Appendix A — References
Master citation list for the Life Sciences AI Handbook, organized by topic area. Journal citations should resolve to the connected author, venue, year, and DOI record. Organizational, program, company, preprint, and regulatory claims should link to the controlling or primary source.
Protein Structure Prediction and Databases
- Jumper et al., 2021: AlphaFold 2, Nature
- Baek et al., 2021: RoseTTAFold, Science
- Lin et al., 2023: ESM-2 / ESMFold, Science
- Abramson et al., 2024: AlphaFold 3, Nature
- Evans et al., 2021, preprint: AlphaFold-Multimer, bioRxiv
- Wu et al., 2022, preprint: OmegaFold, bioRxiv
- Wohlwend et al., 2024, preprint: Boltz-1, bioRxiv
- Passaro et al., 2025, preprint: Boltz-2, bioRxiv
- Chai Discovery et al., 2024, preprint: Chai-1, bioRxiv
- Varadi et al., 2024: AlphaFold Protein Structure Database, NAR
- Varadi et al., 2022: Original AFDB paper, NAR
- Hayes et al., 2025: ESM-3 peer-reviewed, Science
- Rives et al., 2021: ESM-1b, PNAS
- Elnaggar et al., 2022: ProtBERT / ProtTrans, IEEE TPAMI
- Nijkamp et al., 2023: ProGen2, Cell Systems
Protein Design and Antibodies
- Watson et al., 2023: RFdiffusion, Nature
- Butcher et al., 2025, preprint: RFdiffusion3, bioRxiv
- Dauparas et al., 2022: ProteinMPNN, Science
- Dauparas et al., 2025: LigandMPNN, Nature Methods
- Ingraham et al., 2023: Chroma, Nature
- Alamdari et al., 2023, preprint: EvoDiff, bioRxiv
- Lisanza et al., 2025: ProteinGenerator, Nature Biotechnology
- Johnson et al., 2025: COMPSS enzyme-design filters, Nature Biotechnology
- Jiang et al., 2025: EVOLVEpro, Science
- Zambaldi et al., 2024, preprint: AlphaProteo, arXiv
- Anthropic, August 2026: Claude de novo protein binders, company report
- Bennett et al., 2026: RFantibody, Nature
- Ruffolo et al., 2023: IgFold, Nature Communications
- Olsen et al., 2022: AbLang, Bioinformatics Advances
- Hie et al., 2024: PLM-guided antibody evolution, Nature Biotechnology
- Shanker et al., 2024: Structure-informed language-model evolution, Science
- Leaver-Fay et al., 2011: Rosetta3, Methods in Enzymology
Single-Cell and Cellular AI
- Cui et al., 2024: scGPT, Nature Methods
- Theodoris et al., 2023: Geneformer, Nature
- Hao et al., 2024: scFoundation, Nature Methods
- Yang et al., 2022: scBERT, Nature Machine Intelligence
- Shen et al., 2023: tGPT, iScience
- Rosen et al., 2026: UCE, Nature
- Wen et al., 2023, preprint: CellPLM, bioRxiv
- Tejada-Lapuerta et al., 2025: Nicheformer, Nature Methods
- Heimberg et al., 2025: SCimilarity, Nature
- Tabula Sapiens Consortium, 2022: Tabula Sapiens, Science
- Regev et al., 2017: Human Cell Atlas, eLife
- Rood et al., 2025: HCA roadmap, Nature
- Luecken et al., 2022: scIB benchmark, Nature Methods
- Luecken et al., 2025: OpenProblems, Nature Biotechnology
- Ahlmann-Eltze et al., 2025: Linear baseline critique, Nature Methods
- Boiarsky et al., 2024: scFM deep-dive, Nature Machine Intelligence
- Bray et al., 2016: Cell Painting, Nature Protocols
- Cimini et al., 2023: Cell Painting v2, Nature Protocols
- Carpenter et al., 2006: CellProfiler, Genome Biology
- McQuin et al., 2018: CellProfiler 3.0, PLOS Biology
- Gulati et al., 2025: Spatial transcriptomics review, Nature Reviews MCB
- Marconato et al., 2025: SpatialData, Nature Methods
- Blampey et al., 2025: Novae spatial transcriptomics foundation model, Nature Methods
- Chandrasekaran et al., 2024: Matched chemical and genetic Cell Painting perturbations, Nature Methods
- Chandrasekaran et al., 2025: JUMP underexpression and overexpression morphology map, Nature Methods
- Ramezani et al., 2025: Genome-wide human cell morphology atlas, Nature Methods
