker Translation (passage 1)Recommended in: Diagnostics and Biomarker Translation (passage 2)
- FDA, 2026Cited in: Diagnostics and Biomarker Translation (passage 1); Diagnostics and Biomarker Translation (passage 2)Recommended in: Diagnostics and Biomarker Translation (passage 3)
- FDA-NIH Biomarker Working Group, 2025Cited in: Diagnostics and Biomarker Translation
- HTAN, 2026Cited in: Diagnostics and Biomarker Translation
- Clinical validation of a targeted methylation-based multi-cancer early detection test using an independent validation setE.A. Klein; D. Richards; A. Cohn; et al. 2021. Annals of Oncologyjournal articleCited in: Diagnostics and Biomarker Translation (passage 1); Diagnostics and Biomarker Translation (passage 2)
- Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK)Lisa M. McShane; Douglas G. Altman; Willi Sauerbrei; et al. 2005. JNCI: Journal of the National Cancer Institutejournal articleCited in: Diagnostics and Biomarker Translation
- NCI OCCPR, 2026Cited in: Diagnostics and Biomarker Translation
- NCI TCGA, 2026Cited in: Diagnostics and Biomarker Translation
Clinical Trial AI for Translational Research
References
- Real-world validation of a multimodal LLM-powered pipeline for high-accuracy clinical trial patient matchingAnatole Callies; Quentin Bodinier; Philippe Ravaud; et al. 2025. Communications Medicinejournal articleCited in: Clinical Trial AI for Translational Research
- ClinicalTrials.gov, NCT05279755Cited in: Clinical Trial AI for Translational Research
- Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extensionSamantha Cruz Rivera; Xiaoxuan Liu; An-Wen Chan; et al. 2020. Nature Medicinejournal articleCited in: Clinical Trial AI for Translational Research
- EMA, 2024Cited in: Clinical Trial AI for Translational Research (passage 1); Clinical Trial AI for Translational Research (passage 2)
- FDA, 2026Cited in: Clinical Trial AI for Translational Research
- FDA, 2026Cited in: Clinical Trial AI for Translational Research (passage 1); Clinical Trial AI for Translational Research (passage 2)
- FDA, 2026Cited in: Clinical Trial AI for Translational Research
- HINT: Hierarchical interaction network for clinical-trial-outcome predictionsTianfan Fu; Kexin Huang; Cao Xiao; et al. 2022. Patternsjournal articleCited in: Clinical Trial AI for Translational Research
- A large-scale database for clinical trial outcomes and featuresChufan Gao; Jathurshan Pradeepkumar; Trisha Das; et al. 2026. Nature Healthjournal articleCited in: Clinical Trial AI for Translational Research
- A prospective pragmatic evaluation of automatic trial matching tools in a molecular tumor boardLilia Gueguen; Louise Olgiati; Clément Brutti-Mairesse; et al. 2025. npj Precision Oncologyjournal articleCited in: Clinical Trial AI for Translational Research
- HHS, 2026Cited in: Clinical Trial AI for Translational Research
- Matching patients to clinical trials with large language modelsQiao Jin; Zifeng Wang; Charalampos S. Floudas; et al. 2024. Nature Communicationsjournal articleCited in: Clinical Trial AI for Translational Research
- Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extensionXiaoxuan Liu; Samantha Cruz Rivera; David Moher; et al. 2020. Nature Medicinejournal articleCited in: Clinical Trial AI for Translational Research
- Clinical Trial Notifications Triggered by Artificial Intelligence–Detected Cancer ProgressionTali Mazor; Karim S. Farhat; Pavel Trukhanov; et al. 2025. JAMA Network Openjournal articleCited in: Clinical Trial AI for Translational Research
- Adaptive designs in clinical trials: why use them, and how to run and report themPhilip Pallmann; Alun W. Bedding; Babak Choodari-Oskooei; et al. 2018. BMC Medicinejournal articleCited in: Clinical Trial AI for Translational Research
- Enriching Clinical Trials With Machine LearningRoy Perlis. 2026. JAMAjournal articleCited in: Clinical Trial AI for Translational Research
- ProJenX, September 2024Cited in: Clinical Trial AI for Translational Research
