Confidence-gated triage: Coupling drug–target affinity and ADME-T predictions to prioritise compounds for docking

Abstract

Background/Objectives: Molecular docking and molecular dynamics are accurate but computationally expensive, so the compounds entering them must be chosen well. The present study proposes CADT, a confidence-gated affinity–ADME-T docking-triage cascade that decides which compounds are worth docking. Methods: The gate combines an ensemble estimate of drug–target affinity with its epistemic uncertainty and an applicability-domain check. Predicted absorption, distribution, metabolism, excretion, and toxicity (ADME-T) developability is added as a soft flag. All components were trained on openly licensed Therapeutics Data Commons data. Ranking was assessed on the DAVIS and KIBA kinase panels and on BindingDB (Formula presented.), under three split protocols over five seeds. The routing decision was then examined against molecular docking, in which 407 compound–target pairs were docked into six withheld kinases. Results: A Morgan-fingerprint gradient-boosting model reached a concordance index of (Formula presented.), with (Formula presented.) for unseen targets and (Formula presented.) for unseen drugs. Across eight ADME-T endpoints, the area under the ROC curve ranged from (Formula presented.) to (Formula presented.). On the cold-target split the cascade reduced the compounds sent to docking by (Formula presented.) while retaining (Formula presented.) of the true strong binders. Docking measured that reduction at (Formula presented.), and at an equal budget, the gate enriched true binders more than the docking score itself. Conclusions: A transparent pre-screen can prioritise compounds ahead of structure-based calculation at a fraction of its cost. However, the uncertainty and applicability-domain terms act as an abstention mechanism rather than an accuracy gain, and that abstention is not free.