Researchers working with AI‑generated miniprotein binders often struggle to tell which computational scores truly predict experimental success. Raw AUC values can look promising but hide how many designs would actually be tested in a lab budget. Different structure predictors give overlapping yet imperfect signals, and vendor assay results frequently disagree, inflating apparent noise. Expression failures masquerade as binding failures, and target identity can dominate any performance comparison, leading to overly optimistic results if models are evaluated with random splits that leak target information.
A practical workflow addresses these issues. First, define an evaluable set using only designs that received at least one vendor call, then compute hit rates with Wilson confidence intervals to handle small‑sample subgroups. Rank‑normalize each predictor’s scores and combine them into consensus metrics (mean, median, min) – this consistently outperforms the best single score while staying scale‑free. Translate ranking performance into precision‑at‑N curves to see exactly how many hits you gain at realistic testing budgets (e.g., top 50 or 100 designs). Quantify vendor disagreement with Cohen’s κ and log‑log KD plots to establish a ceiling for any model’s AUC. Control for expression by conditioning on designs with medium or high expression before re‑evaluating scores, isolating interface quality from solubility effects. Finally, assess model generalizability with grouped cross‑validation by target; the gap between random and grouped CV reveals leakage from target‑specific features, and the grouped AUC should be reported as the honest estimate. Permutation importance on a held‑out target block shows which features survive when target memorization is removed.
By following these steps—defining a solid evaluable set, using consensus ranking, budgeting with precision@N, accounting for vendor and expression noise, and employing target‑aware validation—teams can obtain reliable, actionable insights from AI‑designed binder datasets and avoid the common pitfalls that inflate perceived performance.
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