{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T15:15:25Z","timestamp":1777043725495,"version":"3.51.4"},"reference-count":27,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T00:00:00Z","timestamp":1775779200000},"content-version":"vor","delay-in-days":9,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Innovation (STI) 2030\u2014Major Projects","award":["2022ZD0208700"],"award-info":[{"award-number":["2022ZD0208700"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62376264"],"award-info":[{"award-number":["62376264"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"State Key Laboratory of Chemo and Biosensing"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,4,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Traditional drug discovery methods are costly and inefficient, while existing deep learning approaches remain limited by task specificity and practical applicability. Accurately modeling protein\u2013molecule interactions is critical for advancing virtual screening, docking, and drug design.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We propose DrugBLIP, a multi-task graph transformer model based on SE(3)-equivariant architectures, to unify protein\u2013molecule interaction learning. By integrating contrastive learning, matching tasks, and docking optimization, DrugBLIP captures 3D spatial relationships through a hybrid graph transformer framework. Evaluations demonstrate state-of-the-art performance: DrugBLIP achieves an AUROC of 0.8217 and BEDROC of 0.5743 on virtual screening, outperforming traditional and deep learning baselines by 10%\u2013127% across metrics. It also attains 91.2% top-1 docking success on CASF-2016 and 41.8% target fishing accuracy, showcasing robustness in diverse scenarios. Additionally, DrugBLIP reduces computational time by 700\u00d7 compared to traditional docking tools.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Code is available at https:\/\/github.com\/Wolkenwandler\/DrugBLIP and archived at Zenodo with DOI: 10.5281\/zenodo.16990700.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag069","type":"journal-article","created":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T11:48:16Z","timestamp":1775735296000},"source":"Crossref","is-referenced-by-count":0,"title":["DrugBLIP: exploring the protein\u2013molecule interaction mechanisms with a multi-task learning graph transformer"],"prefix":"10.1093","volume":"42","author":[{"given":"Rubo","family":"Wang","sequence":"first","affiliation":[{"name":"Institute of Microelectronics, Chinese Academy of Sciences , Beijing, 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