{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T16:01:38Z","timestamp":1780761698419,"version":"3.54.1"},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2021,12,2]],"date-time":"2021-12-02T00:00:00Z","timestamp":1638403200000},"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\/501100000038","name":"Natural Science and Engineering Research Council of Canada","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2020YFB2104400"],"award-info":[{"award-number":["2020YFB2104400"]}],"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":["61428209"],"award-info":[{"award-number":["61428209"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,17]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Drug repositioning is proposed to find novel usages for existing drugs. Among many types of drug repositioning approaches, predicting drug\u2013drug interactions (DDIs) helps explore the pharmacological functions of drugs and achieves potential drugs for novel treatments. A number of models have been applied to predict DDIs. The DDI network, which is constructed from the known DDIs, is a common part in many of the existing methods. However, the functions of DDIs are different, and thus integrating them in a single DDI graph may overlook some useful information. We propose a graph convolutional network with multi-kernel (GCNMK) to predict potential DDIs. GCNMK adopts two DDI graph kernels for the graph convolutional layers, namely, increased DDI graph consisting of \u2018increase\u2019-related DDIs and decreased DDI graph consisting of \u2018decrease\u2019-related DDIs. The learned drug features are fed into a block with three fully connected layers for the DDI prediction. We compare various types of drug features, whereas the target feature of drugs outperforms all other types of features and their concatenated features. In comparison with three different DDI prediction methods, our proposed GCNMK achieves the best performance in terms of area under receiver operating characteristic curve and area under precision-recall curve. In case studies, we identify the top 20 potential DDIs from all unknown DDIs, and the top 10 potential DDIs from the unknown DDIs among breast, colorectal and lung neoplasms-related drugs. Most of them have evidence to support the existence of their interactions. fangxiang.wu@usask.ca<\/jats:p>","DOI":"10.1093\/bib\/bbab511","type":"journal-article","created":{"date-parts":[[2021,11,8]],"date-time":"2021-11-08T20:12:45Z","timestamp":1636402365000},"source":"Crossref","is-referenced-by-count":72,"title":["Predicting drug\u2013drug interactions by graph convolutional network with multi-kernel"],"prefix":"10.1093","volume":"23","author":[{"given":"Fei","family":"Wang","sequence":"first","affiliation":[{"name":"Division of Biomedical Engineering, University of Saskatchewan, 57 Campus Drive, S7N 5A9, Saskatchewan, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiujuan","family":"Lei","sequence":"additional","affiliation":[{"name":"School of Computer Science, Shaanxi Normal University, 620 West Chang\u2019an Avenue, 710119, Shaanxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Liao","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Hainan Normal University, 99 Longkun South Road, 571158, Hainan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang-Xiang","family":"Wu","sequence":"additional","affiliation":[{"name":"Division of Biomedical Engineering, University of Saskatchewan, 57 Campus Drive, S7N 5A9, Saskatchewan, Canada"},{"name":"Department of Mechanical Engineering and Department of Computer Science, University of Saskatchewan, 57 Campus Drive, S7N 5A9, Saskatchewan, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,12,2]]},"reference":[{"issue":"1","key":"2022012000320178700_ref1","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1038\/nrd.2018.168","article-title":"Drug repurposing: progress, challenges and recommendations","volume":"18","author":"Pushpakom","year":"2019","journal-title":"Nat Rev Drug Discov"},{"issue":"17","key":"2022012000320178700_ref2","doi-asserted-by":"crossref","first-page":"2664","DOI":"10.1093\/bioinformatics\/btw228","article-title":"Drug repositioning based on comprehensive similarity measures and Bi-Random walk algorithm","volume":"32","author":"Luo","year":"2016","journal-title":"Bioinformatics"},{"issue":"6","key":"2022012000320178700_ref3","doi-asserted-by":"crossref","first-page":"1890","DOI":"10.1109\/TCBB.2018.2832078","article-title":"Computational drug repositioning with random walk on a heterogeneous network","volume":"16","author":"Luo","year":"2018","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2022012000320178700_ref4","first-page":"1","article-title":"Predicting drug-disease associations via using gaussian interaction profile and kernel-based autoencoder","volume":"2019","author":"Jiang","year":"2019","journal-title":"Biomed Res Int"},{"key":"2022012000320178700_ref5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fchem.2019.00924","article-title":"Identification of drug-disease associations using information of molecular structures and clinical symptoms via deep convolutional neural network","volume":"7","author":"Li","year":"2020","journal-title":"Front Chem"},{"issue":"4","key":"2022012000320178700_ref6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/bib\/bbaa243","article-title":"Predicting drug-disease associations through layer attention graph convolutional network","volume":"22","author":"Yu","year":"2021","journal-title":"Brief Bioinform"},{"key":"2022012000320178700_ref7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TCBB.2020.3019781","article-title":"Identifying gene signatures for cancer drug repositioning based on sample clustering","author":"Wang","year":"2020","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"issue":"1","key":"2022012000320178700_ref8","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":"4","key":"2022012000320178700_ref9","doi-asserted-by":"crossref","first-page":"1401","DOI":"10.1021\/acs.jproteome.6b00618","article-title":"Deep-learning-based drug-target interaction prediction","volume":"16","author":"Wen","year":"2017","journal-title":"J Proteome