{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T08:26:02Z","timestamp":1784881562151,"version":"3.55.0"},"reference-count":60,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2022,3,15]],"date-time":"2022-03-15T00:00:00Z","timestamp":1647302400000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFB0204403"],"award-info":[{"award-number":["2018YFB0204403"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Research and Development Project of Guangdong Province","award":["2021B0101310002"],"award-info":[{"award-number":["2021B0101310002"]}]},{"name":"Shenzhen Basic Research Fund","award":["RCYX2020071411473419"],"award-info":[{"award-number":["RCYX2020071411473419"]}]},{"name":"Shenzhen Basic Research Fund","award":["KQTD20200820113106007"],"award-info":[{"award-number":["KQTD20200820113106007"]}]},{"name":"Shenzhen Basic Research Fund","award":["JSGG20201102163800001"],"award-info":[{"award-number":["JSGG20201102163800001"]}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2020JQ04"],"award-info":[{"award-number":["ZR2020JQ04"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11874238"],"award-info":[{"award-number":["11874238"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,13]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Scoring functions are important components in molecular docking for structure-based drug discovery. Traditional scoring functions, generally empirical- or force field-based, are robust and have proven to be useful for identifying hits and lead optimizations. Although multiple highly accurate deep learning- or machine learning-based scoring functions have been developed, their direct applications for docking and screening are limited. We describe a novel strategy to develop a reliable protein\u2013ligand scoring function by augmenting the traditional scoring function Vina score using a correction term (OnionNet-SFCT). The correction term is developed based on an AdaBoost random forest model, utilizing multiple layers of contacts formed between protein residues and ligand atoms. In addition to the Vina score, the model considerably enhances the AutoDock Vina prediction abilities for docking and screening tasks based on different benchmarks (such as cross-docking dataset, CASF-2016, DUD-E and DUD-AD). Furthermore, our model could be combined with multiple docking applications to increase pose selection accuracies and screening abilities, indicating its wide usage for structure-based drug discoveries. Furthermore, in a reverse practice, the combined scoring strategy successfully identified multiple known receptors of a plant hormone. To summarize, the results show that the combination of data-driven model (OnionNet-SFCT) and empirical scoring function (Vina score) is a good scoring strategy that could be useful for structure-based drug discoveries and potentially target fishing in future.<\/jats:p>","DOI":"10.1093\/bib\/bbac051","type":"journal-article","created":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T12:10:46Z","timestamp":1643717446000},"source":"Crossref","is-referenced-by-count":120,"title":["Improving protein\u2013ligand docking and screening accuracies by incorporating a scoring function correction term"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1179-2106","authenticated-orcid":false,"given":"Liangzhen","family":"Zheng","sequence":"first","affiliation":[{"name":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China"},{"name":"Shanghai Zelixir Biotech Company Ltd., Shanghai 200030, 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518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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"}]},{"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, 60 Nanyang Drive 637551, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,3,14]]},"reference":[{"key":"2022051813074098200_ref1","doi-asserted-by":"crossref","first-page":"407","DOI":"10.2174\/138920306778559395","article-title":"Scoring Functions for Protein-Ligand Docking","volume":"7","author":"Bentham Science Publisher BSP","year":"2006","journal-title":"Curr Protein Pept 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