{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T05:57:34Z","timestamp":1773381454116,"version":"3.50.1"},"reference-count":28,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T00:00:00Z","timestamp":1728432000000},"content-version":"vor","delay-in-days":8,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62473149"],"award-info":[{"award-number":["62473149"]}],"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":["61962050"],"award-info":[{"award-number":["61962050"]}],"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":["62072473"],"award-info":[{"award-number":["62072473"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004761","name":"Natural Science Foundation of Hunan Province of China","doi-asserted-by":"crossref","award":["2022JJ30428"],"award-info":[{"award-number":["2022JJ30428"]}],"id":[{"id":"10.13039\/501100004761","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Excellent youth funding of Hunan Provincial Education Department","award":["22B0372"],"award-info":[{"award-number":["22B0372"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Drug\u2013drug interactions (DDIs) can cause unexpected adverse drug reactions, affecting treatment efficacy and patient safety. The need for computational methods to predict DDIs has been growing due to the necessity of identifying potential risks associated with drug combinations in advance. Although several deep learning methods have been recently proposed to predict DDIs, many overlook feature learning based on interactions between the substructures of drug pairs.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this work, we introduce a molecular Substructure-based Dual Attention Feature Learning framework (MSDAFL), designed to fully utilize the information between substructures of drug pairs to enhance the performance of DDI prediction. We employ a self-attention module to obtain a set number of self-attention vectors, which are associated with various substructural patterns of the drug molecule itself, while also extracting interaction vectors representing inter-substructure interactions between drugs through an interactive attention module. Subsequently, an interaction module based on cosine similarity is used to further capture the interactive characteristics between the self-attention vectors of drug pairs. We also perform normalization after the interaction feature extraction to mitigate overfitting. After applying three-fold cross-validation, the MSDAFL model achieved average precision scores of 0.9707, 0.9991, and 0.9987, and area under the receiver operating characteristic curve scores of 0.9874, 0.9934, and 0.9974 on three datasets, respectively. In addition, the experiment results of five-fold cross-validation and cross-datum study also indicate that MSDAFL performs well in predicting DDIs.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Data and source codes are available at https:\/\/github.com\/27167199\/MSDAFL.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae596","type":"journal-article","created":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T20:33:54Z","timestamp":1728506034000},"source":"Crossref","is-referenced-by-count":4,"title":["MSDAFL: molecular substructure-based dual attention feature learning framework for predicting drug\u2013drug interactions"],"prefix":"10.1093","volume":"40","author":[{"given":"Chao","family":"Hou","sequence":"first","affiliation":[{"name":"School of Informatics, Hunan University of Chinese Medicine , Changsha, Hunan 410208,","place":["China"]}]},{"given":"Guihua","family":"Duan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , Changsha, Hunan 410083,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5021-6841","authenticated-orcid":false,"given":"Cheng","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Informatics, Hunan University of Chinese Medicine , Changsha, Hunan 410208,","place":["China"]}]}],"member":"286","published-online":{"date-parts":[[2024,10,9]]},"reference":[{"key":"2024101722010785000_btae596-B1","doi-asserted-by":"publisher","DOI":"10.1101\/2020.11.09.375626","article-title":"Deepdrug: a general graph-based deep learning framework for drug relation prediction","author":"Cao","year":"2020","journal-title":"bioRxiv"},{"key":"2024101722010785000_btae596-B2","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.ymeth.2020.05.014","article-title":"GCN-BMP: investigating 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