- Translating evidence into practice: adapting TrialGPT for real-world clinical trial eligibility screeningMahanazuddin Syed; Muayad Hamidi; Manju Bikkanuri; et al. 2026. Journal of the American Medical Informatics Associationjournal articleCited in: Clinical Trial AI for Translational Research
- Biotech's Lost ArchiveTeslo. 2025. Institute for ProgressCited in: Clinical Trial AI for Translational Research
- Teslo, 2026Cited in: Clinical Trial AI for Translational Research
- TGA, 2024Cited in: Clinical Trial AI for Translational Research
- Manual vs AI-Assisted Prescreening for Trial Eligibility Using Large Language Models—A Randomized Clinical TrialOzan Unlu; Matthew Varugheese; Jiyeon Shin; et al. 2025. JAMAjournal articleCited in: Clinical Trial AI for Translational Research
- FDA Perspective on the Regulation of Artificial Intelligence in Health Care and BiomedicineHaider J. Warraich; Troy Tazbaz; Robert M. Califf. 2025. JAMAjournal articleCited in: Clinical Trial AI for Translational Research
- Artificial intelligence in drug developmentKang Zhang; Xin Yang; Yifei Wang; et al. 2025. Nature Medicinejournal articleCited in: Clinical Trial AI for Translational Research
- The Virtual Biotech: A multi-agent AI framework for therapeutic discovery and developmentHarrison G. Zhang; Peter Eckmann; Jiacheng Miao; Andrew B. Mahon; James Zou. 2026. Sciencejournal articleCited in: Clinical Trial AI for Translational Research
Real-World Evidence and Biomarker AI
References
- Biomarkers Definitions Working Group, 2001Cited in: Real-World Evidence and Biomarker AI
- Real-World Evidence and Real-World Data for Evaluating Drug Safety and EffectivenessJacqueline Corrigan-Curay; Leonard Sacks; Janet Woodcock. 2018. JAMAjournal articleCited in: Real-World Evidence and Biomarker AI
- FDA, 2018Cited in: Real-World Evidence and Biomarker AI (passage 1); Real-World Evidence and Biomarker AI (passage 2)
- FDA, 2026Cited in: Real-World Evidence and Biomarker AI
- FDA, 2026Cited in: Real-World Evidence and Biomarker AI
- FDA, 2026Cited in: Real-World Evidence and Biomarker AI (passage 1); Real-World Evidence and Biomarker AI (passage 2)
- Biomarkers and surrogate endpoints in clinical trialsThomas R. Fleming; John H. Powers. 2012. Statistics in Medicinejournal articleCited in: Real-World Evidence and Biomarker AI
- When and How Can Real World Data Analyses Substitute for Randomized Controlled Trials?Jessica M. Franklin; Sebastian Schneeweiss. 2017. Clinical Pharmacology & Therapeuticsjournal articleCited in: Real-World Evidence and Biomarker AI
- Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available: Table 1.Miguel A. Hernán; James M. Robins. 2016. American Journal of Epidemiologyjournal articleCited in: Real-World Evidence and Biomarker AI
- Target Trial EmulationMiguel A. Hernán; Wei Wang; David E. Leaf. 2022. JAMAjournal articleCited in: Real-World Evidence and Biomarker AI
- “Target Trial Emulation” for Observational Studies — Potential and PitfallsRebecca A. Hubbard; Constantine A. Gatsonis; Joseph W. Hogan; et al. 2024. New England Journal of Medicinejournal articleCited in: Real-World Evidence and Biomarker AI
- Real-World Evidence — What Is It and What Can It Tell Us?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
- Tempus AI, 2024Cited in: Real-World Evidence and Biomarker AI
- High-throughput target trial emulation for Alzheimer’s disease drug repurposing with real-world dataChengxi Zang; Hao Zhang; Jie Xu; et al. 2023. Nature Communicationsjournal articleCited in: Real-World Evidence and Biomarker AI
Translational Evidence and Failure Modes
References
- Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselinesConstantin Ahlmann-Eltze; Wolfgang Huber; Simon Anders. 2025. Nature Methodsjournal articleCited in: Translational Evidence and Failure Modes (passage 1); Translational Evidence and Failure Modes (passage 2)
- PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequencesMartin Buttenschoen; Garrett M. Morris; Charlotte M. Deane. 2024. Chemical Sciencejournal articleCited in: Translational Evidence and Failure Modes (passage 1); Translational Evidence and Failure Modes (passage 2)