- Santos-Júnior et al., 2024: Global microbiome antimicrobial peptide mining, Cell
Histopathology, Microscopy, and Cryo-EM
- Chen et al., 2024: UNI computational pathology foundation model, Nature Medicine
- Lu et al., 2024: CONCH visual-language pathology foundation model, Nature Medicine
- Vorontsov et al., 2024: Virchow pathology foundation model, Nature Medicine
- Xu et al., 2024: Prov-GigaPath whole-slide foundation model, Nature
- Wang et al., 2024: CHIEF pathology foundation model, Nature
- Xiang et al., 2025: MUSK vision-language pathology model, Nature
- Ding et al., 2025: TITAN multimodal whole-slide foundation model, Nature Medicine
- Graham et al., 2019: HoVer-Net, Medical Image Analysis
- Kather et al., 2019: Microsatellite-instability prediction from histology, Nature Medicine
- Kather et al., 2020: Pan-cancer genetic-alteration detection from histology, Nature Cancer
- Stringer et al., 2021: Cellpose, Nature Methods
- Stringer et al., 2025: Cellpose3, Nature Methods
- Schmidt et al., 2018: StarDist, MICCAI proceedings
- Archit et al., 2025: Segment Anything for Microscopy, Nature Methods
- Weigert et al., 2018: CARE fluorescence microscopy restoration, Nature Methods
- Krull et al., 2019: Noise2Void, CVPR
- Nehme et al., 2018: Deep-STORM, Optica
- Ouyang et al., 2018: ANNA-PALM, Nature Biotechnology
- Christiansen et al., 2018: In-silico labeling, Cell
- Punjani et al., 2017: cryoSPARC, Nature Methods
- Bepler et al., 2019: Topaz particle picking, Nature Methods
- Wagner et al., 2019: crYOLO particle picking, Communications Biology
- Zhong et al., 2021: CryoDRGN, Nature Methods
- Schwab et al., 2024: DynaMight, Nature Methods
- Powell et al., 2024: tomoDRGN, Nature Methods
- PathAI, 2026: Pathology AI company source
- Aiforia, 2026: Pathology image-analysis company source
- Paige, 2026: Pathology AI company source
- Owkin, 2026: Biomedical AI company source
- Tempus, 2026: Precision-medicine company source
Perturbation Prediction
- Dixit et al., 2016: Perturb-seq, Cell
- Adamson et al., 2016: CRISPRi single-cell screening, Cell
- Norman et al., 2019: Genetic interaction manifolds from rich single-cell phenotypes, Science
- Replogle et al., 2022: Genome-scale Perturb-seq, Cell
- Frangieh et al., 2021: Perturb-CITE-seq screens in patient models, Nature Genetics
- Roohani et al., 2024: GEARS, Nature Biotechnology
- Lotfollahi et al., 2019: scGen, Nature Methods
- Lotfollahi et al., 2023: CPA, Molecular Systems Biology
- Jiang et al., 2024: scPRAM, Bioinformatics
- Bunne et al., 2023: Neural optimal transport for single-cell perturbation responses, Nature Methods
- Bunne et al., 2024: AI virtual-cell roadmap, Cell
- Roohani et al., 2025: Virtual Cell Challenge commentary, Cell
- Arc Institute, 2025: Virtual Cell Challenge 2025 results
Systems Biology and Virtual Organisms
Thornburg et al., 2026: Four-dimensional mechanistic whole-cell model of JCVI-syn3A, Cell
Qian et al., 2026: Proposed virtual-yeast architecture and roadmap, Nature
Song et al., 2026: Perspective proposing AIDO as integrated multiscale foundation models, Nature Medicine
Badia-i-Mompel et al., 2023: Gene regulatory network inference in single-cell multi-omics, Nature Reviews Genetics
Karr et al., 2012: Whole-cell computational model of Mycoplasma genitalium, Cell
Bunne et al., 2024: AI virtual-cell roadmap, Cell
Roohani et al., 2025: Virtual Cell Challenge commentary, Cell
Sager et al., 2015: PBPK modeling and simulation systematic review, Drug Metabolism and Disposition
Li et al., 2025: PBPK modeling in FDA regulatory review, Pharmaceutics
