{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T13:56:21Z","timestamp":1776952581736,"version":"3.51.4"},"reference-count":40,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2022,2,26]],"date-time":"2022-02-26T00:00:00Z","timestamp":1645833600000},"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\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2412018QD022"],"award-info":[{"award-number":["2412018QD022"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"publisher","award":["61976050"],"award-info":[{"award-number":["61976050"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"publisher","award":["61972384"],"award-info":[{"award-number":["61972384"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Jilin Provincial Science and Technology Department","award":["20190302109GX"],"award-info":[{"award-number":["20190302109GX"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,3,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Accurate identification of drug\u2013target interactions (DTIs) plays a crucial role in drug discovery. Compared with traditional experimental methods that are labor-intensive and time-consuming, computational methods are more and more popular in recent years. Conventional computational methods almost simply view heterogeneous networks which integrate diverse drug-related and target-related dataset instead of fully exploring drug and target similarities. In this paper, we propose a new method, named DTIHNC, for $\\mathbf{D}$rug\u2013$\\mathbf{T}$arget $\\mathbf{I}$nteraction identification, which integrates $\\mathbf{H}$eterogeneous $\\mathbf{N}$etworks and $\\mathbf{C}$ross-modal similarities calculated by relations between drugs, proteins, diseases and side effects. Firstly, the low-dimensional features of drugs, proteins, diseases and side effects are obtained from original features by a denoising autoencoder. Then, we construct a heterogeneous network across drug, protein, disease and side-effect nodes. In heterogeneous network, we exploit the heterogeneous graph attention operations to update the embedding of a node based on information in its 1-hop neighbors, and for multi-hop neighbor information, we propose random walk with restart aware graph attention to integrate more information through a larger neighborhood region. Next, we calculate cross-modal drug and protein similarities from cross-scale relations between drugs, proteins, diseases and side effects. Finally, a multiple-layer convolutional neural network deeply integrates similarity information of drugs and proteins with the embedding features obtained from heterogeneous graph attention network. Experiments have demonstrated its effectiveness and better performance than state-of-the-art methods. Datasets and a stand-alone package are provided on Github with website https:\/\/github.com\/ningq669\/DTIHNC.<\/jats:p>","DOI":"10.1093\/bib\/bbac016","type":"journal-article","created":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T12:07:30Z","timestamp":1642162050000},"source":"Crossref","is-referenced-by-count":33,"title":["Identifying drug\u2013target interactions via heterogeneous graph attention networks combined with cross-modal similarities"],"prefix":"10.1093","volume":"23","author":[{"given":"Lu","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Northeast Normal University, Jingyue Street, 130117, Changchun, China"}]},{"given":"Jiahao","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Northeast Normal University, Jingyue Street, 130117, Changchun, China"}]},{"given":"Yue","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Information Science and Technology, Dalian Maritime University, Lingshui Street, 116026, Dalian, China"}]},{"given":"Qiao","family":"Ning","sequence":"additional","affiliation":[{"name":"Department of Information Science and Technology, Dalian Maritime University, Lingshui Street, 116026, Dalian, China"}]},{"given":"Na","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Northeast Normal University, Jingyue Street, 130117, Changchun, China"}]},{"given":"Minghao","family":"Yin","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Northeast Normal University, Jingyue Street, 130117, Changchun, China"}]}],"member":"286","published-online":{"date-parts":[[2022,2,26]]},"reference":[{"key":"2022031506330216900_ref1","article-title":"Predicting Drug-Target on Heterogeneous Network with Co-rank","author":"Huang","year":"2018","journal-title":"The 8th International Conference on Computer Engineering and Networks (CENet)"},{"key":"2022031506330216900_ref2","article-title":"Novel opportunities for computational biology and sociology in drug discovery: Corrected paper","author":"Yao","year":"2010","journal-title":"Trends Biotechnol"},{"key":"2022031506330216900_ref3","article-title":"Xiaoli Li and Chee Keong Kwoh. 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