{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T18:07:02Z","timestamp":1789150022538,"version":"build-2803163510"},"reference-count":70,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T00:00:00Z","timestamp":1789084800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Recep Tayyip Erdogan University Development Foundation","award":["020260070210514"],"award-info":[{"award-number":["020260070210514"]}]},{"award":["020260070210514"],"award-info":[{"award-number":["020260070210514"]}],"id":[{"id":"https:\/\/ror.org\/0468j1635","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Pharmaceuticals"],"abstract":"<jats:p>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\u2013ADME-T docking-triage cascade that decides which compounds are worth docking. Methods: The gate combines an ensemble estimate of drug\u2013target 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 Kd, under three split protocols over five seeds. The routing decision was then examined against molecular docking, in which 407 compound\u2013target pairs were docked into six withheld kinases. Results: A Morgan-fingerprint gradient-boosting model reached a concordance index of 0.866\u00b10.006, with 0.813 for unseen targets and 0.720 for unseen drugs. Across eight ADME-T endpoints, the area under the ROC curve ranged from 0.65 to 0.91. On the cold-target split the cascade reduced the compounds sent to docking by 86% while retaining 61% of the true strong binders. Docking measured that reduction at 85%, 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.<\/jats:p>","DOI":"10.3390\/ph19091445","type":"journal-article","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T17:14:59Z","timestamp":1789146899000},"page":"1445","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Confidence-Gated Triage: Coupling Drug\u2013Target Affinity and ADME-T Predictions to Prioritise Compounds for Docking"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9689-2239","authenticated-orcid":false,"given":"Gozde","family":"Yalcin Ozkat","sequence":"first","affiliation":[{"name":"Department of Bioengineering, Faculty of Engineering & Architecture, Recep Tayyip Erdogan University, Rize 53100, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1038\/s41573-019-0024-5","article-title":"Applications of machine learning in drug discovery and development","volume":"18","author":"Vamathevan","year":"2019","journal-title":"Nat. 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