{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T10:10:42Z","timestamp":1784110242251,"version":"3.55.0"},"reference-count":54,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2021,4,8]],"date-time":"2021-04-08T00:00:00Z","timestamp":1617840000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"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"]}]},{"name":"Degree & Postgraduate Education Reform Project of Hunan Province","award":["2019JGYB051"],"award-info":[{"award-number":["2019JGYB051"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2282019SYLB004"],"award-info":[{"award-number":["2282019SYLB004"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Biomolecular recognition between ligand and protein plays an essential role in drug discovery and development. However, it is extremely time and resource consuming to determine the protein\u2013ligand binding affinity by experiments. At present, many computational methods have been proposed to predict binding affinity, most of which usually require protein 3D structures that are not often available. Therefore, new methods that can fully take advantage of sequence-level features are greatly needed to predict protein\u2013ligand binding affinity and accelerate the drug discovery process. We developed a novel deep learning approach, named DeepDTAF, to predict the protein\u2013ligand binding affinity. DeepDTAF was constructed by integrating local and global contextual features. More specifically, the protein-binding pocket, which possesses some special properties for directly binding the ligand, was firstly used as the local input feature for protein\u2013ligand binding affinity prediction. Furthermore, dilated convolution was used to capture multiscale long-range interactions. We compared DeepDTAF with the recent state-of-art methods and analyzed the effectiveness of different parts of our model, the significant accuracy improvement showed that DeepDTAF was a reliable tool for affinity prediction. The resource codes and data are available at https: \/\/github.com\/KailiWang1\/DeepDTAF.<\/jats:p>","DOI":"10.1093\/bib\/bbab072","type":"journal-article","created":{"date-parts":[[2021,2,23]],"date-time":"2021-02-23T12:14:31Z","timestamp":1614082471000},"source":"Crossref","is-referenced-by-count":172,"title":["DeepDTAF: a deep learning method to predict protein\u2013ligand binding affinity"],"prefix":"10.1093","volume":"22","author":[{"given":"Kaili","family":"Wang","sequence":"first","affiliation":[{"name":"Central China Normal University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renyi","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaohang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science at Old Dominion University, Norfolk, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,4,8]]},"reference":[{"key":"2021090815525262700_ref1","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1038\/nrd2829","article-title":"Targeting innate immunity protein kinase signalling in inflammation","volume":"8","author":"Gaestel","year":"2009","journal-title":"Nat Rev Drug Discov"},{"key":"2021090815525262700_ref2","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1007\/978-1-4939-2269-7_22","article-title":"Drug affinity responsive target stability (DARTS) for small-molecule target identification","volume":"1263","author":"Pai","year":"2015","journal-title":"Methods 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