▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒Executive summary

Pharma R&D AI is now an experiment-capacity allocation decision: the model worth funding is the one that proves it changes which targets, molecules, assays, biomarkers or IND/CTA-supporting analyses consume scarce validation slots, not the one that produces the most plausible predictions. Structure prediction and molecular foundation model papers show expanded modelling capability ³⁴, while synthesis, wet-lab assays, omics runs, translational review and regulatory credibility remain the constrained resources. The strategic question is therefore not which model can predict more; it is which model deserves permission to spend scarce experimental capacity.

A strong retrospective benchmark can still worsen portfolio allocation when it sends better-looking but weakly testable compounds, targets or biomarkers into the same assay bottleneck ¹⁵.

The private-eval denominator is experiment yield: hit quality per assay panel, failed synthesis avoided, cycle time to kill or redesign, biomarker reproducibility, translational decision quality and credibility evidence for any submission-supporting analysis. Public lab-execution evidence is directional here, not a published ROI benchmark ¹⁰.

The winning vendor or internal platform may still have frontier models, proprietary data and large compute ⁶⁷. The paid proof is narrower: each model-generated target, binder, property or biomarker must show the experiment it displaced, the result it returned and the next programme decision it improved.

Make private evals replay recent programme choices and then run prospectively: baseline decision, model recommendation, experiment chosen or displaced, assay or omics result, scientist correction and next-cycle update.

Do not pay for prediction volume, leaderboard rank or platform breadth unless it converts into higher-quality experimental queues, faster kill calls or stronger translational and regulatory evidence.

The buying decision

Question	Answer	Action
What is the object of approval?	An experiment-capacity allocation right for named target, molecule, assay, biomarker or IND/CTA-supporting analysis slots.	Require each model to state which experiment it consumes or displaces and what kill, redesign or advance decision it informs.
Which model goes first?	The model aimed at a costly design-make-test-analyse bottleneck where archived decisions, assay outcomes and scientist corrections already exist.	Run replay tests, then a prospective batch, before letting the model reprioritize lead optimization, target validation or translational biomarker work.
What remains blocked?	Predicted structures, molecules or biomarkers without physical-validity screening ¹⁵, omics provenance ⁸⁹, reviewer correction and validation evidence ¹¹¹.	Keep them out of the prioritized wet-lab queue and out of submission-supporting analyses until the experiment-yield record is complete.

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Public evidence is now structured enough to define private approval tests. Prediction abundance now meets scarce validation capacity across chemistry, biology, translational research and regulatory evidence. Model capability is no longer the only bottleneck. Structure-prediction and molecular foundation model evidence widens the set of plausible targets, structures and molecules, but synthesis, assays, omics validation, translational interpretation and regulatory credibility decide whether that extra optionality improves a programme ³⁴.

Contract for an experiment-yield scorecard by target, molecule, biomarker or analysis: baseline choice, AI recommendation, experiment chosen or displaced, wet-lab or omics result, next-cycle learning, cycle time and kill, redesign or advance outcome.

For model-led R&D workflows, the approval record has to connect source data, accepted output, reviewer action, retained audit and lifecycle evidence before prediction volume or staffing claims matter.

Triggers that change approval timing

Trigger	Why it matters	Decision use
FDA AI drug-development guidance	Regulatory decision-support AI has a named credibility route ¹.	Classify which model outputs may touch submission-supporting evidence.
ICH M15 model-informed evidence	ICH M15 supplies MAP/MAR and model-risk discipline for model-informed drug development; verify exact publication, adoption and implementation metadata before relying on date-specific timing ¹¹.	Ask whether the vendor can produce submission-grade model evidence, not only discovery benchmarks.
Regulated lab data integrity	Part 11 and Annex 11 controls decide whether AI-touched lab and omics records can support regulated work ¹²¹³.	Put audit trail, access control, backup, supplier oversight and change-control clauses into the validation plan.
Nonclinical data integrity	GLP data-integrity rules add retention and traceability pressure for nonclinical evidence ¹⁴.	Keep traceability, retention and reviewer evidence separate from model-performance proof.
Foundation-model progress	Structure and molecular models can change experiment choice ³⁴.	Retest on archived programmes and current assays.
Industrial compute and data platforms	Public leaders show compute and platform investment ⁶⁷, while lab-loop evidence still needs task-level proof ¹⁰.	Treat data rights, compute, assay capacity and decision logs as diligence requirements, not public-source proof.

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Workflow approval changes by authority, source boundary and proof burden. Fund pharma R&D AI models only when they earn the right to allocate scarce experimental capacity. The decision map should start with a scientific decision record from source data to model output, experiment, reviewer decision and retained evidence: what source record is read, what output is drafted, who approves it, where it is stored and what event forces reapproval.

A system can pass a low-risk support test and still fail production work: source evidence, accepted output, integration state, exception handling, reviewer burden and downstream rework have to hold together.

The useful vendor question is whether the system can make the named task inspectable, reversible and measurable inside the operating record.

Workflow selection

Workflow	Accepted work	Approval gate	Approval rule
Target and biology prioritization	Ranked target hypothesis with source evidence, uncertainty and validation experiment.	Data lineage, disease context, orthogonal evidence and scientist review.	Proceed when the model changes experiment selection, not slide quality.
Structure and molecule design	Candidate structure, binding hypothesis or molecule design sent to assay.	Model limitations and physical-validity screen ¹⁵.	Measure experimental hit quality and cycle time.
Translational and omics route	Biomarker, cohort, mechanism or stratification hypothesis with reproducible run evidence.	Run ID, sample provenance and workflow version ⁸⁹. Part 11 or Annex 11 control and reviewer sign-off ¹²¹³.	Use versioned omics workflows before model-derived biomarkers enter decisions.
Regulatory decision support	AI-derived analysis used to support safety, effectiveness or quality evidence.	Credibility assessment, model risk, Model Analysis Plan or Report, data provenance, model validation and lifecycle control ¹¹¹.	Treat as a regulated evidence route, not research productivity tooling.

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Terms used in this report

Term	Plain meaning
Route	The approved path from work to accepted output: source set, data boundary, model or vendor channel, tools, reviewer, evidence record and reapproval trigger.
LLM	Large language model: a model trained to read and generate text-like tokens.

Definitions are reader aids, not separate evidence claims.

AI models for pharma and biotech R&D

The colophon

What the locked report contains