Tegenge et al., 2025: PBPK modeling and simulation, US FDA CBER experience, The AAPS Journal
Pathmanathan et al., 2024: Credibility assessment of in silico clinical trials for medical devices, PLOS Computational Biology
Pammi et al., 2025: Digital twins, synthetic patient data, and in-silico trials in paediatric clinical trials, The Lancet Digital Health
Akbarialiabad et al., 2025: Enhancing randomized clinical trials with digital twins, npj Systems Biology and Applications
Genome and Variant Prediction
King et al., 2026: Generative design and experimental testing of complete ΦX174-like bacteriophage genomes, Science
Nguyen et al., 2024: Evo, Science
Brixi et al., 2026: Evo 2, Nature
Dalla-Torre et al., 2025: Nucleotide Transformer, Nature Methods
Merchant et al., 2026: Semantic design with Evo, Nature
Benegas et al., 2025: GPN-MSA, Nature Biotechnology
Fu et al., 2025: GET transcription foundation model, Nature
Fradkin et al., 2026: Orthrus RNA foundation model, Nature Methods
He et al., 2025: LucaOne, Nature Machine Intelligence
de Almeida et al., 2025: ChatNT, Nature Machine Intelligence
Avsec et al., 2021: Enformer, Nature Methods
Avsec et al., 2026: AlphaGenome, Nature
Zhou and Troyanskaya, 2015: DeepSEA, Nature Methods
Cheng et al., 2023: AlphaMissense, Science
Frazer et al., 2021: EVE, Nature
Meier et al., 2021, preprint: ESM-1v, arXiv
Sundaram et al., 2018: PrimateAI, Nature Genetics
Jaganathan et al., 2019: SpliceAI, Cell
Ioannidis et al., 2016: REVEL, American Journal of Human Genetics
Kircher et al., 2014: CADD, Nature Genetics
Zhan et al., 2025: Disease-specific variant language model, Nature Machine Intelligence
Richards et al., 2015: ACMG/AMP guidelines, Genetics in Medicine
Organismal and Environmental Biology
- Wang et al., 2025: Brain foundation model, Nature
- Horvath, 2013: DNA methylation age across tissues and cell types, Genome Biology
- Bell et al., 2019: DNA methylation aging clocks review and evaluation, Genome Biology
- Mendoza-Revilla et al., 2024: AgroNT plant genomic foundation model, Communications Biology
- Yu et al., 2024: PlantRNA-FM, Nature Machine Intelligence
- Hickey et al., 2019: Breeding crops to feed 10 billion, Nature Biotechnology
- Rafiq et al., 2025: AI in ecology, Nature Ecology & Evolution
- Pollock et al., 2025: AI and biodiversity knowledge shortfalls, Nature Reviews Biodiversity
Small Molecule and Drug Discovery
- Olivecrona et al., 2017: REINVENT, Journal of Cheminformatics
- Blaschke et al., 2020: REINVENT 2.0, JCIM
- Loeffler et al., 2024: REINVENT 4, Journal of Cheminformatics
- Jin et al., 2018, preprint: JT-VAE, arXiv
- Nigam et al., 2022: JANUS, Digital Discovery
- Irwin et al., 2022: Chemformer, Machine Learning: Science and Technology
- Ding et al., 2026: NaFM natural-product foundation model, Nature Machine Intelligence
- Ahmad et al., 2022, preprint: ChemBERTa-2, arXiv
- Corso et al., 2023, preprint: DiffDock, arXiv / ICLR
- Stärk et al., 2022, preprint: EquiBind, arXiv / ICML
- Peng et al., 2022, preprint: Pocket2Mol, arXiv / ICML
- Buttenschoen et al., 2024: PoseBusters, Chemical Science
- Trott and Olson, 2010: Vina, Journal of Computational Chemistry
- Koes et al., 2013: Smina, JCIM
- Yang et al., 2019: Chemprop, JCIM
- Swanson et al., 2024: ADMET-AI, Bioinformatics
- Fu et al., 2024: ADMETlab 3.0, Nucleic Acids Research
- Wu et al., 2018: MoleculeNet, Chemical Science
- Huang et al., 2022: Therapeutics Data Commons, Nature Chemical Biology
- Ren et al., 2025: ISM001-055 (rentosertib), Nature Biotechnology
- Xu et al., 2025: Rentosertib randomized Phase 2a trial, Nature Medicine
Chemical Biology, Diagnostics, and Cell Therapy
