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Start with the right unit of analysis
Three different claims are often blended together in investor presentations:
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- Company valuation: what investors currently pay for the business, its cash, platform, partnerships and pipeline.
- Platform capability: what a company says its models can discover or optimize across many programs.
- Drug-candidate evidence: whether one named molecule has a plausible mechanism, reproducible activity, acceptable safety and a demonstrated patient benefit.
These are related but not interchangeable. A company can have a valuable platform and no clinically validated asset. A candidate can show encouraging pharmacology while the company is overvalued, underfunded or unable to manufacture it. Conversely, a low valuation does not make a weak molecule scientifically attractive. Keep the valuation question and the probability-of-success question in separate columns in your analysis.
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“AI-powered” is not a sufficient description. The U.S. Food and Drug Administration (FDA) describes AI use across nonclinical, clinical, postmarketing and manufacturing activities. A company may use software for trial-site selection or manufacturing while the molecule itself was designed by conventional medicinal chemistry. Ask for the precise contribution and the point in the workflow where it changed a decision.
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Map the claimed AI contribution
| Development activity | What to ask | Evidence that would be relevant |
|---|---|---|
| Target discovery | Did a model identify a new target, or rank known targets? | Human genetics, disease biology, target-engagement data and independent validation. |
| Molecule generation | Did the system create the chemical structure, select a known scaffold or suggest analogues? | Prospective synthesis records, design criteria, hit confirmation and novelty analysis. |
| Lead optimization | Which properties were optimized and under what constraints? | Measured potency, selectivity, exposure, solubility, stability and toxicity across successive compounds. |
| Property prediction | Were activity, absorption, distribution, metabolism, excretion or toxicity predictions used to stop or advance compounds? | Prospective performance on compounds unavailable during model training. |
| Trial design or operations | Did AI choose endpoints, enrich a population, forecast enrollment or monitor safety? | Prespecified analyses, operational validation and an explanation of how errors are handled. |
| Manufacturing or quality | Was AI used for process development, release testing or supply planning? | Validated assays, process controls and quality-system documentation. |
Request a program timeline showing which decisions were made by a model, by scientists, or by a combination. “AI discovered” can cover anything from a model proposing one analogue to a system selecting the target, scaffold and development candidate. Those levels of contribution should not be compared as if they were equivalent.
2. Define the model’s context of use
The FDA’s January 2025 draft guidance, Considerations for the Use of Artificial Intelligence To Support Regulatory Decision-Making for Drug and Biological Products, frames model credibility around a particular context of use (COU): the defined question the model is intended to answer and how its output will be used. The document proposes a risk-based approach for AI-generated information or data supporting decisions about safety, effectiveness or quality. It is a draft Level 1 guidance, explicitly “Not for implementation,” and its recommendations are nonbinding; it is not a final regulation.
Write the COU in one sentence
A useful COU identifies the input, output, decision and consequences of error. For example: “Using molecular structure and assay data available before synthesis, the model ranks compounds for biochemical potency against target X to select candidates for testing.” That is materially different from “the model predicts which drug will succeed in patients.” The second claim requires clinical and disease-level evidence that a potency-ranking model cannot supply.
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Audit the model evidence
- Inputs: Identify the assays, structures, patient data or literature used, including data quality and missing values.
- Training separation: Establish whether test compounds, laboratories, patients or time periods were held out before the model was trained.
- Endpoint: Define the exact metric, such as potency, selectivity, exposure or enrollment probability.
- Performance: Report sensitivity, specificity, calibration, ranking quality or another metric appropriate to the decision, rather than a vague accuracy claim.
- Domain shift: Show how performance changes on a new chemical series, disease subtype, assay platform or clinical site.
- Uncertainty: Explain when the model should abstain and how scientists handle low-confidence predictions.
- Error consequences: Distinguish an inexpensive false positive in a screening queue from a false negative that removes a potentially useful molecule, or a wrong safety prediction that exposes volunteers to risk.
- Version control: Record the model, data, code and decision thresholds used for each candidate; silent updates can invalidate comparisons.