Res"},{"key":"2022012000320178700_ref10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fphar.2019.01592","article-title":"A novel approach for drug-target interactions prediction based on multimodal deep autoencoder","volume":"10","author":"Wang","year":"2020","journal-title":"Front Pharmacol"},{"issue":"25","key":"2022012000320178700_ref11","first-page":"1","article-title":"Predicting drug-target interactions from drug structure and protein sequence using novel convolutional neural networks","volume":"20","author":"Hu","year":"2019","journal-title":"BMC Bioinformatics"},{"key":"2022012000320178700_ref12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TCBB.2020.2977335","article-title":"Drug-target interaction prediction: end-to-end deep learning approach","author":"Monteiro","year":"2020","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"issue":"35","key":"2022012000320178700_ref13","doi-asserted-by":"crossref","first-page":"20701","DOI":"10.1039\/D0RA02297G","article-title":"Drug-target affinity prediction using graph neural network and contact maps","volume":"10","author":"Jiang","year":"2020","journal-title":"RSC Adv"},{"key":"2022012000320178700_ref14","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.jbi.2017.04.021","article-title":"Computational prediction of drug-drug interactions based on drugs functional similarities","volume":"70","author":"Ferdousi","year":"2017","journal-title":"J Biomed Inform"},{"issue":"15","key":"2022012000320178700_ref15","first-page":"1","article-title":"DDIGIP: predicting drug-drug interactions based on Gaussian interaction profile kernels","volume":"20","author":"Yan","year":"2019","journal-title":"BMC Bioinformatics"},{"issue":"19","key":"2022012000320178700_ref16","first-page":"1","article-title":"DDI-PULearn: a positive-unlabeled learning method for large-scale prediction of drug-drug interactions","volume":"20","author":"Zheng","year":"2019","journal-title":"BMC Bioinformatics"},{"issue":"2","key":"2022012000320178700_ref17","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1111\/cbdd.12378","article-title":"Drug repurposing based on drug-drug interaction","volume":"85","author":"Zhou","year":"2015","journal-title":"Chem Biol Drug Des"},{"issue":"1","key":"2022012000320178700_ref18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-016-1415-9","article-title":"Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data","volume":"18","author":"Zhang","year":"2017","journal-title":"BMC Bioinformatics"},{"issue":"1","key":"2022012000320178700_ref19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-020-03724-x","article-title":"DPDDI: a deep predictor for drug-drug interactions","volume":"21","author":"Feng","year":"2020","journal-title":"BMC Bioinformatics"},{"issue":"1","key":"2022012000320178700_ref20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-77766-9","article-title":"SkipGNN: predicting molecular interactions with skip-graph networks","volume":"10","author":"Huang","year":"2020","journal-title":"Sci Rep"},{"key":"2022012000320178700_ref21","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.ymeth.2020.05.007","article-title":"Predicting drug-drug interactions using multi-modal deep auto-encoders based network embedding and positive-unlabeled learning","volume":"179","author":"Zhang","year":"2020","journal-title":"Methods"},{"key":"2022012000320178700_ref22","first-page":"1","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2016"},{"issue":"D1","key":"2022012000320178700_ref23","doi-asserted-by":"publisher","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":"2022012000320178700_ref24","doi-asserted-by":"publisher","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":"2022012000320178700_ref25","doi-asserted-by":"publisher","first-page":"D545","DOI":"10.1093\/nar\/gkaa970","article-title":"KEGG: integrating viruses and cellular organisms","volume":"49","author":"Kanehisa","year":"2021","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"2022012000320178700_ref26","doi-asserted-by":"publisher","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"},{"key":"2022012000320178700_ref27","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1145\/2939672.2939754","volume-title":"Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining","author":"Grover","year":"2016"},{"issue":"6","key":"2022012000320178700_ref28","doi-asserted-by":"publisher","first-page":"1437","DOI":"10.1016\/j.cell.2017.10.049","article-title":"A next generation connectivity map: L1000 platform and the first 1,000,000 profiles","volume":"171","author":"Subramanian","year":"2017","journal-title":"Cell"},{"issue":"33","key":"2022012000320178700_ref29","doi-asserted-by":"crossref","first-page":"14621","DOI":"10.1073\/pnas.1000138107","article-title":"Discovery of drug mode of action and drug repositioning from transcriptional responses","volume":"107","author":"Iorio","year":"2010","journal-title":"Proc Natl Acad Sci"},{"issue":"125","key":"2022012000320178700_ref30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1126\/scitranslmed.3003377","article-title":"Data-driven prediction of drug effects and interactions","volume":"4","author":"Tatonetti","year":"2012","journal-title":"Sci Transl Med"},{"key":"2022012000320178700_ref31","article-title":"Drug Interactions Checker","author":"Drugs.com","year":"13, 2021"},{"issue":"11","key":"2022012000320178700_ref32","first-page":"1","article-title":"Visualizing data using t-SNE","volume":"9","author":"Laurens","year":"2008","journal-title":"J Mach Learn Res"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/1\/bbab511\/42231538\/bbab511.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/1\/bbab511\/42231538\/bbab511.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,20]],"date-time":"2022-01-20T00:47:34Z","timestamp":1642639654000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbab511\/6447677"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,2]]},"references-count":32,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1,17]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbab511","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,1]]},"published":{"date-parts":[[2021,12,2]]},"article-number":"bbab511"}}