AI can help biotechnology teams search large datasets, propose molecular designs and prioritize experiments. Its value depends on a defined task, suitable data and evidence that the output works in the intended setting. Read a claim by asking what was predicted, what was measured, and which decision the result can support.
Use this guide when assessing a paper, product demonstration or company announcement. The examples establish particular research capabilities; each application still needs its own validation.
Start with concrete examples
| Application | Useful output | Check before relying on it |
|---|---|---|
| Protein structure prediction | A proposed three-dimensional structure with confidence estimates. | Sequence and model version, local confidence, domain placement and relevant experimental structures. |
| Protein design | Candidate structures or sequences for a specified function. | Expression, folding, binding or functional measurements for the candidates actually tested. |
| Data analysis and prioritization | Ranked observations, candidate features or suggested experiments. | Independent evaluation data, leakage controls, comparison with a useful baseline and reproducibility. |
| Clinical or manufacturing decisions | An output intended for a specified regulated workflow. | Evidence suited to that context, human responsibilities, change control and applicable regulatory status. |
AlphaFold DB documentation describes confidence measures including local pLDDT and predicted aligned error. High confidence in one region does not settle the placement of every domain or establish a molecule's therapeutic effect. Check the specific model: capabilities and output types vary across versions.
The original 2023 RFdiffusion study went beyond generating attractive structures by experimentally characterizing designed proteins. That combination of computational proposals and measured tests is what makes the result informative. It establishes performance on the studied design tasks, with further work required for any proposed medicine.
Locate a claim on the evidence ladder
- Retrospective benchmark: performance on a dataset with defined labels and a documented split.
- Prospective experiment: predictions made before new measurements, using predefined success criteria.
- Independent replication: evidence that another team or setting can reproduce the relevant result.
- Clinical or operational validation: performance in the intended workflow, including errors and consequences.
- Authorized use where required: a regulator's decision for a particular product and scope, followed by appropriate monitoring.
These are questions to investigate, not a universal regulatory sequence. A tool that helps search literature may need different validation from a model used to influence treatment. FDA's January 2025 draft guidance on AI in drug development presents a risk-based credibility framework tied to context of use. It remains labeled draft in the source checked for this guide.
Ask six questions about the evaluation
- What was held out? Closely related molecules, repeated patients or copies of the same experiment can make an apparent test easier than deployment.
- What is the comparator? A useful baseline could be an established method, expert workflow or simpler statistical model.
- Which metric matters? Ranking accuracy, laboratory hit rate, clinical benefit and time saved answer different questions.
- What is the denominator? Count every candidate tested or case evaluated, including failures and exclusions.
- Where does performance change? Examine unfamiliar populations, instruments, laboratories and biological families.
- Can the result be checked? Seek data provenance, model version, analysis settings and an explanation of unavailable material.
For literature assistance, open each cited paper and verify that its population, methods and results support the sentence. Generated citations and polished summaries need the same source checks as any other research note. Use approved systems for confidential sequences, patient records or proprietary results.
A faster screen needs a fair comparison
Write your conclusion narrowly: “The model improved the observed hit rate in this evaluation” is more useful than a promise that it will shorten every drug-development program.
Keep a claim record you can update
For each consequential claim, record the source and date, exact task, input data, model version, comparator, result, limitations and next experiment. Separate the author's measurement from your inference. Mark the claim for review when a replication, trial result, model update or regulatory decision changes its meaning.
A practical adoption decision includes a named reviewer, an error-handling process and a way to compare the tool with the existing workflow. Start where outputs can be checked and where the cost of an error is understood.