{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T13:49:22Z","timestamp":1764683362801,"version":"3.46.0"},"reference-count":65,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T00:00:00Z","timestamp":1764633600000},"content-version":"vor","delay-in-days":31,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012151","name":"Sanming Project of Medicine in Shenzhen","doi-asserted-by":"publisher","award":["SZSM202211032"],"award-info":[{"award-number":["SZSM202211032"]}],"id":[{"id":"10.13039\/501100012151","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Drug repurposing significantly reduces development costs and shortens research cycles, making it a critical strategy in drug discovery. An emerging class of drug repurposing approaches applies deep learning to structural data. However, these methods often depend on static representations of molecular and protein structures, which may not fully capture the dynamic character of compound\u2013protein interactions. To address these challenges and enhance the accuracy of compound\u2013protein interaction predictions, we introduce an innovative prompt-based multimodal representation learning framework that dynamically encodes task-specific contextual information for drug repurposing. Specifically, the framework includes a dynamic prompt generation module that adaptively creates receptor-specific prompts and a prompt calibration module for effective multimodal feature integration and optimization. When applied to identifying FDA-approved drug candidates targeting G-protein-coupled receptors, our method achieved a 7.4% improvement in mean absolute error compared with state-of-the-art methods, with up to a 25.1% improvement for specific target-of-interest. By demonstrating potential in repurposing non-opioid treatments without the risk of addiction for safe pain management, our method has the capacity to advance drug discovery and meet a wide range of therapeutic needs.<\/jats:p>","DOI":"10.1093\/bib\/bbaf636","type":"journal-article","created":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T12:58:15Z","timestamp":1763211495000},"source":"Crossref","is-referenced-by-count":0,"title":["Prompt-based multimodal representation learning for drug repurposing"],"prefix":"10.1093","volume":"26","author":[{"given":"Jinliang","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science and Artificial Intelligence , Zhengzhou University, No. 100 Science Avenue, Zhengzhou, Henan 450001,","place":["China"]},{"name":"Centre for Artificial Intelligence , University of Technology Sydney, 15 Broadway, Ultimo, NSW 2007,","place":["Australia"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaicheng","family":"U","sequence":"additional","affiliation":[{"name":"Tri-Institutional Computational Biology & Medicine , Weill Cornell Medicine, 445 East 69th Street, New York, NY 10021,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dhruv","family":"Rana","sequence":"additional","affiliation":[{"name":"NYU Department of Psychology , 6 Washington Pl, New York, NY 10003,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sophia","family":"Meixuan Zhang","sequence":"additional","affiliation":[{"name":"Department of Pediatrics , Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiahui","family":"Yu","sequence":"additional","affiliation":[{"name":"NYU Department of Psychology , 6 Washington Pl, 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