{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T21:11:09Z","timestamp":1784927469925,"version":"3.55.0"},"reference-count":53,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":["62002178"],"award-info":[{"award-number":["62002178"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Drug\u2013target interaction (DTI) prediction is an essential step in drug repositioning. A few graph neural network (GNN)-based methods have been proposed for DTI prediction using heterogeneous biological data. However, existing GNN-based methods only aggregate information from directly connected nodes restricted in a drug-related or a target-related network and are incapable of capturing high-order dependencies in the biological heterogeneous graph. In this paper, we propose a metapath-aggregated heterogeneous graph neural network (MHGNN) to capture complex structures and rich semantics in the biological heterogeneous graph for DTI prediction. Specifically, MHGNN enhances heterogeneous graph structure learning and high-order semantics learning by modeling high-order relations via metapaths. Additionally, MHGNN enriches high-order correlations between drug-target pairs (DTPs) by constructing a DTP correlation graph with DTPs as nodes. We conduct extensive experiments on three biological heterogeneous datasets. MHGNN favorably surpasses 17 state-of-the-art methods over 6 evaluation metrics, which verifies its efficacy for DTI prediction. The code is available at https:\/\/github.com\/Zora-LM\/MHGNN-DTI.<\/jats:p>","DOI":"10.1093\/bib\/bbac578","type":"journal-article","created":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T14:37:15Z","timestamp":1672670235000},"source":"Crossref","is-referenced-by-count":73,"title":["Metapath-aggregated heterogeneous graph neural network for drug\u2013target interaction prediction"],"prefix":"10.1093","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9361-7231","authenticated-orcid":false,"given":"Mei","family":"Li","sequence":"first","affiliation":[{"name":"Tianjin Key Laboratory of Network and Data Security Technology , China"},{"name":"College of Computer Science, Nankai University , 300350, Tianjin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5039-0922","authenticated-orcid":false,"given":"Xiangrui","family":"Cai","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Network and Data Security Technology , China"},{"name":"College of Computer Science, Nankai University , 300350, Tianjin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6887-6231","authenticated-orcid":false,"given":"Sihan","family":"Xu","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Network and Data Security Technology , China"},{"name":"College of Cyber Science, Nankai University , 300350, Tianjin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5588-9017","authenticated-orcid":false,"given":"Hua","family":"Ji","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Network and Data Security Technology , China"},{"name":"College of Computer Science, Nankai University , 300350, Tianjin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,1,2]]},"reference":[{"issue":"7607","key":"2023011917105140600_ref1","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1038\/534314a","article-title":"Can you teach old drugs new tricks?","volume":"534","author":"Nosengo","year":"2016","journal-title":"Nature News"},{"key":"2023011917105140600_ref2","first-page":"3371","volume-title":"IJCAI","author":"Gao","year":"2018"},{"issue":"6","key":"2023011917105140600_ref3","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1093\/bioinformatics\/btaa880","article-title":"Moltrans: molecular interaction transformer for drug-target interaction prediction","volume":"37","author":"Huang","year":"2021","journal-title":"Bioinformatics"},{"issue":"8","key":"2023011917105140600_ref4","doi-asserted-by":"crossref","first-page":"1140","DOI":"10.1093\/bioinformatics\/btaa921","article-title":"Graphdta: predicting drug\u2013target binding affinity with graph neural networks","volume":"37","author":"Nguyen","year":"2021","journal-title":"Bioinformatics"},{"issue":"3","key":"2023011917105140600_ref5","doi-asserted-by":"crossref","first-page":"bbaa205","DOI":"10.1093\/bib\/bbaa205","article-title":"Dti-mlcd: predicting drug-target interactions using multi-label learning with community detection method","volume":"22","author":"Chu","year":"2021","journal-title":"Brief Bioinform"},{"key":"2023011917105140600_ref6","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.semcancer.2020.01.010","article-title":"Structure-based drug repositioning: potential and limits","volume":"68","author":"Adasme","year":"2021","journal-title":"Semin Cancer Biol"},{"issue":"2","key":"2023011917105140600_ref7","doi-asserted-by":"crossref","first-page":"1656","DOI":"10.1093\/bib\/bbaa003","article-title":"Exploration of databases and methods supporting drug repurposing: a comprehensive survey","volume":"22","author":"Tanoli","year":"2021","journal-title":"Brief Bioinform"},{"key":"2023011917105140600_ref8","first-page":"1109","volume-title":"SIGKDD","author":"Liu","year":"2021"},{"key":"2023011917105140600_ref9","first-page":"2946","volume-title":"SIGKDD","author":"Hao","year":"2021"},{"issue":"1","key":"2023011917105140600_ref10","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1093\/bioinformatics\/bty543","article-title":"Neodti: neural integration of neighbor information from a heterogeneous network for discovering new drug\u2013target interactions","volume":"35","author":"Wan","year":"2019","journal-title":"Bioinformatics"},{"key":"2023011917105140600_ref11","doi-asserted-by":"crossref","first-page":"bbab346","DOI":"10.1093\/bib\/bbab346","article-title":"Drug\u2013target interaction predication via multi-channel graph neural networks","volume":"23","author":"Li","year":"2021","journal-title":"Brief Bioinform"},{"issue":"5","key":"2023011917105140600_ref12","doi-asserted-by":"crossref","first-page":"bbaa430","DOI":"10.1093\/bib\/bbaa430","article-title":"An end-to-end heterogeneous graph representation learning-based framework for drug\u2013target interaction prediction","volume":"22","author":"Peng","year":"2021","journal-title":"Brief Bioinform"},{"key":"2023011917105140600_ref13","first-page":"1","article-title":"Imchgan: inductive matrix completion with heterogeneous graph attention networks for drug-target interactions prediction","volume":"19","author":"Li","year":"2021","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"issue":"10","key":"2023011917105140600_ref14","doi-asserted-by":"crossref","first-page":"2847","DOI":"10.1093\/bioinformatics\/btac164","article-title":"Supervised graph co-contrastive learning for drug\u2013target interaction prediction","volume":"38","author":"Li","year":"2022","journal-title":"Bioinformatics"},{"key":"2023011917105140600_ref15","volume-title":"ICLR","author":"Kipf","year":"2016"},{"key":"2023011917105140600_ref16","volume-title":"ICLR","author":"Veli\u010dkovi\u0107","year":"2018"},{"key":"2023011917105140600_ref17","first-page":"793","volume-title":"SIGKDD","author":"Zhang","year":"2019"},{"key":"2023011917105140600_ref18","first-page":"2022","volume-title":"WWW","author":"Wang","year":"2019"},{"key":"2023011917105140600_ref19","first-page":"2331","volume-title":"WWW","author":"Xinyu","year":"2020"},{"key":"2023011917105140600_ref20","volume-title":"IJCAI","author":"Zhao","year":"2020"},{"key":"2023011917105140600_ref21","first-page":"135","volume-title":"SIGKDD","author":"Dong","year":"2017"},{"issue":"1","key":"2023011917105140600_ref22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-017-00680-8","article-title":"A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information","volume":"8","author":"Luo","year":"2017","journal-title":"Nat Commun"},{"issue":"7","key":"2023011917105140600_ref23","doi-asserted-by":"crossref","first-page":"1775","DOI":"10.1039\/C9SC04336E","article-title":"Target identification among known drugs by deep learning from heterogeneous networks","volume":"11","author":"Zeng","year":"2020","journal-title":"Chem Sci"},{"issue":"6","key":"2023011917105140600_ref24","doi-asserted-by":"crossref","first-page":"bbab275","DOI":"10.1093\/bib\/bbab275","article-title":"A heterogeneous network embedding framework for predicting similarity-based drug-target interactions","volume":"22","author":"An","year":"2021","journal-title":"Brief Bioinform"},{"key":"2023011917105140600_ref25","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.neucom.2020.12.068","article-title":"Prediction of drug-target interactions based on multi-layer network representation learning","volume":"434","author":"Shang","year":"2021","journal-title":"Neurocomputing"},{"key":"2023011917105140600_ref26","article-title":"Translating embeddings for modeling multi-relational data","volume":"26","author":"Bordes","year":"2013","journal-title":"NeurIPS"},{"key":"2023011917105140600_ref27","first-page":"137","article-title":"Heterogeneous graph convolutional network integrates multi-modal similarities for drug-target interaction prediction","volume-title":"BIBM","author":"Lu","year":"2021"},{"key":"2023011917105140600_ref28","first-page":"2071","article-title":"Complex embeddings for simple link prediction","volume-title":"ICML","author":"Trouillon","year":"2016"},{"key":"2023011917105140600_ref29","first-page":"11","article-title":"Drug target discovery using knowledge graph embeddings","volume-title":"SAC","author":"Mohamed","year":"2019"},{"key":"2023011917105140600_ref30","first-page":"588","article-title":"Discovering dti and ddi by knowledge graph with mhrw and improved neural network","volume-title":"BIBM","author":"Zhang","year":"2021"},{"issue":"1","key":"2023011917105140600_ref31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-021-27137-3","article-title":"A unified drug-target interaction prediction framework based on knowledge graph and recommendation system","volume":"12","author":"Ye","year":"2021","journal-title":"Nat Commun"},{"key":"2023011917105140600_ref32","volume-title":"SIGIR","author":"He","year":"2017"},{"key":"2023011917105140600_ref33","first-page":"1263","volume-title":"ICML","author":"Gilmer","year":"2017"},{"key":"2023011917105140600_ref34","first-page":"1025","article-title":"Inductive representation learning on large graphs","volume-title":"NeurIPS","author":"Hamilton","year":"2017"},{"issue":"D1","key":"2023011917105140600_ref35","doi-asserted-by":"crossref","first-page":"D1074","DOI":"10.1093\/nar\/gkx1037","article-title":"Drugbank 