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Start with the decision, not the model
The first document should be a one-page context-of-use statement. It should name the bottleneck, the stage of discovery, the intended output, the people who will use it, and the downstream decision that output is meant to change. Examples include ranking compounds for synthesis, selecting experiments for a target-validation campaign, predicting a property before an assay, or generating starting points for medicinal-chemistry review. These are different use cases with different evidence burdens.
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Specify the scientific setting
- Problem and modality: State whether the work concerns target identification, small molecules, biologics, formulation, safety prediction, or another task.
- Stage: Distinguish exploratory research, hit finding, lead optimization, candidate selection, preclinical planning, or evidence intended for a regulatory submission.
- Inputs and output: List the data types, required formats, turnaround time, uncertainty information, and whether the output is a ranked list, structure, prediction, recommendation, or report.
- User and decision: Identify the scientist or committee using the result and the action that follows if it is accepted, rejected, or uncertain.
- Consequence of error: Describe the cost of a false positive, false negative, biased ranking, or untraceable result.
Record what is explicitly out of scope. A system validated on one target class, assay type, or chemical space should not be assumed to generalize to another. The FDA and EMA’s current good-practice principles emphasize a clear context of use and a risk-based assessment rather than a single rating for every AI system.
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State what the company does without the partner: which experiments, review steps, cycle times, or decisions are the baseline. Then state what would change if the system worked as claimed. This prevents a partnership from being justified by an abstract promise such as “faster discovery” when the actual workflow has no capacity to use the output.
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Request evidence that can be independently interpreted
Ask for evidence at the level of the claim. A retrospective benchmark may support a limited prediction claim; it does not by itself establish that a program will produce a viable drug or improve patient outcomes. Require enough detail to reproduce the analysis or at least to understand where it can fail.
| Evidence area | Questions to ask | Why it matters |
|---|---|---|
| Data provenance | Where did training, tuning, and test data originate? What inclusion and exclusion rules, assay conditions, labeling process, and third-party restrictions apply? | Unclear provenance can hide bias, incompatible measurements, or a license that does not permit the proposed use. |
| Evaluation design | How were train, validation, and test sets separated? Were scaffold, temporal, target, site, or patient-level splits used where appropriate? How was leakage checked? | A random split can overstate performance when near-duplicates or future information cross the boundary. |
| Comparator and metrics | What baseline, published method, or internal workflow is the comparison? Which metrics, confidence intervals, calibration measures, and error strata are reported? | A metric is useful only if it answers the decision question and reflects the cost of different errors. |
| External or prospective validation | Has the method been tested on an independent dataset, a new site, a later time period, or prospective wet-lab experiments? | Prospective or external performance is more informative about transfer to the company’s setting than a result on familiar data. |
| Reproducibility | What model version, software dependencies, random seeds, hardware, prompts, and preprocessing steps produced the result? | Without versioning, a result cannot be audited or compared after an update. |
| Failure analysis | Which targets, chemotypes, assay conditions, or data-quality regimes produce poor results? How is uncertainty surfaced to users? | Known failure modes determine whether human review or a narrower deployment is needed. |
Request a demonstration using a held-out or newly collected set that reflects the proposed context. If the claim concerns molecule prioritization, ask how many candidates were synthesized, how selection was blinded, what the comparator selected, and how results were analyzed. If the partner cannot disclose sensitive details, seek a controlled review by an independent expert, a secure execution environment, or a contractual audit right rather than accepting an unqualified claim.
The available guidance does not establish one universal metric set or acceptance threshold for all AI drug-discovery systems. Set the metrics and thresholds before the pilot results are visible, and explain why each threshold changes a real decision.
Diligence data, intellectual property, and confidentiality
Model quality is inseparable from the rights and restrictions attached to data and outputs. Build an asset map before negotiating economics.
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Separate background and foreground assets
Define each party’s background intellectual property: existing software, model architectures, weights, databases, patents, know-how, compounds, assays, and documentation. Define foreground assets created during the project, including inventions, candidate compounds, structures, experimental results, trained or fine-tuned models, embeddings, and process improvements. Specify whether ownership is exclusive, joint, or licensed, and identify the field, territory, duration, and sublicensing rights.
Set data-use boundaries
- List every dataset the partner may receive and the permitted purpose for each one.
- State whether project data, validation data, prompts, outputs, or experimental results may be retained after the project.
- Prohibit model training or improvement with the company’s data unless that use is expressly authorized, technically segregated where required, and documented.
- Check third-party licenses, patient-consent limits, privacy obligations, export controls, and confidentiality markings.
- Define deletion or return procedures, backup handling, security controls, breach notification, access logs, and audit rights.
