{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T01:50:51Z","timestamp":1785894651820,"version":"3.56.0"},"reference-count":42,"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"}],"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\u2013drug interaction (DDI) prediction identifies interactions of drug combinations in which the adverse side effects caused by the physicochemical incompatibility have attracted much attention. Previous studies usually model drug information from single or dual views of the whole drug molecules but ignore the detailed interactions among atoms, which leads to incomplete and noisy information and limits the accuracy of DDI prediction. In this work, we propose a novel dual-view drug representation learning network for DDI prediction (\u2018DSN-DDI\u2019), which employs local and global representation learning modules iteratively and learns drug substructures from the single drug (\u2018intra-view\u2019) and the drug pair (\u2018inter-view\u2019) simultaneously. Comprehensive evaluations demonstrate that DSN-DDI significantly improved performance on DDI prediction for the existing drugs by achieving a relatively improved accuracy of 13.01% and an over 99% accuracy under the transductive setting. More importantly, DSN-DDI achieves a relatively improved accuracy of 7.07% to unseen drugs and shows the usefulness for real-world DDI applications. Finally, DSN-DDI exhibits good transferability on synergistic drug combination prediction and thus can serve as a generalized framework in the drug discovery field.<\/jats:p>","DOI":"10.1093\/bib\/bbac597","type":"journal-article","created":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T14:37:29Z","timestamp":1672670249000},"source":"Crossref","is-referenced-by-count":154,"title":["DSN-DDI: an accurate and generalized framework for drug\u2013drug interaction prediction by dual-view representation learning"],"prefix":"10.1093","volume":"24","author":[{"given":"Zimeng","family":"Li","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering, Hunan University , Changsha 410086 , China"},{"name":"Microsoft Research AI4Science , Beijing 10080 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shichao","family":"Zhu","sequence":"additional","affiliation":[{"name":"Microsoft Research AI4Science , Beijing 10080 , China"},{"name":"School of Cyber Security, University of Chinese Academy of Sciences , Beijing 100049 , China"},{"name":"Institute of Information Engineering, Chinese Academy of Sciences , Beijing 100093 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Shao","sequence":"additional","affiliation":[{"name":"Microsoft Research AI4Science , Beijing 10080 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangxiang","family":"Zeng","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Hunan University , Changsha 410086 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9483-0050","authenticated-orcid":false,"given":"Tong","family":"Wang","sequence":"additional","affiliation":[{"name":"Microsoft Research AI4Science , Beijing 10080 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tie-Yan","family":"Liu","sequence":"additional","affiliation":[{"name":"Microsoft Research AI4Science , Beijing 10080 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,1,2]]},"reference":[{"key":"2023011917142087900_ref1","doi-asserted-by":"crossref","first-page":"125ra131","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":"2023011917142087900_ref2","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1038\/nrd2683","article-title":"Mechanisms of drug combinations: interaction and network perspectives","volume":"8","author":"Jia","year":"2009","journal-title":"Nat Rev Drug Discov"},{"key":"2023011917142087900_ref3","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1038\/nbt.3834","article-title":"Synergistic drug combinations for cancer identified in a CRISPR screen for pairwise genetic interactions","volume":"35","author":"Han","year":"2017","journal-title":"Nat Biotechnol"},{"key":"2023011917142087900_ref4","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1186\/s12918-018-0532-7","article-title":"Predicting and understanding comprehensive drug-drug interactions via semi-nonnegative matrix factorization","volume":"12","author":"Yu","year":"2018","journal-title":"BMC Syst Biol"},{"key":"2023011917142087900_ref5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2019\/9187204","article-title":"Detecting drug communities and predicting comprehensive drug\u2013drug interactions via balance regularized semi-nonnegative matrix factorization","volume":"11","author":"Shi","year":"2019","journal-title":"J