{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:07:49Z","timestamp":1783436869743,"version":"3.54.6"},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2021,4,19]],"date-time":"2021-04-19T00:00:00Z","timestamp":1618790400000},"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":["61971296"],"award-info":[{"award-number":["61971296"]}],"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":["U19A2078"],"award-info":[{"award-number":["U19A2078"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Sichuan Science and Technology Planning Project","award":["2020YFG0319"],"award-info":[{"award-number":["2020YFG0319"]}]},{"name":"Sichuan Science and Technology Planning Project","award":["2020YFH0186"],"award-info":[{"award-number":["2020YFH0186"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Drug-target interaction (DTI) prediction has drawn increasing interest due to its substantial position in the drug discovery process. Many studies have introduced computational models to treat DTI prediction as a regression task, which directly predict the binding affinity of drug-target pairs. However, existing studies (i) ignore the essential correlations between atoms when encoding drug compounds and (ii) model the interaction of drug-target pairs simply by concatenation. Based on those observations, in this study, we propose an end-to-end model with multiple attention blocks to predict the binding affinity scores of drug-target pairs. Our proposed model offers the abilities to (i) encode the correlations between atoms by a relation-aware self-attention block and (ii) model the interaction of drug representations and target representations by the multi-head attention block. Experimental results of DTI prediction on two benchmark datasets show our approach outperforms existing methods, which are benefit from the correlation information encoded by the relation-aware self-attention block and the interaction information extracted by the multi-head attention block. Moreover, we conduct the experiments on the effects of max relative position length and find out the best max relative position length value $k \\in \\{3, 5\\}$. Furthermore, we apply our model to predict the binding affinity of Corona Virus Disease 2019 (COVID-19)-related genome sequences and $3137$ FDA-approved drugs.<\/jats:p>","DOI":"10.1093\/bib\/bbab117","type":"journal-article","created":{"date-parts":[[2021,3,15]],"date-time":"2021-03-15T12:09:42Z","timestamp":1615810182000},"source":"Crossref","is-referenced-by-count":105,"title":["Deep drug-target binding affinity prediction with multiple attention blocks"],"prefix":"10.1093","volume":"22","author":[{"given":"Yuni","family":"Zeng","sequence":"first","affiliation":[{"name":"College of Computer Science, Sichuan University, Chengdu, Sichuan,610065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangru","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer Science, Sichuan University, Chengdu, Sichuan,610065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yujie","family":"Luo","sequence":"additional","affiliation":[{"name":"Shenzhen Peng Cheng Laboratory, Shenzhen, 518052, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuedong","family":"Li","sequence":"additional","affiliation":[{"name":"Chengdu Sobey Digital Technology Co., Ltd, Chengdu, 610041,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dezhong","family":"Peng","sequence":"additional","affiliation":[{"name":"College of Computer Science, Sichuan University, Chengdu, Sichuan,610065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,4,19]]},"reference":[{"key":"2021090814534700700_ref1","doi-asserted-by":"crossref","first-page":"170433","DOI":"10.1109\/ACCESS.2020.3024238","article-title":"Deep learning for predicting drug-target interactions: A case study of COVID-19 drug repurposing","volume":"8","author":"Abdel-Basset","year":"2020","journal-title":"IEEE Access"},{"key":"2021090814534700700_ref2","first-page":"06450","article-title":"Layer normalization","author":"Ba","year":"2016","journal-title":"CoRR, abs\/1607"},{"key":"2021090814534700700_ref3","doi-asserted-by":"crossref","first-page":"784","DOI":"10.1016\/j.csbj.2020.03.025","article-title":"Predicting commercially available antiviral drugs that may act on the novel coronavirus (sars-cov-2) through a drug-target interaction deep learning model","volume":"18","author":"Bo","year":"2020","journal-title":"Comput Struct Biotechnol J"},{"key":"2021090814534700700_ref4","doi-asserted-by":"crossref","first-page":"1046","DOI":"10.1038\/nbt.1990","article-title":"Comprehensive analysis of kinase inhibitor selectivity","volume":"29","author":"Davis","year":"2011","journal-title":"Nat Biotechnol"},{"issue":"4","key":"2021090814534700700_ref5","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1093\/bib\/bbr013","article-title":"Exploiting drug-disease relationships for computational drug repositioning","volume":"12","author":"Dudley","year":"2011","journal-title":"Briefings Bioinform"},{"key":"2021090814534700700_ref6","doi-asserted-by":"crossref","first-page":"3371","DOI":"10.24963\/ijcai.2018\/468","article-title":"Interpretable drug target prediction using deep neural representation","volume-title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI, July 13\u201319, Stockholm, Sweden","author":"Gao","year":"2018"},{"key":"2021090814534700700_ref7","first-page":"6489","article-title":"A lightweight approach for natural language inference","volume-title":"The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, Honolulu, Hawaii, USA, January 27\u2013February 1, 2019","author":"Guo","year":"2019"},{"key":"2021090814534700700_ref8","first-page":"770","article-title":"Deep residual learning for image recognition","volume-title":"2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016","author":"He","year":"2016"},{"issue":"1","key":"2021090814534700700_ref9","doi-asserted-by":"crossref","first-page":"24:1","DOI":"10.1186\/s13321-017-0209-z","article-title":"Simboost: a read-across approach for predicting drug-target binding affinities using gradient boosting machines","volume":"9","author":"He","year":"2017","journal-title":"J. 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