The Life Sciences AI Handbook
AI for Biomedical Discovery, Biotechnology, and Translational Research
The Life Sciences AI Handbook
AI for Biomedical Discovery, Biotechnology, and Translational Research
A professional handbook for researchers, biotechnology teams, computational biologists, physician-scientists, and students evaluating AI systems across molecules, cells, experiments, and therapeutic development.
Core Reading Path
Life sciences AI is best read as an experimental discipline. Models that predict structures, design proteins, rank targets, generate molecules, or forecast perturbations need evidence tied to assays and decisions.
- Demonstrated: protein structure prediction, selected protein design tasks, molecular property benchmarks, single-cell representation learning, perturbation prediction in bounded settings, and closed-loop experiments.
- Theoretical: integrated models that connect molecules, cells, tissues, and experiments across laboratories.
- Beyond current capabilities: fully autonomous biological discovery without experimental validation or human governance.
Handbook Map
- Part I: Foundations covers scope, data infrastructure, foundation models, and evaluation.
- Part II: Molecular AI covers protein structure, protein design, antibodies, genome models, and variant effects.
- Part III: Therapeutics AI covers target identification, small molecules, RNA and vaccines, trials, and translational failure modes.
- Part IV: Cellular and Systems Biology covers single-cell models, spatial omics, image phenotyping, virtual cells, and multi-omics.
- Part V: Engineering and Automation covers self-driving labs, robotic labs, synthetic biology design tools, and agentic workflows.
- Part VI: Practice and Governance covers benchmarks, reproducibility, information hazards, workforce, and institutional readiness.
Explore the Handbook Series
The Physician AI Handbook
Clinical AI across medical specialties, implementation, safety, workflow, liability, and physician-facing evaluation.
The Public Health AI Handbook
AI for surveillance, forecasting, public health operations, population health analytics, and deployment in health agencies.
The Biosecurity Handbook
Biological risk, dual-use research oversight, biosafety, AI-bio convergence, governance, and risk evaluation.
The Life Sciences AI Handbook
AI for molecular design, cellular systems, biomedical discovery, biotechnology, automation, and translational research.
License and Citation
This work is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
Suggested citation: Tegomoh, B. (2026). The Life Sciences AI Handbook: AI for Biomedical Discovery, Biotechnology, and Translational Research. DOI pending. URL: https://lifesciencesaihandbook.com