- Biomarkers and surrogate endpoints in clinical trialsThomas R. Fleming; John H. Powers. 2012. Statistics in Medicinejournal articleCited in: Translational Evidence and Failure Modes
- Clinical development success rates for investigational drugsMichael Hay; David W Thomas; John L Craighead; et al. 2014. Nature Biotechnologyjournal articleCited in: Translational Evidence and Failure Modes
- Diagnosing the decline in pharmaceutical R&D efficiencyJack W. Scannell; Alex Blanckley; Helen Boldon; et al. 2012. Nature Reviews Drug Discoveryjournal articleCited in: Translational Evidence and Failure Modes
- Decision Curve Analysis: A Novel Method for Evaluating Prediction ModelsAndrew J. Vickers; Elena B. Elkin. 2006. Medical Decision Makingjournal articleCited in: Translational Evidence and Failure Modes
- An analysis of the attrition of drug candidates from four major pharmaceutical companiesMichael J. Waring; John Arrowsmith; Andrew R. Leach; et al. 2015. Nature Reviews Drug Discoveryjournal articleCited in: Translational Evidence and Failure Modes
- MoleculeNet: a benchmark for molecular machine learningZhenqin Wu; Bharath Ramsundar; Evan N. Feinberg; et al. 2018. Chemical Sciencejournal articleCited in: Translational Evidence and Failure Modes
- Artificial intelligence in drug developmentKang Zhang; Xin Yang; Yifei Wang; et al. 2025. Nature Medicinejournal articleCited in: Translational Evidence and Failure Modes
Part VI: Research Systems and Automation
Self-Driving Laboratories
References
- Anthropic, 2026Cited in: Self-Driving Laboratories
- Autonomous chemical research with large language modelsDaniil A. Boiko; Robert MacKnight; Ben Kline; et al. 2023. Naturejournal articleCited in: Self-Driving Laboratories (passage 1); Self-Driving Laboratories (passage 2); Self-Driving Laboratories (passage 3)
- A mobile robotic chemistBenjamin Burger; Phillip M. Maffettone; Vladimir V. Gusev; et al. 2020. Naturejournal articleCited in: Self-Driving Laboratories (passage 1); Self-Driving Laboratories (passage 2)
- The past, present and future of self-driving laboratoriesRichard B. Canty; Milad Abolhasani. 2026. Nature Reviews Chemistryjournal articleCited in: Self-Driving Laboratories (passage 1); Self-Driving Laboratories (passage 2)
- Closing the Loop in AI-Driven Biomedical DiscoveryAda Fang; Kevin Li; Ayush Noori; et al. 2026preprintCited in: Self-Driving Laboratories
- A multi-agent system for automating scientific discoveryAli E. Ghareeb; Benjamin Chang; Ludovico Mitchener; et al. 2026. Naturejournal articleCited in: Self-Driving Laboratories (passage 1); Self-Driving Laboratories (passage 2); Self-Driving Laboratories (passage 3)
- Toward autonomous science with agentic artificial intelligenceLucie Y. Guo; Darren S.J. Ting; Xinyi Su; Alex Aliper; Alex Zhavoronkov; Daniel S.W. Ting. 2026. Celljournal articleCited in: Self-Driving Laboratories
- The Automation of ScienceRoss D. King; Jem Rowland; Stephen G. Oliver; et al. 2009. Sciencejournal articleCited in: Self-Driving Laboratories (passage 1); Self-Driving Laboratories (passage 2)
- Challenges in High-Throughput Inorganic Materials Prediction and Autonomous SynthesisJosh Leeman; Yuhan Liu; Joseph Stiles; et al. 2024. PRX Energyjournal articleCited in: Self-Driving Laboratories (passage 1); Self-Driving Laboratories (passage 2); Self-Driving Laboratories (passage 3)
- Augmenting large language models with chemistry toolsAndres M. Bran; Sam Cox; Oliver Schilter; et al. 2024. Nature Machine Intelligencejournal articleCited in: Self-Driving Laboratories
- Self-driving laboratory for accelerated discovery of thin-film materialsB. P. MacLeod; F. G. L. Parlane; T. D. Morrissey; et al. 2020. Science Advancesjournal articleCited in: Self-Driving Laboratories (passage 1); Self-Driving Laboratories (passage 2)