5.0: a major update to the drugbank database for 2018","volume":"46","author":"Wishart","year":"2018","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"2023011917105140600_ref36","doi-asserted-by":"crossref","first-page":"D605","DOI":"10.1093\/nar\/gkaa1074","article-title":"The string database in 2021: customizable protein\u2013protein networks, and functional characterization of user-uploaded gene\/measurement sets","volume":"49","author":"Szklarczyk","year":"2021","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"2023011917105140600_ref37","doi-asserted-by":"crossref","first-page":"D1075","DOI":"10.1093\/nar\/gkv1075","article-title":"The sider database of drugs and side effects","volume":"44","author":"Kuhn","year":"2016","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"2023011917105140600_ref38","doi-asserted-by":"crossref","first-page":"D1138","DOI":"10.1093\/nar\/gkaa891","article-title":"Comparative toxicogenomics database (ctd): update 2021","volume":"49","author":"Davis","year":"2021","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"2023011917105140600_ref39","doi-asserted-by":"crossref","first-page":"D506","DOI":"10.1093\/nar\/gky1049","article-title":"Uniprot: a worldwide hub of protein knowledge","volume":"47","author":"UniProt Consortium","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2023011917105140600_ref40","volume-title":"ICLR","author":"Sun","year":"2019"},{"key":"2023011917105140600_ref41","volume-title":"ICLR","author":"Clevert","year":"2016"},{"key":"2023011917105140600_ref42","first-page":"1","volume-title":"IJCNN","author":"Zheng","year":"2018"},{"issue":"suppl_1","key":"2023011917105140600_ref46","first-page":"D1035","article-title":"Drugbank 3.0: a comprehensive resource for \u2018omics\u2019 research on drugs","volume":"39","author":"Knox","year":"2010","journal-title":"Nucleic Acids Res"},{"issue":"suppl_1","key":"2023011917105140600_ref47","doi-asserted-by":"crossref","first-page":"D767","DOI":"10.1093\/nar\/gkn892","article-title":"Human protein reference database-2009 update","volume":"37","author":"Keshava Prasad","year":"2009","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"2023011917105140600_ref48","doi-asserted-by":"crossref","first-page":"D1104","DOI":"10.1093\/nar\/gks994","article-title":"The comparative toxicogenomics database: update 2013","volume":"41","author":"Davis","year":"2013","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"2023011917105140600_ref49","first-page":"343","article-title":"A side effect resource to capture phenotypic effects of drugs","volume":"6","author":"Kuhn","year":"2010","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"2023011917105140600_ref50","doi-asserted-by":"crossref","first-page":"D1091","DOI":"10.1093\/nar\/gkt1068","article-title":"Drugbank 4.0: shedding new light on drug metabolism","volume":"42","author":"Law","year":"2014","journal-title":"Nucleic Acids Res"},{"key":"2023011917105140600_ref51","first-page":"gkw993","article-title":"Drugcentral: online drug compendium","author":"Ursu","year":"2016","journal-title":"Nucleic Acids Res"},{"issue":"9","key":"2023011917105140600_ref52","doi-asserted-by":"crossref","first-page":"2044","DOI":"10.1021\/ci9001876","article-title":"Pubchem as a source of polypharmacology","volume":"49","author":"Chen","year":"2009","journal-title":"J Chem Inf Model"},{"issue":"suppl_1","key":"2023011917105140600_ref53","doi-asserted-by":"crossref","first-page":"D258","DOI":"10.1093\/nar\/gkh036","article-title":"The gene ontology (go) database and informatics resource","volume":"32","author":"Gene Ontology Consortium","year":"2004","journal-title":"Nucleic Acids Res"},{"key":"2023011917105140600_ref43","volume-title":"Elementary mathematical theory of classification and prediction","author":"Tanimoto","year":"1958"},{"issue":"13","key":"2023011917105140600_ref44","doi-asserted-by":"crossref","first-page":"i232","DOI":"10.1093\/bioinformatics\/btn162","article-title":"Prediction of drug-target interaction networks from the integration of chemical and genomic spaces","volume":"24","author":"Yamanishi","year":"2008","journal-title":"Bioinformatics"},{"issue":"23","key":"2023011917105140600_ref45","doi-asserted-by":"crossref","first-page":"4485","DOI":"10.1093\/bioinformatics\/btab473","article-title":"Multidti: drug\u2013target interaction prediction based on multi-modal representation learning to bridge the gap between new chemical entities and known heterogeneous network","volume":"37","author":"Zhou","year":"2021","journal-title":"Bioinformatics"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/1\/bbac578\/48782113\/bbac578.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/1\/bbac578\/48782113\/bbac578.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T17:29:23Z","timestamp":1674149363000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbac578\/6966534"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1]]},"references-count":53,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,1,19]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbac578","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,1]]},"published":{"date-parts":[[2023,1]]},"article-number":"bbac578"}}