- Edwards et al., 2025: Target 2035 and chemical probes, Nature Reviews Chemistry
- FDA, 2026: Cellular and gene therapy products
- FDA, 2026: About biomarkers and qualification
- FDA, 2026: Companion diagnostics
Drug Repurposing and Combination Therapy
- Subramanian et al., 2017: Connectivity Map L1000, Cell
- Preuer et al., 2018: DeepSynergy, Bioinformatics
- Ianevski et al., 2020: SynergyFinder 2.0, Nucleic Acids Research
- Pushpakom et al., 2019: Drug repurposing review, Nature Reviews Drug Discovery
- Gordon et al., 2020: SARS-CoV-2 protein interaction map, Nature
- WHO Solidarity Trial Consortium, 2021: Repurposed antiviral drugs for COVID-19, NEJM
- RECOVERY Collaborative Group, 2021: Dexamethasone in hospitalized COVID-19 patients, NEJM
- Recursion, 2026: AI drug-discovery pipeline source
- Insilico Medicine, 2026: AI drug-discovery company source
Autonomous Laboratories and Agentic Science
- Canty and Abolhasani, 2026: Current review of self-driving laboratory scalability, generalizability, and provenance, Nature Reviews Chemistry
- Panapitiya et al., 2026: AutoLabs agent-to-liquid-handler protocol generation, Scientific Reports
- Li et al., 2026: Biomedical agent-team architecture and deployment perspective, Nature Biotechnology
- Gottweis et al., 2026: Co-Scientist multi-agent biomedical research system, Nature
- Ghareeb et al., 2026: Robin lab-in-the-loop scientific discovery system, Nature
- Boiko et al., 2023: Coscientist, Nature
- Swanson et al., 2025: Virtual Lab, Nature
- Szymanski et al., 2023: A-Lab, Nature (with subsequent novelty critique)
- Leeman et al., 2024: A-Lab novelty critique, PRX Energy
- MacLeod et al., 2020: Ada, Science Advances
- Roch et al., 2020: ChemOS, PLOS ONE
- Burger et al., 2020: Mobile robotic chemist, Nature
- King et al., 2009: Adam robot scientist, Science
- M. Bran et al., 2024: ChemCrow, Nature Machine Intelligence
- Alber et al., 2026: CellVoyager, Nature Methods
- Wierenga et al., 2023: PyLabRobot hardware-agnostic liquid handling, Device
- Baig et al., 2020: SBOL version 3, Journal of Integrative Bioinformatics
Biomanufacturing
- Helleckes et al., 2023: Machine learning in bioprocess development, Trends in Biotechnology
- Mondal et al., 2023: ML-based bioprocess optimisation, monitoring, and control review, Bioresource Technology
- Yang et al., 2023: Big data and ML-driven bioprocessing, Bioresource Technology
- Chen et al., 2020: Digital twins in pharmaceutical and biopharmaceutical manufacturing, Processes
- FDA, 2004: PAT framework guidance
- Ginkgo Bioworks, 2026: Biofoundry and autonomous-lab company source
- Cytiva, 2026: Bioprocess equipment and life-sciences infrastructure source
- Sartorius, 2026: Bioprocess and laboratory infrastructure source
- Resilience, 2026: Biomanufacturing CDMO source
Evaluation and Reproducibility
- Walsh et al., 2021: DOME framework, Nature Methods
- Farrell et al., 2026: Open and sustainable AI recommendations for the life sciences, Nature Methods
- Kryshtafovych et al., 2024: CASP15 assessment
- Zhang et al., 2026, preprint: Novice uplift on bounded digital biology tasks, arXiv
- Vieira et al., 2026, preprint: Protein-language-model mutational-effect evaluation, bioRxiv
Data Infrastructure and Public Resources
- wwPDB, 2026: Worldwide Protein Data Bank
- RCSB PDB, 2026: RCSB Protein Data Bank
- AlphaFold Protein Structure Database, 2026: EMBL-EBI AlphaFold DB
- ESM Metagenomic Atlas, 2026: Meta FAIR ESM Atlas
- CAMEO, 2026: CAMEO continuous evaluation
- Zdrazil et al., 2024: ChEMBL update
- Kim et al., 2023: PubChem update
- Ochoa et al., 2021: Open Targets Platform