- Should We Teach AI a Better Scientific Method?James R. Neilson. 2023. Chemistry of Materialsjournal articleCited in: Self-Driving Laboratories (passage 1); Self-Driving Laboratories (passage 2); Self-Driving Laboratories (passage 3)
- AutoLabs: cognitive multi-agent systems with self-correction for autonomous chemical experimentationGihan Panapitiya; Emily Saldanha; Heather Job; et al. 2026. Scientific Reportsjournal articleCited in: Self-Driving Laboratories
- Large language models as uncertainty-calibrated optimizers for experimental discoveryBojana Ranković; Ryan-Rhys Griffiths; Philippe Schwaller. 2026. Nature Machine Intelligencejournal articleCited in: Self-Driving Laboratories
- ChemOS: An orchestration software to democratize autonomous discoveryLoïc M. Roch; Florian Häse; Christoph Kreisbeck; et al. 2020. PLOS ONEjournal articleCited in: Self-Driving Laboratories (passage 1); Self-Driving Laboratories (passage 2)
- Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving LabMartin Seifrid; Robert Pollice; Andrés Aguilar-Granda; et al. 2022. Accounts of Chemical Researchjournal articleCited in: Self-Driving Laboratories
- The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodiesKyle Swanson; Wesley Wu; Nash L. Bulaong; et al. 2025. Naturejournal articleCited in: Self-Driving Laboratories (passage 1); Self-Driving Laboratories (passage 2); Self-Driving Laboratories (passage 3)
- An autonomous laboratory for the accelerated synthesis of inorganic materialsNathan J. Szymanski; Bernardus Rendy; Yuxing Fei; et al. 2023. Naturejournal articleCited in: Self-Driving Laboratories (passage 1); Self-Driving Laboratories (passage 2); Self-Driving Laboratories (passage 3)
Robotic Lab Automation and Cloud Labs
References
- ARPA-H IGoR, 2026Cited in: Robotic Lab Automation and Cloud Labs
- Synthetic biology open language (SBOL) version 3.0.0Hasan Baig; Pedro Fontanarrosa; Vishwesh Kulkarni; et al. 2020. Journal of Integrative Bioinformaticsjournal articleCited in: Robotic Lab Automation and Cloud Labs
- A mobile robotic chemistBenjamin Burger; Phillip M. Maffettone; Vladimir V. Gusev; et al. 2020. Naturejournal articleCited in: Robotic Lab Automation and Cloud Labs (passage 1); Robotic Lab Automation and Cloud Labs (passage 2)
- PyLabRobot: An open-source, hardware-agnostic interface for liquid-handling robots and accessoriesRick P. Wierenga; Stefan M. Golas; Wilson Ho; et al. 2023. Devicejournal articleCited in: Robotic Lab Automation and Cloud Labs (passage 1); Robotic Lab Automation and Cloud Labs (passage 2); Robotic Lab Automation and Cloud Labs (passage 3)
- The FAIR Guiding Principles for scientific data management and stewardshipMark D. Wilkinson; Michel Dumontier; IJsbrand Jan Aalbersberg; et al. 2016. Scientific Datajournal articleCited in: Robotic Lab Automation and Cloud Labs
Synthetic Biology Design Tools
References
- Automated synthetic cell-based screening for designed proteins with emergent functionsKareem Al Nahas; Béla P. Frohn; Aleksandra Šakanović; et al. 2026. Nature Communicationsjournal articleCited in: Synthetic Biology Design Tools
- Protein design meets biosecurityDavid Baker; George Church. 2024. Sciencejournal articleCited in: Synthetic Biology Design Tools
- Genome modelling and design across all domains of life with Evo 2Garyk Brixi; Matthew G. Durrant; Jerome Ku; et al. 2026. Naturejournal articleCited in: Synthetic Biology Design Tools
- Optknock: A bilevel programming framework for identifying gene knockout strategies for microbial strain optimizationAnthony P. Burgard; Priti Pharkya; Costas D. Maranas. 2003. Biotechnology and Bioengineeringjournal articleCited in: Synthetic Biology Design Tools
- Nucleotide Transformer: building and evaluating robust foundation models for human genomicsHugo Dalla-Torre; Liam Gonzalez; Javier Mendoza-Revilla; et al. 2024. Nature Methodsjournal articleCited in: Synthetic Biology Design Tools
- Robust deep learning–based protein sequence design using ProteinMPNNJ. Dauparas; I. Anishchenko; N. Bennett; et al. 2022. Sciencejournal articleCited in: Synthetic Biology Design Tools (passage 1); Synthetic Biology Design Tools (passage 2)
