{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T19:36:51Z","timestamp":1786131411224,"version":"3.56.0"},"reference-count":75,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2022,12,10]],"date-time":"2022-12-10T00:00:00Z","timestamp":1670630400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,19]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The recently reported machine learning- or deep learning-based scoring functions (SFs) have shown exciting performance in predicting protein\u2013ligand binding affinities with fruitful application prospects. However, the differentiation between highly similar ligand conformations, including the native binding pose (the global energy minimum state), remains challenging that could greatly enhance the docking. In this work, we propose a fully differentiable, end-to-end framework for ligand pose optimization based on a hybrid SF called DeepRMSD+Vina combined with a multi-layer perceptron (DeepRMSD) and the traditional AutoDock Vina SF. The DeepRMSD+Vina, which combines (1) the root mean square deviation (RMSD) of the docking pose with respect to the native pose and (2) the AutoDock Vina score, is fully differentiable; thus is capable of optimizing the ligand binding pose to the energy-lowest conformation. Evaluated by the CASF-2016 docking power dataset, the DeepRMSD+Vina reaches a success rate of 94.4%, which outperforms most reported SFs to date. We evaluated the ligand conformation optimization framework in practical molecular docking scenarios (redocking and cross-docking tasks), revealing the high potentialities of this framework in drug design and discovery. Structural analysis shows that this framework has the ability to identify key physical interactions in protein\u2013ligand binding, such as hydrogen-bonding. Our work provides a paradigm for optimizing ligand conformations based on deep learning algorithms. The DeepRMSD+Vina model and the optimization framework are available at GitHub repository https:\/\/github.com\/zchwang\/DeepRMSD-Vina_Optimization.<\/jats:p>","DOI":"10.1093\/bib\/bbac520","type":"journal-article","created":{"date-parts":[[2022,12,11]],"date-time":"2022-12-11T11:26:55Z","timestamp":1670758015000},"source":"Crossref","is-referenced-by-count":52,"title":["A fully differentiable ligand pose optimization framework guided by deep learning and a traditional scoring function"],"prefix":"10.1093","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4554-133X","authenticated-orcid":false,"given":"Zechen","family":"Wang","sequence":"first","affiliation":[{"name":"School of Physics, Shandong University , Jinan, Shandong 250100 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1179-2106","authenticated-orcid":false,"given":"Liangzhen","family":"Zheng","sequence":"additional","affiliation":[{"name":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen, Guangdong 518055 , China"},{"name":"Shanghai Zelixir Biotech Company Ltd. , Shanghai 200030 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Shanghai Zelixir Biotech Company Ltd. , Shanghai 200030 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingzhi","family":"Lin","sequence":"additional","affiliation":[{"name":"Shanghai Zelixir Biotech Company Ltd. , Shanghai 200030 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhihao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Physics, Shandong University , Jinan, Shandong 250100 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adams Wai-Kin","family":"Kong","sequence":"additional","affiliation":[{"name":"Rolls-Royce Corporate Lab, Nanyang Technological University , Singapore 637551 , Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2499-026X","authenticated-orcid":false,"given":"Yuguang","family":"Mu","sequence":"additional","affiliation":[{"name":"School of Biological Sciences, Nanyang Technological University , Singapore 637551 , Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4791-7540","authenticated-orcid":false,"given":"Yanjie","family":"Wei","sequence":"additional","affiliation":[{"name":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen, Guangdong 518055 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weifeng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Physics, Shandong University , Jinan, Shandong 250100 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,12,10]]},"reference":[{"key":"2023011917141574800_ref1","doi-asserted-by":"crossref","first-page":"4331","DOI":"10.3390\/ijms20184331","article-title":"Molecular docking: shifting paradigms in drug discovery","volume":"20","author":"Pinzi","year":"2019","journal-title":"Int J Mol Sci"},{"key":"2023011917141574800_ref2","doi-asserted-by":"crossref","first-page":"5851","DOI":"10.1021\/jm060999m","article-title":"Prediction of protein\u2212ligand interactions. 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