{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T22:38:08Z","timestamp":1788043088658,"version":"build-2784847793"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2021,10,19]],"date-time":"2021-10-19T00:00:00Z","timestamp":1634601600000},"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\/100017054","name":"NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization","doi-asserted-by":"publisher","award":["U1909208"],"award-info":[{"award-number":["U1909208"]}],"id":[{"id":"10.13039\/100017054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772552"],"award-info":[{"award-number":["61772552"]}],"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":["62072473"],"award-info":[{"award-number":["62072473"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013314","name":"111 Project","doi-asserted-by":"publisher","award":["B18059"],"award-info":[{"award-number":["B18059"]}],"id":[{"id":"10.13039\/501100013314","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hunan Provinvial Science and Technology Program","award":["2018WK4001"],"award-info":[{"award-number":["2018WK4001"]}]},{"name":"Hunan Provinvial Science and Technology Program","award":["2020GK2019"],"award-info":[{"award-number":["2020GK2019"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Identifying drug\u2013target interactions (DTIs) is a crucial step in drug repurposing and drug discovery. Accurately identifying DTIs in silico can significantly shorten development time and reduce costs. Recently, many sequence-based methods are proposed for DTI prediction and improve performance by introducing the attention mechanism. However, these methods only model single non-covalent inter-molecular interactions among drugs and proteins and ignore the complex interaction between atoms and amino acids.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In this article, we propose an end-to-end bio-inspired model based on the convolutional neural network (CNN) and attention mechanism, named HyperAttentionDTI, for predicting DTIs. We use deep CNNs to learn the feature matrices of drugs and proteins. To model complex non-covalent inter-molecular interactions among atoms and amino acids, we utilize the attention mechanism on the feature matrices and assign an attention vector to each atom or amino acid. We evaluate HpyerAttentionDTI on three benchmark datasets and the results show that our model achieves significantly improved performance compared with the state-of-the-art baselines. Moreover, a case study on the human Gamma-aminobutyric acid receptors confirm that our model can be used as a powerful tool to predict DTIs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The codes of our model are available at https:\/\/github.com\/zhaoqichang\/HpyerAttentionDTI and https:\/\/zenodo.org\/record\/5039589.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab715","type":"journal-article","created":{"date-parts":[[2021,10,13]],"date-time":"2021-10-13T15:30:18Z","timestamp":1634139018000},"page":"655-662","source":"Crossref","is-referenced-by-count":273,"title":["HyperAttentionDTI: improving drug\u2013protein interaction prediction by sequence-based deep learning with attention mechanism"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8319-9793","authenticated-orcid":false,"given":"Qichang","family":"Zhao","sequence":"first","affiliation":[{"name":"Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University , Changsha 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haochen","family":"Zhao","sequence":"additional","affiliation":[{"name":"Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University , Changsha 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Zheng","sequence":"additional","affiliation":[{"name":"Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University , Changsha 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1516-0480","authenticated-orcid":false,"given":"Jianxin","family":"Wang","sequence":"additional","affiliation":[{"name":"Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University , Changsha 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,10,19]]},"reference":[{"key":"2023020108494161200_btab715-B1","doi-asserted-by":"crossref","first-page":"4633","DOI":"10.1093\/bioinformatics\/btaa544","article-title":"Deepcda: deep cross-domain compound\u2013protein affinity prediction through lstm and convolutional neural networks","volume":"36","author":"Abbasi","year":"2020","journal-title":"Bioinformatics"},{"key":"2023020108494161200_btab715-B2","doi-asserted-by":"crossref","first-page":"170433","DOI":"10.1109\/ACCESS.2020.3024238","article-title":"Deeph-dta: deep learning for predicting drug\u2013target interactions: a case study of covid-19 drug repurposing","volume":"8","author":"Abdel-Basset","year":"2020","journal-title":"IEEE Access"},{"key":"2023020108494161200_btab715-B3","doi-asserted-by":"crossref","first-page":"1663","DOI":"10.1093\/bib\/bbz103","article-title":"Computational\/in silico methods in drug target and lead prediction","volume":"21","author":"Agamah","year":"2020","journal-title":"Brief. 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