- RetroPath2.0: A retrosynthesis workflow for metabolic engineersBaudoin Delépine; Thomas Duigou; Pablo Carbonell; et al. 2018. Metabolic Engineeringjournal articleCited in: Synthetic Biology Design Tools
- IGSC, 2024Cited in: Synthetic Biology Design Tools
- Ikonomova et al., 2025Cited in: Synthetic Biology Design Tools
- Inter-tool Analysis of a NIST Dataset for Assessing Baseline Nucleic Acid Sequence ScreeningTyler S. Laird; Kevin Flyangolts; Craig Bartling; et al. 2025. Applied Biosafetyjournal articleCited in: Synthetic Biology Design Tools
- Large language models generate functional protein sequences across diverse familiesAli Madani; Ben Krause; Eric R. Greene; et al. 2023. Nature Biotechnologyjournal articleCited in: Synthetic Biology Design Tools
- Semantic design of functional de novo genes from a genomic language modelAditi T. Merchant; Samuel H. King; Eric Nguyen; et al. 2025. Naturejournal articleCited in: Synthetic Biology Design Tools
- National Academies, 2025Cited in: Synthetic Biology Design Tools
- Sequence modeling and design from molecular to genome scale with EvoEric Nguyen; Michael Poli; Matthew G. Durrant; et al. 2024. Sciencejournal articleCited in: Synthetic Biology Design Tools (passage 1); Synthetic Biology Design Tools (passage 2)
- NIH Office of Science Policy, 2024Cited in: Synthetic Biology Design Tools
- A call for built-in biosecurity safeguards for generative AI toolsMengdi Wang; Zaixi Zhang; Amrit Singh Bedi; et al. 2025. Nature Biotechnologyjournal articleCited in: Synthetic Biology Design Tools
- De novo design of protein structure and function with RFdiffusionJoseph L. Watson; David Juergens; Nathaniel R. Bennett; et al. 2023. Naturejournal articleCited in: Synthetic Biology Design Tools (passage 1); Synthetic Biology Design Tools (passage 2)
AI for Biomanufacturing
References
- Digital Twins in Pharmaceutical and Biopharmaceutical Manufacturing: A Literature ReviewYingjie Chen; Ou Yang; Chaitanya Sampat; et al. 2020. Processesjournal articleCited in: AI for Biomanufacturing
- Cytiva, 2026Cited in: AI for Biomanufacturing
- FDA, 2004Cited in: AI for Biomanufacturing
- FDA, 2011Cited in: AI for Biomanufacturing
- Ginkgo Bioworks, 2026Cited in: AI for Biomanufacturing
- Machine learning in bioprocess development: from promise to practiceLaura M. Helleckes; Johannes Hemmerich; Wolfgang Wiechert; et al. 2023. Trends in Biotechnologyjournal articleCited in: AI for Biomanufacturing (passage 1); AI for Biomanufacturing (passage 2)
- ICH Q10, 2008Cited in: AI for Biomanufacturing
- ICH Q8(R2), 2009Cited in: AI for Biomanufacturing
- Review on machine learning-based bioprocess optimization, monitoring, and control systemsPartha Pratim Mondal; Abhinav Galodha; Vishal Kumar Verma; et al. 2023. Bioresource Technologyjournal articleCited in: AI for Biomanufacturing
- Resilience, 2026Cited in: AI for Biomanufacturing
- Sartorius, 2026Cited in: AI for Biomanufacturing
- Big data and machine learning driven bioprocessing – Recent trends and critical analysisChao-Tung Yang; Endah Kristiani; Yoong Kit Leong; et al. 2023. Bioresource Technologyjournal articleCited in: AI for Biomanufacturing
Agentic Science Workflows
References
- CellVoyager: AI CompBio agent generates new insights by autonomously analyzing biological dataSamuel Alber; Bowen Chen; Eric Sun; et al. 2026. Nature Methodsjournal articleCited in: Agentic Science Workflows (passage 1); Agentic Science Workflows (passage 2)
- ARPA-H IGoR, 2026Cited in: Agentic Science Workflows (passage 1); Agentic Science Workflows (passage 2)
- Autonomous chemical research with large language modelsDaniil A. Boiko; Robert MacKnight; Ben Kline; et al. 2023. Naturejournal articleCited in: Agentic Science Workflows (passage 1); Agentic Science Workflows (passage 2)
- Humans or LLMs as the Judge? A Study on Judgement BiasGuiming 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
