{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T12:11:41Z","timestamp":1760616701591,"version":"build-2065373602"},"reference-count":47,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:00:00Z","timestamp":1758672000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62422113"],"award-info":[{"award-number":["62422113"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shenzhen Science and Technology Program","award":["20231129091450002"],"award-info":[{"award-number":["20231129091450002"]}]},{"name":"Shenzhen Polytechnic University Research Fund","award":["6025310047K"],"award-info":[{"award-number":["6025310047K"]}]},{"name":"Key Field of Department of Education of Guangdong Province","award":["2022ZDZX2082"],"award-info":[{"award-number":["2022ZDZX2082"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Accurate prediction of protein\u2013ligand binding (PLB) relationships plays a crucial role in drug discovery, which helps identify drugs that modulate the activity of specific targets. Traditional biological assays for measuring PLB relationships are time consuming and costly. In addition, models for predicting PLB relationships have been developed and widely used in drug discovery tasks. However, learning more accurate PLB representations is essential to meet the stringent standards required for drug discovery.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We propose an image-based PLB representation learning framework, called ImagePLB, which equips ligand representation learner (LRL) and protein representation learner (PRL) to accept 3D multi-view ligand images and protein graphs as input, respectively, and learns rich interaction information between ligand and protein through a binding representation learner (BRL). Considering the scarcity of protein\u2013ligand pairs, we further propose a multi-level next trajectory prediction (MLNTP) task to pre-train ImagePLB on the 4D flexible dynamics trajectory of 16\u2009972 complexes, including ligand level, protein level, and complex level, to learn information related to trajectories. Besides, by introducing trajectory regularization (TR), we effectively alleviate the problem of high (even almost identical) feature similarity caused by adjacent trajectories. Compared with the current state-of-the-art methods, ImagePLB has achieved competitive improvements on PLB-related prediction tasks, including protein\u2013ligand affinity and efficacy prediction tasks. This study opens the door to the image-based PLB learning paradigm.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>All data and implementation details of code can be obtained from https:\/\/github.com\/HongxinXiang\/ImagePLB.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf535","type":"journal-article","created":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T15:50:50Z","timestamp":1758729050000},"source":"Crossref","is-referenced-by-count":0,"title":["An image-based protein\u2013ligand binding representation learning framework via multi-level flexible dynamics trajectory pre-training"],"prefix":"10.1093","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8345-8735","authenticated-orcid":false,"given":"Hongxin","family":"Xiang","sequence":"first","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University , Changsha, Hunan 410082,","place":["China"]}]},{"given":"Mingquan","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University , Changsha, Hunan 410082,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5823-478X","authenticated-orcid":false,"given":"Linlin","family":"Hou","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University , Changsha, Hunan 410082,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8113-9367","authenticated-orcid":false,"given":"Shuting","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Computer Science & Technology, Wuhan University of Science and Technology , Wuhan, Hubei 430081,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8910-0929","authenticated-orcid":false,"given":"Jianmin","family":"Wang","sequence":"additional","affiliation":[{"name":"The Interdisciplinary Graduate Program in Integrative Biotechnology, Yonsei University , Incheon 03722,","place":["Korea"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7993-0803","authenticated-orcid":false,"given":"Jun","family":"Xia","sequence":"additional","affiliation":[{"name":"School of Engineering, Westlake University , Hangzhou, Zhejiang 310024,","place":["China"]}]},{"given":"Wenjie","family":"Du","sequence":"additional","affiliation":[{"name":"School of Software Engineering, University of Science and Technology of China , Hefei, Anhui 230026,","place":["China"]}]},{"given":"Sisi","family":"Yuan","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics and Genomics, University of North Carolina at Charlotte , Charlotte, NC 28223,","place":["United 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