- CZ CELLxGENE team, 2023, preprint: CZ CELLxGENE Discover, bioRxiv
- Berman et al., 2000: Protein Data Bank, Nucleic Acids Research
- Wilkinson et al., 2016: FAIR guiding principles, Scientific Data
- Gebru et al., 2021: Datasheets for datasets, Communications of the ACM
- Regev et al., 2017: Human Cell Atlas, eLife
Biomedical Knowledge Graphs and Literature AI
- Lee et al., 2020: BioBERT, Bioinformatics
- Gu et al., 2022: PubMedBERT, ACM Transactions on Computing for Healthcare
- Beltagy et al., 2019: SciBERT, EMNLP-IJCNLP
- Kilicoglu et al., 2012: SemMedDB, Bioinformatics
- Himmelstein et al., 2017: Hetionet, eLife
- Chandak et al., 2023: PrimeKG, Scientific Data
- Huang et al., 2024: TxGNN, Nature Medicine
- Ochoa et al., 2021: Open Targets Platform, Nucleic Acids Research
- Lobentanzer et al., 2023: BioCypher biomedical knowledge representation, Nature Biotechnology
- Taylor et al., 2022, preprint: Galactica, arXiv
- Edwards, Ars Technica, November 2022: Galactica demo withdrawal coverage
- OpenEvidence, 2026: Biomedical evidence retrieval platform
- Causaly, 2026: Life sciences literature and knowledge platform
- Iris.ai, 2026: Enterprise research knowledge platform
- Elicit, 2026: Literature-search and research-assistant platform
Programs and Regulatory References
- NIH Common Fund Bridge2AI, 2026: NIH Bridge2AI program
- Bridge2AI Consortium, 2026: Bridge2AI consortium site
- ARPA-H IGoR, 2026: ARPA-H IGoR program
- HHS press release, May 2026: ARPA-H IGoR launch
- NCI CRDC, 2026: NCI Cancer Research Data Commons
- CZ CELLxGENE Discover, 2026: Chan Zuckerberg Initiative
- CZI Virtual Cells Platform, 2026: CZI Virtual Cells Platform
- FDA, 2026: FDA AI/ML in drug development
- FDA, 2023: Assessing the credibility of computational modeling and simulation in medical device submissions (final guidance, builds on ASME V&V 40)
- FDA, 2025: Considerations for the use of AI to support regulatory decision-making for drug and biological products (draft guidance)
- FDA, Model-Informed Drug Development Paired Meeting Program: MIDD Paired Meeting Program
- FDA, M15 General Principles for Model-Informed Drug Development: ICH M15 guideline, finalized 2026
- EMA, 2024: EMA reflection paper on AI in medicines
- White House, 2026: USG Policy for Stopping High-Risk Life Sciences Research
- NIH, 2026: NIH notice describing the July 2026 policy, continuing pauses, and implementation timetable
- Warraich et al., 2025: FDA perspective on AI regulation, JAMA
- Zhang et al., 2025: AI in drug development, Nature Medicine
- Bloomfield et al., 2024: AI and biosecurity governance, Science
- Baker and Church, 2024: Protein design meets biosecurity, Science
- Wang et al., 2025: Built-in safeguards for generative AI biology tools, Nature Biotechnology
Target Identification and Human Genetics
- Buniello et al., 2025: Open Targets Platform update, Nucleic Acids Research
- Ochoa et al., 2021: Open Targets Platform, Nucleic Acids Research
- Nelson et al., 2015: Human genetic evidence and approved drug indications, Nature Genetics
- Finan et al., 2017: Druggable genome and target validation, Science Translational Medicine
- Tsherniak et al., 2017: Cancer Dependency Map, Cell
- Trajanoska et al., 2023: Human genetics from target discovery to clinical development, Nature
- Minikel et al., 2024: Genetic evidence and clinical success, Nature
- Razuvayevskaya et al., 2024: Genetic factors associated with clinical trial stoppage, Nature Genetics
Real-World Evidence and Biomarker AI
- Frieden, 2017: Evidence beyond randomized controlled trials, NEJM
- FDA, 2026: FDA real-world evidence program
- FDA, 2018: Framework for FDA’s Real-World Evidence Program