Protect downstream use
The contract should say who may use results in regulatory submissions, internal decisions, publications, patent filings, investor communications, and partnerships with other parties. Cover rights to make derivatives, continue development after termination, and use validation data to test a replacement system. WIPO’s cross-cutting guidance treats these issues—especially background versus foreground IP, validation data, publication, regulatory use, derivatives, and confidentiality—as core contracting questions rather than boilerplate.
Make regulatory relevance specific
Ask whether the system is an internal research aid or will generate information intended to support a regulatory decision about safety, effectiveness, or quality. The distinction changes documentation, validation, change control, and the evidence regulators may expect.
FDA status and context
The FDA’s January 2025 document describes a nonbinding draft guidance and is marked “Not for implementation.” Its scope is a risk-based credibility assessment for an AI model in a particular context of use. The agency describes it this way: "Specifically, this guidance provides a risk-based credibility assessment framework that may be used for establishing and evaluating the credibility of an AI model for a particular context of use (COU)." Do not present the draft as an approval, certification, or finding that every discovery tool is acceptable.
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FDA/EMA principles and lifecycle controls
Ten FDA/EMA good-practice principles published in January 2026 address human-centered design, risk, standards, context of use, multidisciplinary expertise, data governance, model development, performance assessment, lifecycle management, and essential information. Translate those themes into operational questions: who approves a model update, how performance drift is monitored, what records are retained, how users are trained, and how a failed or withdrawn model is handled. An EMA reflection paper adopted in September 2024 also supports thinking about AI across the medicinal-product lifecycle; its high-level value is the lifecycle perspective, not a universal approval pathway.
If a partner’s system may support a submission, involve regulatory, statistics, quality, clinical, and data-governance specialists early. Document the version used to generate each result, the data and preprocessing applied, human review, deviations, and the rationale for relying on the output.
Check operational fit and realistic value
A technically credible system can still fail in production because data arrive late, formats do not match, compute is unavailable, or scientists cannot interpret the output.
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Assess implementation requirements
- Interfaces to electronic laboratory notebooks, assay databases, compound-registration systems, identity and access management, and analytics environments.
- Compute, storage, software dependencies, model-serving architecture, and support coverage.
- Cybersecurity review, privacy controls, segregation of confidential programs, and incident response.
- Human oversight, training, explainability or uncertainty displays, and procedures for overriding a recommendation.
- Update cadence, monitoring, revalidation triggers, rollback capability, and ownership of ongoing validation.
- Responsibilities for wet-lab execution, sample logistics, data cleaning, and resolving discrepant results.
Use a transparent value model
Estimate value from a stated baseline and counterfactual, not from a vendor’s headline percentage. A simple internal model can be: expected value equals the change in successful decisions or avoided work multiplied by the value per change, minus implementation, data, validation, licensing, and support costs. Show the assumptions separately: number of programs, experiments per decision, laboratory capacity, probability that a prediction changes action, and the time window in which savings or capacity become available. Present ranges or scenarios when assumptions are uncertain. No universal return-on-investment hurdle is established for AI drug discovery, so the company must set a project-specific threshold.
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Design the pilot as a decision instrument
A pilot should answer a written question and end in a predefined go, revise, or no-go decision. A showcase that selects favorable examples is not a useful test.
- Write the decision question. Example: Can the system improve prospective prioritization of compounds for a named target and assay within the next campaign cycle, compared with the current workflow?
- Freeze the protocol. Specify datasets, access controls, preprocessing, model version, comparator, blinding, analysis plan, and who may change the protocol.
- Set success criteria in advance. Choose decision-relevant outcomes, such as prospective hit rate, enrichment at a fixed synthesis budget, calibration, reproducibility across sites, or reduction in review time. Define minimum thresholds and an uncertainty rule before results are seen.
- Allocate responsibilities. Name the parties responsible for data preparation, model execution, medicinal-chemistry review, wet-lab work, statistical analysis, security, and independent adjudication.
- Budget time and resources. Include sample preparation, scientist hours, compute, integration work, confirmatory experiments, and time for investigating failures.
- Test the intended operating mode. Decide whether the pilot tests scientific performance, workflow utility, integration, commercial viability, or a sequence of these. One pilot rarely answers all four.
- Analyze failures, not only averages. Report performance by target, chemical series, assay condition, data-quality level, and uncertainty band. Record cases where human experts rejected a seemingly strong recommendation.
- Apply the gate. If criteria are met, define the next scope and additional evidence required. If they are missed, specify whether to narrow the context, repair data, rerun with a revised protocol, or stop. Preserve a no-go path.
