{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T14:48:01Z","timestamp":1774277281041,"version":"3.50.1"},"reference-count":35,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2023,5,24]],"date-time":"2023-05-24T00:00:00Z","timestamp":1684886400000},"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":["61832019"],"award-info":[{"award-number":["61832019"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hunan Provincial Science and Technology Program","award":["2019CB1007"],"award-info":[{"award-number":["2019CB1007"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Computational approaches for identifying the protein\u2013ligand binding affinity can greatly facilitate drug discovery and development. At present, many deep learning-based models are proposed to predict the protein\u2013ligand binding affinity and achieve significant performance improvement. However, protein\u2013ligand binding affinity prediction still has fundamental challenges. One challenge is that the mutual information between proteins and ligands is hard to capture. Another challenge is how to find and highlight the important atoms of the ligands and residues of the proteins.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>To solve these limitations, we develop a novel graph neural network strategy with the Vina distance optimization terms (GraphscoreDTA) for predicting protein\u2013ligand binding affinity, which takes the combination of graph neural network, bitransport information mechanism and physics-based distance terms into account for the first time. Unlike other methods, GraphscoreDTA can not only effectively capture the protein\u2013ligand pairs\u2019 mutual information but also highlight the important atoms of the ligands and residues of the proteins. The results show that GraphscoreDTA significantly outperforms existing methods on multiple test sets. Furthermore, the tests of drug\u2013target selectivity on the cyclin-dependent kinase and the homologous protein families demonstrate that GraphscoreDTA is a reliable tool for protein\u2013ligand binding affinity prediction.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>The resource codes are available at https:\/\/github.com\/CSUBioGroup\/GraphscoreDTA.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad340","type":"journal-article","created":{"date-parts":[[2023,5,25]],"date-time":"2023-05-25T00:58:09Z","timestamp":1684976289000},"source":"Crossref","is-referenced-by-count":75,"title":["GraphscoreDTA: optimized graph neural network for protein\u2013ligand binding affinity prediction"],"prefix":"10.1093","volume":"39","author":[{"given":"Kaili","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , Changsha 410083, China"}]},{"given":"Renyi","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , Changsha 410083, China"}]},{"given":"Jing","family":"Tang","sequence":"additional","affiliation":[{"name":"Research Program in Systems Oncology, University of Helsinki , 00014 Helsinki, Finland"},{"name":"Department of Biochemistry and Developmental Biology, University of Helsinki , 00014 Helsinki, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0188-1394","authenticated-orcid":false,"given":"Min","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , Changsha 410083, China"},{"name":"Hunan Provincial Engineering Research Center of Intelligent Computing in Biology and Medicine , Changsha 410083, 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