- Codreanu, Imas, Mateos-Garcia et al., 2026Cited in: Agentic Science Workflows
- Closing the Loop in AI-Driven Biomedical DiscoveryAda Fang; Kevin Li; Ayush Noori; et al. 2026preprintCited in: Agentic Science Workflows
- A multi-agent system for automating scientific discoveryAli E. Ghareeb; Benjamin Chang; Ludovico Mitchener; et al. 2026. Naturejournal articleCited in: Agentic Science Workflows (passage 1); Agentic Science Workflows (passage 2)
- Accelerating scientific discovery with Co-ScientistJuraj Gottweis; Wei-Hung Weng; Alexander Daryin; et al. 2026. Naturejournal articleCited in: Agentic Science Workflows (passage 1); Agentic Science Workflows (passage 2)
- Toward autonomous science with agentic artificial intelligenceLucie Y. Guo; Darren S.J. Ting; Xinyi Su; Alex Aliper; Alex Zhavoronkov; Daniel S.W. Ting. 2026. Celljournal articleCited in: Agentic Science Workflows
- Agentic AI and the rise of in silico team science in biomedical researchBinglan Li; Anil Kumar Saini; Jose Guadalupe Hernandez; et al. 2026. Nature Biotechnologyjournal articleCited in: Agentic Science Workflows
- Augmenting large language models with chemistry toolsAndres M. Bran; Sam Cox; Oliver Schilter; et al. 2024. Nature Machine Intelligencejournal articleCited in: Agentic Science Workflows (passage 1); Agentic Science Workflows (passage 2)
- Paper2AgentJiacheng Miao. 2026. GitHubsoftwareCited in: Agentic Science Workflows
- Reimagining research papers as interactive and reliable AI agentsJiacheng Miao; Joe R. Davis; Yaohui Zhang; Jonathan K. Pritchard; James Zou. 2026. Naturejournal articleCited in: Agentic Science Workflows
- Nature, 2026Cited in: Agentic Science Workflows
- NIST AI 800-2, initial public draft, 2026Cited in: Agentic Science Workflows
- NIST, 2023Cited in: Agentic Science Workflows
- OpenAI, 2026Cited in: Agentic Science Workflows
- Phuong et al., 2026Cited in: Agentic Science Workflows
- The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodiesKyle Swanson; Wesley Wu; Nash L. Bulaong; et al. 2025. Naturejournal articleCited in: Agentic Science Workflows (passage 1); Agentic Science Workflows (passage 2)
- WHO, 2021Cited in: Agentic Science Workflows
- Yao et al., 2025Cited in: Agentic Science Workflows
- The Virtual Biotech: A multi-agent AI framework for therapeutic discovery and developmentHarrison G. Zhang; Peter Eckmann; Jiacheng Miao; Andrew B. Mahon; James Zou. 2026. Sciencejournal articleCited in: Agentic Science Workflows
Part VII: Evaluation, Practice, and Governance
Toolkit for AI-Augmented Bio Research
References
- Accurate structure prediction of biomolecular interactions with AlphaFold 3Josh Abramson; Jonas Adler; Jack Dunger; et al. 2024. Naturejournal articleCited in: Toolkit for AI-Augmented Bio Research
- Addendum: Accurate structure prediction of biomolecular interactions with AlphaFold 3Josh Abramson; Jonas Adler; Jack Dunger; et al. 2024. Naturejournal articleCited in: Toolkit for AI-Augmented Bio Research
- Basic local alignment search toolStephen F. Altschul; Warren Gish; Webb Miller; et al. 1990. Journal of Molecular Biologyjournal articleCited in: Toolkit for AI-Augmented Bio Research
- Anthropic, June 2026Cited in: Toolkit for AI-Augmented Bio Research
- GuacaMol: Benchmarking Models for de Novo Molecular DesignNathan Brown; Marco Fiscato; Marwin H.S. Segler; et al. 2019. Journal of Chemical Information and Modelingjournal articleCited in: Toolkit for AI-Augmented Bio Research
- Fast and sensitive protein alignment using DIAMONDBenjamin Buchfink; Chao Xie; Daniel H Huson. 2014. Nature Methodsjournal articleCited in: Toolkit for AI-Augmented Bio Research
- Accurate proteome-wide missense variant effect prediction with AlphaMissenseJun Cheng; Guido Novati; Joshua Pan; et al. 2023. Sciencejournal articleCited in: Toolkit for AI-Augmented Bio Research
- scGPT: toward building a foundation model for single-cell multi-omics using generative AIHaotian Cui; Chloe Wang; Hassaan Maan; et al. 2024. Nature Methodsjournal articleCited in: Toolkit for AI-Augmented Bio Research
