{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T17:52:35Z","timestamp":1786038755111,"version":"3.56.0"},"reference-count":54,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T00:00:00Z","timestamp":1702598400000},"content-version":"vor","delay-in-days":23,"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":["32271292"],"award-info":[{"award-number":["32271292"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31872723"],"award-info":[{"award-number":["31872723"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012246","name":"Priority Academic Program Development of Jiangsu Higher Education Institutions","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012246","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Protein\u2013ligand interactions are increasingly profiled at high-throughput, playing a vital role in lead compound discovery and drug optimization. Accurate prediction of binding pose and binding affinity constitutes a pivotal challenge in advancing our computational understanding of protein\u2013ligand interactions. However, inherent limitations still exist, including high computational cost for conformational search sampling in traditional molecular docking tools, and the unsatisfactory molecular representation learning and intermolecular interaction modeling in deep learning-based methods. Here we propose a geometry-aware attention-based deep learning model, GAABind, which effectively predicts the pocket\u2013ligand binding pose and binding affinity within a multi-task learning framework. Specifically, GAABind comprehensively captures the geometric and topological properties of both binding pockets and ligands, and employs expressive molecular representation learning to model intramolecular interactions. Moreover, GAABind proficiently learns the intermolecular many-body interactions and simulates the dynamic conformational adaptations of the ligand during its interaction with the protein through meticulously designed networks. We trained GAABind on the PDBbindv2020 and evaluated it on the CASF2016 dataset; the results indicate that GAABind achieves state-of-the-art performance in binding pose prediction and shows comparable binding affinity prediction performance. Notably, GAABind achieves a success rate of 82.8% in binding pose prediction, and the Pearson correlation between predicted and experimental binding affinities reaches up to 0.803. Additionally, we assessed GAABind\u2019s performance on the severe acute respiratory syndrome coronavirus 2 main protease cross-docking dataset. In this evaluation, GAABind demonstrates a notable success rate of 76.5% in binding pose prediction and achieves the highest Pearson correlation coefficient in binding affinity prediction compared with all baseline methods.<\/jats:p>","DOI":"10.1093\/bib\/bbad462","type":"journal-article","created":{"date-parts":[[2023,12,16]],"date-time":"2023-12-16T02:00:00Z","timestamp":1702692000000},"source":"Crossref","is-referenced-by-count":28,"title":["GAABind: a geometry-aware attention-based network for accurate protein\u2013ligand binding pose and binding affinity prediction"],"prefix":"10.1093","volume":"25","author":[{"given":"Huishuang","family":"Tan","sequence":"first","affiliation":[{"name":"Key Laboratory of Ministry of Education for Protein Science, School of Life Sciences, Tsinghua University , Beijing 100084 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhixin","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Ministry of Education for Protein Science, School of Life Sciences, Tsinghua University , Beijing 100084 , China"},{"name":"Institute of Molecular Enzymology, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University , Suzhou 215123 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guang","family":"Hu","sequence":"additional","affiliation":[{"name":"MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Key Laboratory of Pathogen Bioscience and Anti-infective Medicine, Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University , Suzhou 215123 , China"},{"name":"Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development , Soochow University, Suzhou 215123 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,12,14]]},"reference":[{"issue":"9","key":"2023121601590508200_ref1","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.1080\/17460441.2021.1910673","article-title":"Knowledge graphs and their applications in drug discovery","volume":"16","author":"MacLean","year":"2021","journal-title":"Expert Opin Drug Discov"},{"issue":"7","key":"2023121601590508200_ref2","doi-asserted-by":"crossref","first-page":"1775","DOI":"10.1039\/C9SC04336E","article-title":"Target identification among known drugs by deep learning from heterogeneous networks","volume":"11","author":"Zeng","year":"2020","journal-title":"Chem Sci"},{"issue":"1","key":"2023121601590508200_ref3","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.drudis.2015.08.001","article-title":"Identifying compound efficacy targets in phenotypic drug 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