- FDA, 2026: Qualifying a biomarker through the Biomarker Qualification Program
- FDA, 2026: About biomarkers and qualification
- FDA, 2026: Companion diagnostics
- Sherman et al., 2016: Real-world evidence definition and uses, NEJM
- Corrigan-Curay et al., 2018: Real-world data and evidence for safety and effectiveness, JAMA
- Hernán and Robins, 2016: Emulating a target trial when randomization is unavailable, American Journal of Epidemiology
- Hernán et al., 2022: Target trial emulation, JAMA
- Hubbard et al., 2024: Target trial emulation potential and pitfalls, NEJM
- Burcu et al., 2020: External control-arm considerations for medicines, Pharmacoepidemiology and Drug Safety
- Zang et al., 2023: High-throughput target trial emulation for Alzheimer drug repurposing, Nature Communications
- Tempus AI, 2024: Tempus AI S-1 filing in SEC EDGAR
- Tempus, 2026: Clinical and molecular data platform source
- Flatiron Health, 2026: Oncology real-world data platform source
- Komodo Health, 2026: Real-world data analytics platform source
- Datavant, 2026: Datavant/Aetion data collaboration source
RNA, mRNA, and Vaccine Design
- Karikó et al., 2005: Modified nucleosides and innate immune recognition, Immunity
- Pardi et al., 2018: mRNA vaccine platform review, Nature Reviews Drug Discovery
- Sample et al., 2019: Human 5 prime UTR design and variant effects, Nature Biotechnology
- Leppek et al., 2022: mRNA structure, stability, and translation optimization, Nature Communications
- Reynisson et al., 2020: NetMHCpan and NetMHCIIpan antigen-presentation prediction, Nucleic Acids Research
- Zhang et al., 2023: Optimized mRNA design and immunogenicity, Nature
- Hie et al., 2021: Viral evolution and escape language models, Science
Frontier Lab Publications and Partnerships
- Isomorphic Labs, January 2024: Eli Lilly and Novartis collaborations
- Recursion mission, 2026: Recursion Pharmaceuticals mission
- Insilico Medicine, November 2024: ISM001-055 Phase IIa update
- OpenAI, June 2024: Color Health cancer screening copilot
- OpenAI, April 2024: Moderna ChatGPT Enterprise deployment
- OpenAI, September 2024: o1 model GPQA Diamond performance
- Anthropic, October 2025: Claude for Life Sciences launch
- Anthropic, June 2026: Claude Science beta launch
- Anthropic, August 2026: Claude de novo protein binders, company report
Citation Verification
The handbook uses connected-record checks for author, venue, year, and DOI, plus primary-source checks for organizational, program, company, and regulatory claims. A resolving DOI or URL does not by itself prove that the source supports the sentence; claim-to-source alignment remains part of editorial review. The public citation method is documented in How to Cite This Handbook.
Known caveats from verification:
- Evo 2 first author is Brixi, not Nguyen (Nguyen led Evo 1); the peer-reviewed paper is Nature 2026, not the earlier bioRxiv preprint
- Chai-1 uses the corporate author “Chai Discovery”
- Varadi AFDB 2024 NAR title says “in 2024” but Crossref publication year is 2023
- AlphaProteo had no peer-reviewed publication in the evidence review completed August 12, 2026; the cited record is an arXiv preprint
- Claude binder campaign had no peer-reviewed publication in the evidence review completed August 20, 2026; the cited record is an Anthropic company report. Anthropic stated that the binders are not drugs
- Insilico ISM001-055 Phase 2a now has peer-reviewed randomized Nature Medicine evidence; no approval-level or pivotal-trial evidence is implied
For citation format conventions used throughout the handbook, see How to Cite.