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Injecting synthetic competitors drawn from each target’s own wrong-answer score distribution, holding the true site, candidate pool and ranker fixed, reduced top-five recovery by 16.8 points on training folds and 17.0 points on the held-out test fold.
Lacuna, an open-source Python tool for discovering cryptic binding pockets, generates a conformational ensemble from any input structure, detects pockets independently in every conformer, clusters the detections into persistent sites across the ensemble, and ranks those sites with a model fitted on within-structure pairs.
Pockets from a protein language model at locations where geometry finds no concavity improves single-structure recovery by 8.5% (95% CI +4.0 to +13.6) on test-fold data, and lets a five-conformer ensemble match a twenty-conformer one at a third of the wall clock.