In brief: Protein AI evaluation should match the scientific claim. A generative model can produce a sequence, but evidence for folding, binding, activity or safety is a different question.
Define the endpoint before generating
Decide whether you are measuring sequence novelty, structural similarity, biochemical activity or predictive performance on a benchmark. Choose metrics and negative controls before inspecting outputs. A high model score only measures what that score was designed to approximate.
Assess data leakage and distribution shift
Protein families can have extensive sequence homology. Random train/test splits may overestimate performance when closely related proteins appear in both. Where the research goal involves generalization to new folds or families, split the benchmark to represent that difficulty.
Capture reproducibility metadata
- Model and package versions, checkpoint identity and access route.
- Prompts, track masks, decoding configuration and random seeds if available.
- Dataset names, licenses, filters and preprocessing.
- Failure rates, excluded results and human review criteria.
- Baseline comparison and uncertainty estimates.
Use an evaluation ladder
Begin with input validity and simple baselines, then run computational checks appropriate to the task. Structural confidence can flag issues but cannot certify activity or drug-like properties. Use relevant experiments for claims of real-world biological performance.
Report results accurately
Separate generated, predicted, simulated and experimentally observed outcomes. Include caveats for unsupported targets and conditions. A transparent negative result is valuable to other researchers and is preferable to a polished but unverified claim.
Primary sources and further reading
Editorial review: October 2026. This guide is educational. Model availability and APIs may change; confirm exact versions in the linked documentation. Generated structures and sequences require independent scientific validation.