The public description of a current pharmaceutical partnering role at AbbVie similarly emphasizes decision-relevant pilots and benchmarks, explicit success criteria, data needs, assumptions, risks, implementation needs, and recommendations. That description is an example of process practice, not a regulation or a universal standard.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare partnership structures and risk allocation
Choose the structure after the use case and pilot are defined. The same company may need a bounded experiment for one question and a broader license for another.
| Structure | Best suited to | Key terms to compare | Typical unresolved questions |
|---|---|---|---|
| Bounded pilot | Testing a defined scientific or workflow hypothesis before a larger commitment. | Scope, duration, data access, protocol, success criteria, fees, confidentiality, and stop/go rights. | Who owns new inventions and validation data; what happens to data and models at exit. |
| Research collaboration | Joint work across targets or programs with shared scientific responsibilities. | Target and field scope, workplan, funding, milestones, governance, publication, IP, and development options. | Decision rights when results are mixed; rights to continue or expand. |
| Platform or technology license | Repeated internal use of a system or access to platform capabilities. | Users, territories, field, exclusivity, updates, service levels, audit, security, and sublicensing. | Whether improvements, fine-tuned models, and outputs are included; revalidation after updates. |
| Option structure | Preserving the right, but not the obligation, to expand after technical milestones. | Option period, exercise price or milestone, exclusivity during evaluation, data access, and transition support. | Whether the option covers compounds, targets, territories, or only a defined platform capability. |
Compare every proposal on scope and stage; cash funding and reimbursable costs; milestones and contingent payments; ownership or licenses for compounds, inventions, data, and platform outputs; exclusivity, field, target, and territory; access to technology and updates; publication and regulatory-use rights; governance; termination and transition; and expansion options. If a term is not established, mark it for negotiation rather than assuming market practice.
Use public deals as illustrations, not templates
A February 2026 SEC filing by Insilico Medicine describes a Takeda research collaboration and license using its platform to identify, generate, and optimize small-molecule candidates against three initial targets. The same filing separately describes a technology-enablement agreement for platform access. This shows that research work and platform access can be documented as separable arrangements; it does not establish appropriate economics, ownership, milestones, or exclusivity for another company.
Build cross-functional governance before signing
Assign a steering group with scientific, AI and technical, drug-development, legal, privacy, compliance, procurement, information-security, and business-development expertise. Give it a written decision calendar: approval of the context of use, acceptance of the pilot protocol, review of interim risks, final gate, and authorization for expansion. Keep a decision log linking each conclusion to the version of the model, dataset, analysis, and responsible reviewer.
Set escalation rules for data breaches, unexplained performance changes, model updates, conflicts over inventorship, missed milestones, and publication requests. Require notice before material model or data changes and reserve the right to pause use while revalidation occurs. Governance is part of the scientific control system, not an administrative afterthought.
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- The partner cannot state a precise context of use or changes the claim when asked about stage, target, or modality.
- Results rely on an undisclosed benchmark, random splits with probable near-duplicates, or a comparator that does not represent the company’s workflow.
- No model version, data lineage, uncertainty measure, or failure analysis is available.
- The partner seeks unrestricted rights to train on confidential data or retain validation results without addressing third-party restrictions.
- The contract is silent on foreground inventions, candidate compounds, regulatory submissions, publications, derivatives, deletion, or termination.
- A pilot has no pre-agreed threshold, comparator, independent review, or no-go outcome.
- Commercial value is presented as a guaranteed percentage rather than a scenario tied to capacity, costs, and decisions.
- Regulatory language implies approval or endorsement when the relevant FDA material is draft and nonbinding.
A practical go/no-go checklist
Proceed only when the team can answer all of these questions in writing:
- What exact decision will the AI output change, and what happens if it is wrong?
- Does the evidence match the target, data type, discovery stage, and intended user?
- Can the company inspect provenance, splits, comparators, metrics, uncertainty, versions, and failures?
- Are prospective or external tests needed before a larger commitment?
- Who owns or licenses background and foreground IP, compounds, models, data, improvements, and validation results?
- Can each party use results for regulatory work, publications, derivatives, and post-termination development?
- Are security, privacy, retention, deletion, audit, and breach duties explicit?
- What integration, compute, training, monitoring, and revalidation work is required?
- What are the pilot’s success thresholds, resources, timeline, and stop/go consequences?
- Does the proposed structure allocate risk and preserve an evidence-based expansion or exit?
The strongest partnership proposal is therefore not the one with the highest claimed model score. It is the one whose context of use is narrow enough to test, whose evidence can be inspected, whose data and IP rights survive scrutiny, whose regulatory and operational controls are explicit, and whose pilot produces a decision the company is prepared to honor.
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