A retrospective benchmark can show that a model fit historical data. It does not establish that the model will select a better medicine prospectively. Ask to see a locked evaluation, the full denominator and all failed predictions, not only the examples that advanced.
3. Test the biological rationale before admiring the chemistry
A computationally attractive molecule cannot rescue an invalid target or an implausible disease hypothesis. Treat the biology as an independent diligence workstream.
Target and mechanism questions
- What protein, pathway or genetic dependency is being modulated?
- Is the proposed action inhibition, activation, degradation, modulation or another mechanism?
- What direct target-engagement measurement shows that the molecule binds or affects the intended target in the relevant system?
- Does target engagement produce the predicted functional change in cells, not merely in a purified biochemical assay?
- Are there known resistance mechanisms, compensatory pathways or safety liabilities?
- Does the intended patient population actually depend on this biology, and is there a biomarker to identify those patients?
These are diligence questions, not a universal regulatory checklist. The available evidence does not establish a single target-validation standard or verify any particular candidate. A credible program should nevertheless connect molecular binding, cellular function, disease models and patient selection in a coherent chain.
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Separate affinity from useful pharmacology
Very high binding affinity can be irrelevant if the compound cannot reach the target at a tolerable dose. Examine selectivity against related proteins, intracellular exposure, protein binding, half-life, active metabolites, formulation limits and the exposure required for a pharmacodynamic effect. A model that optimizes one assay endpoint may worsen solubility, permeability, clearance or off-target activity. Look for measured trade-offs across the whole profile.
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4. Move from predictions to prospective experiments
Build an evidence ladder and mark whether each result was predicted before the experiment or explained afterward.
| Evidence level | What it can establish | What it cannot establish |
|---|---|---|
| In-silico prediction | A hypothesis about binding, activity, properties or prioritization. | That the molecule works in cells, animals or people. |
| Retrospective benchmark | Performance on historical data under the stated test design. | Prospective generalization or therapeutic efficacy. |
| Biochemical assay | Interaction or activity in a controlled molecular system. | Cell penetration, disease relevance, safety or patient benefit. |
| Cellular assay | Functional activity and sometimes selectivity in living cells. | Human exposure, tissue distribution or clinical tolerability. |
| Animal pharmacology and toxicology | Exposure, pharmacodynamic response and species-specific safety signals. | Reliable prediction of human efficacy or all human adverse effects. |
| Human trial | Safety, pharmacology and, with suitable design, evidence of patient benefit. | Generalization beyond the studied population, dose and duration. |
Demand prospective, independent confirmation
Ask when the prediction was frozen, who synthesized the compound, and whether the assay laboratory knew the model’s ranking. Replication by an independent laboratory is more informative than repeating an experiment within the discovery team. Insist on negative and failed programs: a platform that reports only successful designs may be selecting its own examples. For animal work, check randomization, blinding where feasible, prespecified endpoints, sex and strain, dose exposure and whether the model was chosen because it reflects the human disease rather than because it is easy to improve.
5. Read the clinical record, not the press release
For every trial, obtain the registry entry, protocol and complete results when available. A headline saying that a candidate “entered the clinic” or showed a “positive signal” leaves most of the decision unresolved.
Clinical diligence checklist
- Phase and purpose: Phase I generally informs safety, tolerability, pharmacokinetics and dose selection. It does not establish patient benefit.
- Population: Record disease definition, severity, prior treatment, biomarker status, exclusions and geographic sites. A narrowly selected population may be appropriate but limits generalization.
- Dose and exposure: Compare administered dose, achieved exposure and pharmacodynamic target engagement with the exposure predicted to be effective.
- Endpoint: Distinguish a validated clinical endpoint from a biomarker, imaging measure or exploratory signal. Note whether the endpoint was prespecified.
- Comparator: Determine whether the study is randomized and controlled, dose-ranging, single-arm or uncontrolled. Historical controls are vulnerable to changes in diagnosis and standard of care.
- Sample size and duration: Small, short studies can miss uncommon harms and provide imprecise efficacy estimates.