- Robust deep learning–based protein sequence design using ProteinMPNNJ. Dauparas; I. Anishchenko; N. Bennett; et al. 2022. Sciencejournal articleCited in: Toolkit for AI-Augmented Bio Research
- Nextflow enables reproducible computational workflowsPaolo Di Tommaso; Maria Chatzou; Evan W Floden; et al. 2017. Nature Biotechnologyjournal articleCited in: Toolkit for AI-Augmented Bio Research
- Datasheets for datasetsTimnit Gebru; Jamie Morgenstern; Briana Vecchione; et al. 2021. Communications of the ACMjournal articleCited in: Toolkit for AI-Augmented Bio Research
- Large-scale foundation model on single-cell transcriptomicsMinsheng Hao; Jing Gong; Xin Zeng; et al. 2024. Nature Methodsjournal articleCited in: Toolkit for AI-Augmented Bio Research
- Landrum, 2014Cited in: Toolkit for AI-Augmented Bio Research
- Evolutionary-scale prediction of atomic-level protein structure with a language modelZeming Lin; Halil Akin; Roshan Rao; et al. 2023. Sciencejournal articleCited in: Toolkit for AI-Augmented Bio Research
- Disruption of the research software landscape through AI software generationNelson D. Medina; Joergen M. R. Kornfeld. 2026. Nature Methodsjournal articleCited in: Toolkit for AI-Augmented Bio Research
- ColabFold: making protein folding accessible to allMilot Mirdita; Konstantin Schütze; Yoshitaka Moriwaki; et al. 2022. Nature Methodsjournal articleCited in: Toolkit for AI-Augmented Bio Research
- Model Cards for Model ReportingMargaret 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
- Sustainable data analysis with SnakemakeFelix Mölder; Kim Philipp Jablonski; Brice Letcher; et al. 2021. F1000Researchjournal articleCited in: Toolkit for AI-Augmented Bio Research
- MMseqs2 enables sensitive protein sequence searching for the analysis of massive data setsMartin Steinegger; Johannes Söding. 2017. Nature Biotechnologyjournal articleCited in: Toolkit for AI-Augmented Bio Research
- Transfer learning enables predictions in network biologyChristina V. Theodoris; Ling Xiao; Anant Chopra; et al. 2023. Naturejournal articleCited in: Toolkit for AI-Augmented Bio Research
- DOME: recommendations for supervised machine learning validation in biologyIan Walsh; Dmytro Fishman; Dario Garcia-Gasulla; et al. 2021. Nature Methodsjournal articleCited in: Toolkit for AI-Augmented Bio Research
- De novo design of protein structure and function with RFdiffusionJoseph L. Watson; David Juergens; Nathaniel R. Bennett; et al. 2023. Naturejournal articleCited in: Toolkit for AI-Augmented Bio Research
- The FAIR Guiding Principles for scientific data management and stewardshipMark D. Wilkinson; Michel Dumontier; IJsbrand Jan Aalbersberg; et al. 2016. Scientific Datajournal articleCited in: Toolkit for AI-Augmented Bio Research
- SCANPY: large-scale single-cell gene expression data analysisF. Alexander Wolf; Philipp Angerer; Fabian J. Theis. 2018. Genome Biologyjournal articleCited in: Toolkit for AI-Augmented Bio Research
- MoleculeNet: a benchmark for molecular machine learningZhenqin Wu; Bharath Ramsundar; Evan N. Feinberg; et al. 2018. Chemical Sciencejournal articleCited in: Toolkit for AI-Augmented Bio Research
Benchmarks for Bio AI
References
- Anthropic, 2026, system cardCited in: Benchmarks for Bio AI
- Arc Institute, 2025Cited in: Benchmarks for Bio AI
- GuacaMol: Benchmarking Models for de Novo Molecular DesignNathan Brown; Marco Fiscato; Marwin H.S. Segler; et al. 2019. Journal of Chemical Information and Modelingjournal articleCited in: Benchmarks for Bio AI
- PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequencesMartin Buttenschoen; Garrett M. Morris; Charlotte M. Deane. 2024. Chemical Sciencejournal articleCited in: Benchmarks for Bio AI (passage 1); Benchmarks for Bio AI (passage 2)
- Continuous Automated Model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12Jürgen Haas; Alessandro Barbato; Dario Behringer; et al. 2017. Proteins: Structure, Function, and Bioinformaticsjournal articleCited in: Benchmarks for Bio AI (passage 1); Benchmarks for Bio AI (passage 2)
