{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T06:10:42Z","timestamp":1775715042383,"version":"3.50.1"},"reference-count":35,"publisher":"Oxford University Press (OUP)","issue":"21","license":[{"start":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T00:00:00Z","timestamp":1662940800000},"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 [NSFC","doi-asserted-by":"crossref","award":["62102191"],"award-info":[{"award-number":["62102191"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"key technology key projects of Jiangsu Province Science and Technology Department","award":["BE2020721"],"award-info":[{"award-number":["BE2020721"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,10,31]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>The capability to predict the potential drug binding affinity against a protein target has always been a fundamental challenge in silico drug discovery. The traditional experiments in vitro and in vivo are costly and time-consuming which need to search over large compound space. Recent years have witnessed significant success on deep learning-based models for drug-target binding affinity prediction task.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Following the recent success of the Transformer model, we propose a multigranularity protein\u2013ligand interaction (MGPLI) model, which adopts the Transformer encoders to represent the character-level features and fragment-level features, modeling the possible interaction between residues and atoms or their segments. In addition, we use the convolutional neural network to extract higher-level features based on transformer encoder outputs and a highway layer to fuse the protein and drug features. We evaluate MGPLI on different protein\u2013ligand interaction datasets and show the improvement of prediction performance compared to state-of-the-art baselines.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The model scripts are available at https:\/\/github.com\/IILab-Resource\/MGDTA.git<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac597","type":"journal-article","created":{"date-parts":[[2022,9,10]],"date-time":"2022-09-10T12:00:32Z","timestamp":1662811232000},"page":"4859-4867","source":"Crossref","is-referenced-by-count":30,"title":["MGPLI: exploring multigranular representations for protein\u2013ligand interaction prediction"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0732-051X","authenticated-orcid":false,"given":"Junjie","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University , Nanjing, Jiangsu, China"}]},{"given":"Jie","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University , Nanjing, Jiangsu, China"}]},{"given":"Huiting","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University , Nanjing, Jiangsu, China"}]},{"given":"MengDie","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University , Nanjing, Jiangsu, China"}]},{"given":"Yun","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University , Nanjing, Jiangsu, China"}]},{"given":"Yun","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University , Nanjing, Jiangsu, China"},{"name":"Institute of Medical Informatics and Management, Nanjing Medical University , Nanjing, Jiangsu, China"},{"name":"Department of Information, The First Affiliated Hospital, Nanjing Medical University , Nanjing, Jiangsu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6665-6710","authenticated-orcid":false,"given":"Liang","family":"Cheng","sequence":"additional","affiliation":[{"name":"NHC and CAMS Key Laboratory of Molecular Probe and Targeted Theranostics, Harbin Medical University , Harbin, HeiLongJiang, China"},{"name":"College of Bioinformatics Science and Technology, Harbin Medical University , Harbin, HeiLongJiang, China"}]}],"member":"286","published-online":{"date-parts":[[2022,9,12]]},"reference":[{"key":"2022103112490935400_btac597-B1","first-page":"38","article-title":"Ligityscore: convolutional neural network for binding-affinity predictions","volume":"3","author":"Azzopardi","year":"2021","journal-title":"Bioinformatics"},{"key":"2022103112490935400_btac597-B2","author":"Ba","year":"2016"},{"key":"2022103112490935400_btac597-B3","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1093\/bioinformatics\/btq112","article-title":"A machine learning approach to predicting protein\u2013ligand binding affinity with applications to molecular docking","volume":"26","author":"Ballester","year":"2010","journal-title":"Bioinformatics"},{"key":"2022103112490935400_btac597-B4","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1021\/acs.jcim.8b00839","article-title":"GuacaMol: benchmarking models for de novo molecular design","volume":"59","author":"Brown","year":"2019","journal-title":"J. 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