Chem"},{"key":"2023011917142087900_ref6","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.artmed.2017.05.008","article-title":"Prediction of synergistic anti-cancer drug combinations based on drug target network and drug induced gene expression profiles","volume":"83","author":"Li","year":"2017","journal-title":"Artif Intell Med"},{"key":"2023011917142087900_ref7","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"},{"key":"2023011917142087900_ref8","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0196865","article-title":"Predicting potential drug-drug interactions on topological and semantic similarity features using statistical learning","volume":"13","author":"Kastrin","year":"2018","journal-title":"PLoS One"},{"key":"2023011917142087900_ref9","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Huang","year":"2020"},{"key":"2023011917142087900_ref10","doi-asserted-by":"crossref","first-page":"4316","DOI":"10.1093\/bioinformatics\/btaa501","article-title":"A multimodal deep learning framework for predicting drug\u2013drug interaction events","volume":"36","author":"Deng","year":"2020","journal-title":"Bioinformatics"},{"key":"2023011917142087900_ref11","doi-asserted-by":"crossref","first-page":"2651","DOI":"10.1093\/bioinformatics\/btab169","article-title":"MUFFIN: multi-scale feature fusion for drug\u2013drug interaction prediction","volume":"37","author":"Chen","year":"2021","journal-title":"Bioinformatics"},{"key":"2023011917142087900_ref12","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":"2023011917142087900_ref13","doi-asserted-by":"crossref","DOI":"10.24963\/ijcai.2019\/551","article-title":"Mr-gnn: multi-resolution and dual graph neural network for predicting structured entity interactions","author":"Xu","year":"2019"},{"key":"2023011917142087900_ref14","doi-asserted-by":"crossref","first-page":"2988","DOI":"10.1093\/bioinformatics\/btab207","article-title":"SumGNN: multi-typed drug interaction prediction via efficient knowledge graph summarization","volume":"37","author":"Yu","year":"2021","journal-title":"Bioinformatics"},{"key":"2023011917142087900_ref15","article-title":"Relation matters in sampling: a scalable multi-relational graph neural network for drug-drug interaction prediction","author":"Feeney","year":"2021"},{"key":"2023011917142087900_ref16","author":"Harrold","year":"2014"},{"key":"2023011917142087900_ref17","volume-title":"Proceedings of the AAAI conference on artificial intelligence","author":"Jin","year":"2017"},{"key":"2023011917142087900_ref18","doi-asserted-by":"crossref","first-page":"bbab133","DOI":"10.1093\/bib\/bbab133","article-title":"SSI\u2013DDI: substructure\u2013substructure interactions for drug\u2013drug interaction prediction","volume":"22","author":"Nyamabo","year":"2021","journal-title":"Brief Bioinform"},{"key":"2023011917142087900_ref19","doi-asserted-by":"crossref","first-page":"bbab441","DOI":"10.1093\/bib\/bbab441","article-title":"Drug\u2013drug interaction prediction with learnable size-adaptive molecular substructures","volume":"23","author":"Nyamabo","year":"2022","journal-title":"Brief Bioinform"},{"key":"2023011917142087900_ref20","doi-asserted-by":"crossref","first-page":"8693","DOI":"10.1039\/D2SC02023H","article-title":"Learning size-adaptive molecular substructures for explainable drug\u2013drug interaction prediction by substructure-aware graph neural network","volume":"13","author":"Yang","year":"2022","journal-title":"Chem Sci"},{"key":"2023011917142087900_ref21","article-title":"Drug-drug adverse effect prediction with graph co-attention","author":"Deac","year":"2019"},{"key":"2023011917142087900_ref22","volume-title":"Proceedings of the Web Conference","author":"Wang","year":"2021"},{"key":"2023011917142087900_ref23","doi-asserted-by":"crossref","first-page":"i457","DOI":"10.1093\/bioinformatics\/bty294","article-title":"Modeling polypharmacy side effects with graph convolutional networks","volume":"34","author":"Zitnik","year":"2018","journal-title":"Bioinformatics"},{"key":"2023011917142087900_ref24","doi-asserted-by":"crossref","DOI":"10.24963\/ijcai.2018\/483","article-title":"Drug similarity integration through attentive multi-view graph auto-encoders","author":"Ma","year":"2018"},{"key":"2023011917142087900_ref25","article-title":"Graph attention networks","author":"Veli\u010dkovi\u0107","year":"2017"},{"key":"2023011917142087900_ref26","first-page":"4071","article-title":"Hierarchical question-image co-attention for visual question answering","volume":"29","author":"Lu","year":"2016","journal-title":"Adv Neural Inf Process Syst"},{"key":"2023011917142087900_ref27","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"},{"key":"2023011917142087900_ref28","article-title":"Adam: a method for stochastic optimization","author":"Kingma","year":"2014"},{"key":"2023011917142087900_ref29","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1021\/ci00057a005","article-title":"SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules","volume":"28","author":"Weininger","year":"1988","journal-title":"J Chem Inf Comput Sci"},{"key":"2023011917142087900_ref30","article-title":"RDKit: a software suite for cheminformatics, computational chemistry, and predictive modeling","author":"Landrum","year":"2013","journal-title":"Greg Landrum"},{"key":"2023011917142087900_ref31","doi-asserted-by":"crossref","first-page":"429","DOI":"10.3390\/ph14050429","article-title":"Cold-start problems in data-driven prediction of drug\u2013drug interaction effects","volume":"14","author":"Dewulf","year":"2021","journal-title":"Pharmaceuticals"},{"key":"2023011917142087900_ref32","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1038\/d41573-021-00002-0","article-title":"2020 FDA drug approvals","volume":"20","author":"Mullard","year":"2021","journal-title":"Nat Rev Drug Discov"},{"key":"2023011917142087900_ref33","doi-asserted-by":"crossref","first-page":"1889","DOI":"10.1016\/S0140-6736(14)60614-0","article-title":"Efficacy and safety of nebivolol and valsartan as fixed-dose combination in hypertension: a randomised, multicentre study","volume":"383","author":"Giles","year":"2014","journal-title":"The Lancet"},{"key":"2023011917142087900_ref34","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1093\/jamia\/ocaa212","article-title":"Anticancer drug synergy prediction in understudied tissues using transfer learning","volume":"28","author":"Kim","year":"2021","journal-title":"J Am Med Inform Assoc"},{"key":"2023011917142087900_ref35","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.cmpb.2013.04.018","article-title":"The application of support vector regression for prediction of the antiallodynic effect of drug combinations in the mouse model of streptozocin-induced diabetic neuropathy","volume":"111","author":"Sa\u0142at","year":"2013","journal-title":"Comput Methods Programs Biomed"},{"key":"2023011917142087900_ref36","first-page":"D871","article-title":"DrugCombDB: a comprehensive database of drug combinations toward the discovery of combinatorial therapy","volume":"48","author":"Liu","year":"2020","journal-title":"Nucleic Acids Res"},{"key":"2023011917142087900_ref37","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1038\/nature11003","article-title":"The cancer cell line Encyclopedia enables predictive modelling of anticancer drug sensitivity","volume":"483","author":"Barretina","year":"2012","journal-title":"Nature"},{"key":"2023011917142087900_ref38","doi-asserted-by":"crossref","first-page":"1538","DOI":"10.1093\/bioinformatics\/btx806","article-title":"DeepSynergy: predicting anti-cancer drug synergy with deep learning","volume":"34","author":"Preuer","year":"2018","journal-title":"Bioinformatics"},{"key":"2023011917142087900_ref39","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1008653","article-title":"TranSynergy: mechanism-driven interpretable deep neural network for the synergistic prediction and pathway deconvolution of drug combinations","volume":"17","author":"Liu","year":"2021","journal-title":"PLoS Comput Biol"},{"key":"2023011917142087900_ref40","doi-asserted-by":"crossref","first-page":"bbab390","DOI":"10.1093\/bib\/bbab390","article-title":"DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations","volume":"23","author":"Wang","year":"2022","journal-title":"Brief Bioinform"},{"key":"2023011917142087900_ref41","volume-title":"2019 IEEE International Conference on Big Data (Big Data)","author":"Chen","year":"2019"},{"key":"2023011917142087900_ref42","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1111\/j.2042-7158.1975.tb09393.x","article-title":"Mechanism of induction of hepatic microsomal drug metabolizing enzymes by a series of barbiturates","volume":"27","author":"Ioannides","year":"1975","journal-title":"J Pharm Pharmacol"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/1\/bbac597\/48782745\/bbac597.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/1\/bbac597\/48782745\/bbac597.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T17:45:24Z","timestamp":1674150324000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbac597\/6966537"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1]]},"references-count":42,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,1,19]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbac597","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":"bbac597"}}