- Adverse events: Review serious events, discontinuations, laboratory abnormalities, dose interruptions and exposure-adjusted event rates.
- Reporting status: Separate a completed and fully reported trial from an interim presentation, company announcement or trial that has merely begun.
- Reproducibility: Look for results in a second cohort, independent study or later-stage trial, not only a single subgroup analysis.
Do not infer efficacy from a clean Phase I safety profile. Later-stage, adequately controlled evidence is more decisive for benefit, but no named candidate’s complete trial record or outcome package is established in the evidence considered for this article.
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A 2024 analysis by Jayatunga, Ayers, Bruens, Jayanth and Meier in Drug Discovery Today reported an estimated 80–90% Phase I success rate for AI-discovered molecules. Record that precisely as “80–90% in Phase I — Jayatunga et al., 2024.” It is an early estimate, not a mature end-to-end success rate and not proof that AI is superior to conventional discovery.
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The opened PubMed page and publisher text did not expose the paper’s full methods and denominator details. Before using the percentage as a rigorous comparison, verify the cohort definition, outcome classifications and denominator. “AI-discovered” may include very different degrees of model involvement, and a high Phase I transition rate says little about Phase II efficacy, approval or commercial success.
Andrew Marshall’s November 2025 report in Nature Biotechnology said definitive data on AI-generated drug success were still awaited. It quoted Chris Meier, managing director and partner at Boston Consulting Group: “From the limited phase 2 data we’ve seen so far, there’s no indication that AI-discovered targets or drugs are necessarily any more successful than classically discovered drugs.” That is a statement about the limited later-stage record, not a claim that AI cannot help. It means investors should resist declaring superiority before comparable, adequately reported outcomes exist.
The FDA said in 2025 that it had reviewed more than 500 drug and biological-product submissions with AI components since 2016. This count covers submissions containing AI components; it is not a count of AI-designed candidates, clinical successes or approved AI-discovered drugs.
6. Evaluate the development path separately from efficacy
A candidate can be scientifically credible and still fail as a development program. Score these risks independently rather than allowing a persuasive platform story to hide them.
| Risk area | Questions to answer |
|---|---|
| Manufacturing | Can the active ingredient and finished formulation be produced reproducibly at clinical and commercial scale? Are impurities, polymorphs and process changes controlled? |
| Formulation and delivery | Does the route of administration achieve adequate exposure without an impractical schedule or device? |
| Regulatory path | What evidence will regulators require for the target population, endpoint, companion diagnostic and manufacturing process? Which AI outputs support a decision, and how are they documented? |
| Intellectual property | Are composition-of-matter, use and process rights available for the relevant jurisdictions and term, and can freedom to operate be defended? |
| Enrollment | Is the biomarker-defined population large enough, and can sites recruit against current standards of care? |
| Competitive treatment | What benefit, convenience or safety advantage would justify replacing approved or late-stage alternatives? |
| Market access | Will payers reimburse the treatment at the price needed to support development, and is the evidence meaningful to health-technology assessors? |
| Financing | How much capital is required to reach the next value-inflection study, and can the company fund it without unacceptable dilution or delay? |
None of these factors proves that a molecule works. They determine whether a potentially useful molecule can reach patients and generate a sustainable return.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.7. Keep valuation in its proper place
Use valuation as a market expectation to explain, not as scientific evidence to confirm. A high valuation may reflect cash on hand, a broad platform, licensing rights, management reputation, scarcity value, a partnership announcement or expectations for several programs. It can also embed optimistic assumptions about probability of success, price and timing.
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Build an asset-level view
- List each candidate, indication, target, development phase and ownership or licensing status.
- Assign the strongest evidence currently available: model output, biochemical, cellular, animal, Phase I, randomized efficacy or replicated later-stage evidence.
- Estimate what the next experiment can actually resolve. A dose-ranging study may answer exposure and safety questions but not confirm long-term benefit.
- Identify binary risks: target validity, dose-limiting toxicity, biomarker failure, enrollment, manufacturing or an inadequate comparator.
