flowchart LR
A["Part I:<br/>Foundations"] --> B["Part II:<br/>Molecular<br/>Discovery"]
B --> C["Part III:<br/>Cells, Tissues<br/>and Systems"]
C --> D["Part IV:<br/>Organisms and<br/>Environment"]
D --> E["Part V:<br/>Therapeutic<br/>Translation"]
E --> F["Part VI:<br/>Research,<br/>Practice and<br/>Governance"]
style A fill:#ffffff,stroke:#15803d,stroke-width:2px,color:#334155
style B fill:#ffffff,stroke:#15803d,stroke-width:2px,color:#334155
style C fill:#ffffff,stroke:#15803d,stroke-width:2px,color:#334155
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style E fill:#ffffff,stroke:#15803d,stroke-width:2px,color:#334155
style F fill:#ffffff,stroke:#15803d,stroke-width:2px,color:#334155
click A "/foundations/ai-for-life-sciences.html"
click B "/molecular/protein-structure.html"
click C "/cells/single-cell-models.html"
click D "/organisms/neuroscience-ai.html"
click E "/therapeutics/targets.html"
click F "/automation/self-driving-labs.html"
The Life Sciences AI Handbook
Steering Frontier Models in Biology
The Life Sciences AI Handbook is a free guide to AI for biology, drug discovery, cells, systems biology, autonomous labs, and validation.
Welcome to The Life Sciences AI Handbook
Foundation models now predict protein structures before they are solved, represent cells in latent space, and generate molecules faster than chemistry can synthesize them. The model is no longer the bottleneck. Functional biology is.
The hard part is translation: separating what a model suggests from what biology has shown, and deciding what still has to be tested.
Each chapter answers two kinds of questions. First: what the field is trying to solve, the core idea, where the field stands, what is known and still open, and why it matters. Then: what is demonstrated, what remains theoretical, what is beyond current capability, what would make the area more promising, and what researchers, biotech teams, funders, and program leaders should do next.
This handbook is continuously updated as new papers, benchmarks, and model releases change what the evidence supports.
Important Disclaimers
This handbook is for research and educational purposes only. It does not constitute clinical advice, regulatory guidance, or a recommendation to use any specific tool or platform. Model outputs discussed here are hypotheses that require experimental validation; they are not endpoints.
Validation and safety remain context-specific. Researchers and program leaders should design appropriate validation experiments, assess reproducibility and safety, meet applicable biosafety and biosecurity requirements, and follow publication and data-sharing norms in their field.
Information may become outdated given the pace of model releases, benchmark updates, and laboratory automation tooling. Verify model versions, training data scope, and benchmark results against primary sources before relying on them.
For dual-use considerations, see Information Hazards in Capability Research and the companion Biosecurity Handbook.
Start Here
Start with the evidence framework, then read the best-established molecular example. This path shows how the handbook evaluates capability, evidence, and translation.
- Executive Summary: Evidence framework and core conclusions across the handbook
- AI for the Life Sciences: Scope, audience, and the chapter reference framework
- Protein Structure Prediction: The best-established capability, and what it does and does not solve
Then continue to Evaluation Principles before adopting any model in a research program.
For role-specific reading paths, see How to Read This Handbook.
Book Structure
- Part I: Foundations (6 chapters): Scope, history, biological data infrastructure, knowledge graphs and literature AI, foundation models for biology, evaluation principles
- Part II: Molecular Discovery and Design (5 chapters): Protein structure, protein design, antibodies, nucleic-acid and genome models, variant effects
- Part III: Cells, Tissues, and Systems Biology (8 chapters): Single-cell foundation models, spatial omics, image phenotyping, histopathology, microscopy and cryo-EM, perturbation and virtual cells, microbiome and multi-omics, systems biology and multiscale modeling
- Part IV: Organismal and Environmental Biology (5 chapters): Neuroscience and brain foundation models, aging and longevity biology, plant and crop AI, environmental and ecological AI, virtual organisms
- Part V: Therapeutic Discovery and Translation (10 chapters): Target identification, small molecules and ADMET, chemical biology, drug repurposing and combinations, mRNA and vaccines, cell and gene therapy, diagnostics and biomarkers, clinical-trial AI, real-world evidence, translational failure modes
- Part VI: Research Systems, Practice, and Governance (11 chapters): Self-driving labs, robotic and cloud labs, synthetic-biology design tools, biomanufacturing, agentic science workflows, toolkit, benchmarks, reproducibility and open science, information hazards, workforce and compute readiness, emerging frontiers
Companion Handbooks
The Physician AI Handbook
Clinical AI across every ACGME-recognized medical specialty: FDA-cleared diagnostic tools, clinical decision support, AI-assisted documentation, LLMs in clinical practice, medical liability, privacy and HIPAA, workflow integration, and evaluation frameworks. Peer-reviewed evidence from JAMA, NEJM, Lancet, and specialty journals. For physicians, health system leaders, and anyone building or deploying clinical AI.
The Public Health AI Handbook
AI applications across population health: disease surveillance, epidemic forecasting, genomic pathogen analysis, outbreak detection, health department implementation, deployment failures, AI-assisted coding for epidemiological analysis, behavioral interventions, and health misinformation. For epidemiologists, public health practitioners, and health department leaders.
The Biosecurity Handbook
Where AI capability meets biological risk: laboratory biosafety, the Biological Weapons Convention, dual-use research oversight, DNA synthesis screening, AI-enabled pathogen design risks, LLM information hazards, red-teaming, autonomous lab agents, and governance frameworks for AI-bio convergence. For biosecurity professionals, AI safety researchers, policymakers, and laboratory personnel.
License & Citation
This work is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
You are free to: Share, copy, redistribute, adapt, remix, and build upon this material for any purpose, including commercially, with attribution.
Full license details | CC BY 4.0 Legal Code
How to Cite
Tegomoh, B. (2026). The Life Sciences AI Handbook: Steering Frontier Models in Biology. DOI: pending. URL: lifesciencesaihandbook.com
Full citation formats | Follow updates on Twitter/X
Using this handbook in research or teaching? See the How to Cite page for AMA, APA, Vancouver, and BibTeX formats, and the Model and Dataset Index for canonical references to best-established systems.