{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T18:43:18Z","timestamp":1780598598456,"version":"3.54.1"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"15","license":[{"start":{"date-parts":[[2020,5,14]],"date-time":"2020-05-14T00:00:00Z","timestamp":1589414400000},"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":["61772381"],"award-info":[{"award-number":["61772381"]}],"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":["61572368"],"award-info":[{"award-number":["61572368"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"publisher","award":["2018YFC0407904"],"award-info":[{"award-number":["2018YFC0407904"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Huazhong Agricultural University Scientific & Technological Self-innovation Foundation"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Drug\u2013drug interactions (DDIs) are one of the major concerns in pharmaceutical research. Many machine learning based methods have been proposed for the DDI prediction, but most of them predict whether two drugs interact or not. The studies revealed that DDIs could cause different subsequent events, and predicting DDI-associated events is more useful for investigating the mechanism hidden behind the combined drug usage or adverse reactions.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this article, we collect DDIs from DrugBank database, and extract 65 categories of DDI events by dependency analysis and events trimming. We propose a multimodal deep learning framework named DDIMDL that combines diverse drug features with deep learning to build a model for predicting DDI-associated events. DDIMDL first constructs deep neural network (DNN)-based sub-models, respectively, using four types of drug features: chemical substructures, targets, enzymes and pathways, and then adopts a joint DNN framework to combine the sub-models to learn cross-modality representations of drug\u2013drug pairs and predict DDI events. In computational experiments, DDIMDL produces high-accuracy performances and has high efficiency. Moreover, DDIMDL outperforms state-of-the-art DDI event prediction methods and baseline methods. Among all the features of drugs, the chemical substructures seem to be the most informative. With the combination of substructures, targets and enzymes, DDIMDL achieves an accuracy of 0.8852 and an area under the precision\u2013recall curve of 0.9208.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code and data are available at https:\/\/github.com\/YifanDengWHU\/DDIMDL.<\/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\/btaa501","type":"journal-article","created":{"date-parts":[[2020,5,7]],"date-time":"2020-05-07T11:12:37Z","timestamp":1588849957000},"page":"4316-4322","source":"Crossref","is-referenced-by-count":395,"title":["A multimodal deep learning framework for predicting drug\u2013drug interaction events"],"prefix":"10.1093","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0077-9718","authenticated-orcid":false,"given":"Yifan","family":"Deng","sequence":"first","affiliation":[{"name":"College of Informatics, Huazhong Agricultural University , Wuhan 430070, China"},{"name":"Electronic Information School, Wuhan University , Wuhan 430072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinran","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Informatics, Huazhong Agricultural University , Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Qiu","sequence":"additional","affiliation":[{"name":"College of Informatics, Huazhong Agricultural University , Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7285-588X","authenticated-orcid":false,"given":"Jingbo","family":"Xia","sequence":"additional","affiliation":[{"name":"College of Informatics, Huazhong Agricultural University , Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7329-9670","authenticated-orcid":false,"given":"Wen","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Informatics, Huazhong Agricultural University , Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shichao","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Informatics, Huazhong Agricultural University , Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2020,5,14]]},"reference":[{"key":"2023062312041342900_btaa501-B2","doi-asserted-by":"crossref","first-page":"e278","DOI":"10.1136\/amiajnl-2013-002512","article-title":"Machine learning-based prediction of drug\u2013drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties","volume":"21","author":"Cheng","year":"2014","journal-title":"J. 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