{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T07:01:38Z","timestamp":1775286098079,"version":"3.50.1"},"reference-count":31,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T00:00:00Z","timestamp":1752537600000},"content-version":"vor","delay-in-days":14,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Bio & Medical Technology Development"},{"DOI":"10.13039\/501100001321","name":"National Research Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001321","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Republic of Korea","award":["RS-2022-NR067933"],"award-info":[{"award-number":["RS-2022-NR067933"]}]},{"name":"Institute of Information & communications Technology Planning & Evaluation"},{"name":"Korea governmen","award":["RS-2021-II211343"],"award-info":[{"award-number":["RS-2021-II211343"]}]},{"name":"Artificial Intelligence Graduate School Program"},{"DOI":"10.13039\/501100002551","name":"Seoul National University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002551","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100031757","name":"AIGENDRUG Co., Ltd.","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100031757","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002551","name":"Seoul National University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002551","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Summary<\/jats:title>\n                  <jats:p>Recent breakthroughs in AI-driven generative models enable the rapid design of extensive molecular libraries, creating an urgent need for fast and accurate drug-likeness evaluation. Traditional approaches, however, rely heavily on structural descriptors and overlook pharmacokinetic (PK) factors such as absorption, distribution, metabolism, and excretion (ADME). Furthermore, existing deep-learning models neglect the complex interdependencies among ADME tasks, which play a pivotal role in determining clinical viability. We introduce ADME-DL (drug likeness), a novel two-step pipeline that first enhances diverse range of molecular foundation models (MFMs) via sequential ADME multi-task learning. By enforcing an A\u2192D\u2192M\u2192E flow\u2014grounded in a data-driven task dependency analysis that aligns with established PK principles\u2014our method more accurately encodes PK information into the learned embedding space. In Step 2, the resulting ADME-informed embeddings are leveraged for drug-likeness classification, distinguishing approved drugs from negative sets drawn from chemical libraries. Through comprehensive experiments, our sequential ADME multi-task learning achieves up to +2.4% improvement over state-of-the-art baselines, and enhancing performance across tested MFMs by up to +18.2%. Case studies with clinically annotated drugs validate that respecting the PK hierarchy produces more relevant predictions, reflecting drug discovery phases. These findings underscore the potential of ADME-DL to significantly enhance the early-stage filtering of candidate molecules, bridging the gap between purely structural screening methods and PK-aware modeling.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code for ADME-DL is available at https:\/\/github.com\/eugenebang\/ADME-DL.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf259","type":"journal-article","created":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T13:02:41Z","timestamp":1752584561000},"page":"i352-i361","source":"Crossref","is-referenced-by-count":6,"title":["ADME-drug-likeness: enriching molecular foundation models via pharmacokinetics-guided multi-task learning for drug-likeness prediction"],"prefix":"10.1093","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9217-8380","authenticated-orcid":false,"given":"Dongmin","family":"Bang","sequence":"first","affiliation":[{"name":"Interdisciplinary Program in Bioinformatics, Seoul National University , Seoul, 08826,","place":["Republic of Korea"]},{"name":"AIGENDRUG Co., Ltd , Seoul, 08758,","place":["Republic of Korea"]}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1074-2102","authenticated-orcid":false,"given":"Juyeon","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Statistics, Seoul National University , Seoul, 08826,","place":["Republic of Korea"]}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3199-0138","authenticated-orcid":false,"given":"Haerin","family":"Song","sequence":"additional","affiliation":[{"name":"Interdisciplinary Program in Artificial Intelligence, Seoul National University , Seoul, 08826,","place":["Republic of Korea"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5385-9546","authenticated-orcid":false,"given":"Sun","family":"Kim","sequence":"additional","affiliation":[{"name":"Interdisciplinary Program in Bioinformatics, Seoul National University , Seoul, 08826,","place":["Republic of Korea"]},{"name":"AIGENDRUG Co., Ltd , Seoul, 08758,","place":["Republic of Korea"]},{"name":"Interdisciplinary Program in Artificial Intelligence, Seoul National University , Seoul, 08826,","place":["Republic of Korea"]},{"name":"Department of Computer Science and Engineering, Seoul National University , Seoul, 08826,","place":["Republic of Korea"]}]}],"member":"286","published-online":{"date-parts":[[2025,7,15]]},"reference":[{"key":"2025071509023447300_btaf259-B1","volume-title":"Drug-Drug Interactions: Scientific and Regulatory 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