AI is compressing the time and cost of bringing new therapies to patients by learning the “rules” of biology and chemistry, generating targets and molecules, and optimizing preclinical to clinical decisions—while new regulatory guidance clarifies how AI evidence can support approvals in 2025. Biological foundation models, generative chemistry/protein design, and closed‑loop robotics are moving from pilot to platform across pharma and biotech.
What’s new in 2025
- Biological foundation models
- Pretrained, multitask models on genomes, proteomes, structures, and phenotypes are emerging to generalize across tasks like target discovery, variant effect prediction, and drug response, extending the AlphaFold moment to broader biology.
- Generative design at scale
- Deep generative models for small molecules and proteins propose candidates optimized for potency, ADMET, and developability, increasingly coupled to automated synthesis and testing to close the design–make–test–learn loop.
- Regulatory clarity
- The U.S. FDA released draft guidance describing a seven‑step, risk‑based credibility framework for AI models used to support regulatory decision‑making in drug development, covering nonclinical, clinical, and manufacturing uses.
Where AI impacts the pipeline
- Target and biomarker discovery
- Multi‑omics models find causal genes and pathways and identify predictive biomarkers for patient stratification, improving probability of technical and regulatory success from the outset.
- Generative chemistry and property prediction
- Models propose novel scaffolds and predict toxicity, PK/PD, solubility, and selectivity before synthesis, shrinking the search space and avoiding dead ends earlier.
- Protein and antibody design
- ML‑assisted design accelerates binders, enzymes, and antibodies with multi‑property optimization for stability, immunogenicity, and viscosity, leveraging structure prediction and diffusion‑based backbone generators.
- Preclinical translation
- In vitro/in vivo model selection and dose scheduling benefit from ML meta‑analysis of past study outcomes, improving replication and reducing animal use.
- Clinical trial design and ops
- AI supports protocol simulation, site/participant selection, and adaptive enrollment; patient matching and digital endpoints increase power and reduce screen failures and timelines.
Closing the loop: automation and high‑throughput science
- Lab robotics + AI
- Autonomous platforms synthesize and test AI‑designed compounds, feeding results back to models for rapid iteration and better sample efficiency in under‑explored spaces.
- Federated and privacy‑preserving analysis
- Secure analysis across siloed genomic/clinical datasets enables broader learning while respecting privacy and jurisdictional rules, expanding evidence without centralizing raw data.
Evidence and regulation
- FDA’s risk‑based credibility framework
- Sponsors should define question and context of use, assess model risk, plan and execute credibility studies, and document adequacy for the stated role—a path to accept AI‑generated evidence in filings.
- Scope of guidance
- The draft guidance focuses on AI used to produce evidence for decisions on safety, efficacy, or quality; discovery‑only tools remain outside scope unless their outputs support regulatory claims.
Measured impact and realistic expectations
- Early clinical signals
- Reports cite faster Phase I entries and improved early‑phase success rates for AI‑designed or AI‑prioritized assets, alongside shorter preclinical cycles and cost reductions from better triage.
- Caveats
- Many claims come from vendor case studies; rigorous, peer‑reviewed, prospective validations remain critical to separate durable advances from hype.
Architecture: retrieve → reason → simulate → apply → observe
- Retrieve (ground)
- Aggregate multi‑omics, structures, chem corpora, assay data, and clinical outcomes; manage data rights and privacy; create lineage for all artifacts.
- Reason (model)
- Use foundation models for target hypotheses; generative models to propose molecules/proteins; ADMET/developability predictors to filter; plan experiment batches.
- Simulate (de‑risk)
- Run in silico docking/MD surrogates, virtual trial arms, and protocol simulations; define acceptance criteria tied to translational risks.
- Apply (experiment)
- Execute automated synthesis/assays; iteratively update models with active learning; prepare regulatory‑grade documentation for models used in evidence.
- Observe (verify)
- Monitor hit/lead conversion, assay reproducibility, false‑positive/negative rates, and clinical screen failures; recalibrate models and update governance.
High‑impact use cases
- Next‑gen target discovery
- Foundation models over multi‑omics uncover non‑obvious targets and patient subtypes, guiding precision medicine programs and companion diagnostics roadmaps.
- Antibody/binder engineering
- Diffusion and language‑model approaches generate binders with improved stability and affinity, validated across assays for developability.
- De novo small‑molecule design
- Generative models propose novel chemotypes with predicted ADMET that pass early triage at higher rates than enumeration, accelerating lead optimization.
- Clinical operations optimization
- Patient‑to‑site matching and adaptive enrollment reduce timelines and costs, improving diversity and retention in trials.
Governance, safety, and ethics
- Model risk management
- Treat AI as high‑impact models: versioning, data lineage, bias testing (population, chemistry space), and fail‑safes; align to FDA’s credibility steps for any model used in regulatory evidence.
- Reproducibility and transparency
- Publish model cards, benchmarks, and negative results where possible; consortia are forming to share developability datasets and improve generalization.
- Equity and access
- Ensure diverse datasets and inclusive eligibility criteria to avoid leaving under‑represented populations behind in target discovery and trials.
90‑day roadmap for a biotech/pharma team
- Weeks 1–2: Scope and data
- Pick one program stage (e.g., hit finding in an oncology target); inventory data and define success metrics (hit rate, ADMET pass rate, cycle time); map regulatory touchpoints.
- Weeks 3–6: Prototype
- Fine‑tune or adapt a foundation model for target signals; stand up a generative + screening pipeline; pre‑register evaluation with prospective holdouts.
- Weeks 7–12: Close the loop
- Run an active‑learning batch with robotic assays; document model credibility if results will inform filings; compare against legacy baselines; plan scale‑up.
Common pitfalls—and fixes
- Overfitting to narrow assays
- Fix: diversify tasks and chemotypes; use prospective, blinded validation; penalize confounders in training.
- “Black‑box to the FDA”
- Fix: adopt the seven‑step credibility framework early; provide interpretable summaries, uncertainty estimates, and audit trails; limit AI scope to well‑defined COUs.
- Data leakage and lineage gaps
- Fix: strict temporal splits, de‑duplication of near‑identical molecules, and end‑to‑end tracking of datasets and feature provenance.
Bottom line
AI is becoming core infrastructure for medical research and drug discovery—learning biology’s patterns, generating viable candidates, and streamlining trials—while regulators now outline how AI‑produced evidence can back decisions; the leaders pair foundation and generative models with automated labs and rigorous, transparent validation to turn speed into safe, effective medicines.
Related
How soon will foundation models like AlphaFold transform target identification
Which AI methods best speed up antibody or binder discovery
Why is experimental validation still a bottleneck despite model advances
How might AI plus robotics create a closed-loop drug discovery lab
How will FDA AI guidance change how I design models for regulatory use