- Hong et al., 2026, preprintCited in: Benchmarks for Bio AI
- Artificial intelligence foundation for therapeutic scienceKexin Huang; Tianfan Fu; Wenhao Gao; et al. 2022. Nature Chemical Biologyjournal articleCited in: Benchmarks for Bio AI
- Expanding the AI evaluation toolbox with statistical modelsDrew Keller; Kweku Kwegyir-Aggrey; Ryan Steed; et al. 2026reportCited in: Benchmarks for Bio AI
- Kryshtafovych et al., 2024Cited in: Benchmarks for Bio AI (passage 1); Benchmarks for Bio AI (passage 2)
- Laurent et al., 2024, preprintCited in: Benchmarks for Bio AI
- Li et al., 2024, preprintCited in: Benchmarks for Bio AI
- Benchmarking atlas-level data integration in single-cell genomicsMalte D. Luecken; M. Büttner; K. Chaichoompu; et al. 2021. Nature Methodsjournal articleCited in: Benchmarks for Bio AI
- Defining and benchmarking open problems in single-cell analysisMalte D. Luecken; Scott Gigante; Daniel B. Burkhardt; et al. 2025. Nature Biotechnologyjournal articleCited in: Benchmarks for Bio AI
- Mouton et al., 2024Cited in: Benchmarks for Bio AI
- NIST AI 800-2, initial public draft, 2026Cited in: Benchmarks for Bio AI
- OpenAI, 2024Cited in: Benchmarks for Bio AI
- OpenAI, 2026, system cardCited in: Benchmarks for Bio AI
- Rao et al., 2026, preprintCited in: Benchmarks for Bio AI
- Virtual Cell Challenge: Toward a Turing test for the virtual cellYusuf H. Roohani; Tony J. Hua; Po-Yuan Tung; et al. 2025. Celljournal articleCited in: Benchmarks for Bio AI
- MoleculeNet: a benchmark for molecular machine learningZhenqin Wu; Bharath Ramsundar; Evan N. Feinberg; et al. 2018. Chemical Sciencejournal articleCited in: Benchmarks for Bio AI
- Zhang et al., 2026, preprintCited in: Benchmarks for Bio AI
- An open benchmark and language models for AI in aging biologyAlex 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
Reproducibility and Open Science
References
- AlphaFold Protein Structure Database, 2026Cited in: Reproducibility and Open Science
- ARPA-H IGoR, 2026Cited in: Reproducibility and Open Science
- Chai-1: Decoding the molecular interactions of lifeChai Discovery; Jacques Boitreaud; Jack Dent; et al. 2024preprintCited in: Reproducibility and Open Science
- ESM Metagenomic Atlas, 2026Cited in: Reproducibility and Open Science
- Open and sustainable AI: challenges, opportunities and the road ahead in the life sciencesGavin Farrell; Eleni Adamidi; Rafael Andrade Buono; et al. 2026. Nature Methodsjournal articleCited in: Reproducibility and Open Science
- Datasheets for datasetsTimnit Gebru; Jamie Morgenstern; Briana Vecchione; et al. 2021. Communications of the ACMjournal articleCited in: Reproducibility and Open Science
- Model Cards for Model ReportingMargaret Mitchell; Simone Wu; Andrew Zaldivar; et al. 2019. Proceedings of the Conference on Fairness, Accountability, and Transparencyproceedings articleCited in: Reproducibility and Open Science
- NIH Common FundCited in: Reproducibility and Open Science
- DOME: recommendations for supervised machine learning validation in biologyIan Walsh; Dmytro Fishman; Dario Garcia-Gasulla; et al. 2021. Nature Methodsjournal articleCited in: Reproducibility and Open Science (passage 1); Reproducibility and Open Science (passage 2); Reproducibility and Open Science (passage 3)
- The FAIR Guiding Principles for scientific data management and stewardshipMark D. Wilkinson; Michel Dumontier; IJsbrand Jan Aalbersberg; et al. 2016. Scientific Datajournal articleCited in: Reproducibility and Open Science
- Boltz-1 Democratizing Biomolecular Interaction ModelingJeremy Wohlwend; Gabriele Corso; Saro Passaro; et al. 2024preprintCited in: Reproducibility and Open Science (passage 1); Reproducibility and Open Science (passage 2)
Information Hazards in Capability Research
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Model and Dataset Index
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References
- DOI: 10.5281/zenodo.22073136Cited in: License