- Separate platform optionality from the probability of the lead asset. Future programs are possibilities, not clinical outcomes.
- Compare the implied expectations in the valuation with observable milestones, required capital and realistic timelines.
Because no company-specific valuation, cash runway, patent position, trial record or commercial facts were established for this general framework, those figures must be sourced separately for any investment decision.
8. A practical comparison framework for two candidates
Compare like with like. Do not place a platform-level prediction beside a completed clinical outcome and call them equivalent.
| Comparison axis | Candidate A | Candidate B |
|---|---|---|
| Disease and target validation | Record evidence and uncertainty. | Record evidence and uncertainty. |
| Exact AI role and COU | State the model’s task, inputs and decision. | State the model’s task, inputs and decision. |
| Model validation | Prospective or retrospective; independent or internal; metric and domain shift. | Prospective or retrospective; independent or internal; metric and domain shift. |
| Experimental evidence | Biochemical, cellular, animal and replication status. | Biochemical, cellular, animal and replication status. |
| Clinical evidence | Phase, population, dose, endpoint, comparator, enrollment and reporting completeness. | Phase, population, dose, endpoint, comparator, enrollment and reporting completeness. |
| Safety and pharmacology | Exposure, target engagement, adverse events and discontinuations. | Exposure, target engagement, adverse events and discontinuations. |
| Development feasibility | Manufacturing, formulation, regulatory, IP and enrollment risks. | Manufacturing, formulation, regulatory, IP and enrollment risks. |
| Commercial context | Standard of care, differentiation, access and funding needs. | Standard of care, differentiation, access and funding needs. |
Fill the table with documented facts, and write “not stated” where a company has not disclosed a comparable value. Missing information is a diligence finding, not permission to substitute a favorable assumption.
9. Common red flags and how to follow up
“Our model has near-perfect accuracy”
Ask whether the test set was independent, whether compounds from the same chemical series appeared in training, and whether the metric reflects the actual decision. Request prospective results and calibration, not only a benchmark chart.
“The molecule was designed in weeks”
Speed may describe proposal generation, not synthesis, optimization, toxicology or clinical development. Request the complete timeline and the number of compounds tested, including discarded ones.
“The candidate is already in humans”
Check the trial phase and objective. Entry into Phase I demonstrates that a program reached clinical testing; it does not demonstrate efficacy or regulatory approval.
“The platform has hundreds of programs”
Count active, disclosed candidates and their stages. A large preclinical list can reflect exploratory hypotheses rather than assets with validated biology.
“The valuation proves institutional confidence”
Trace what the valuation assumes about probability of success, timing, dilution and revenue. Market confidence is a hypothesis to test against evidence, not a substitute for it.
No failures are disclosed
Ask how many model-proposed compounds failed potency, selectivity, exposure, toxicity or manufacturability screens. Selective reporting makes a platform appear more reliable than it is.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minute10. What FDA’s AI guidance means for diligence
The January 2025 draft guidance is useful as a vocabulary for asking how a model will support a regulatory decision. It does not certify a company’s model, mandate one algorithm or create a safe-harbor status for “AI-designed” molecules. The central practical question is whether the model is credible for its stated COU and whether the sponsor can document data provenance, validation, limitations, change control and human oversight.
FDA Commissioner Robert M. Califf, M.D., said in the agency’s January 6, 2025 announcement: “With the appropriate safeguards in place, artificial intelligence has transformative potential to advance clinical research and accelerate medical product development to improve patient care.” The safeguards are the operative part of that statement. Acceleration is valuable only when the resulting evidence is reliable enough to protect participants and support a sound benefit–risk decision.
Bottom-line decision rule
Give the greatest weight to reproducible, prospective evidence that connects a validated biological mechanism to meaningful outcomes in well-designed human trials. Treat model benchmarks, platform breadth, promotional language and company valuation as context that helps explain expectations but cannot establish efficacy. An AI-designed candidate becomes genuinely investable when its specific AI contribution is transparent, its model is credible for a defined task, its biology survives independent experiments, its clinical results are complete and appropriately controlled, and its development and commercial risks are manageable.
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