Research on Drug-Drug Interaction Prediction Using Capsule Neural Network Based on Self-Attention Mechanism

Research on Drug-Drug Interaction Prediction Using Capsule Neural Network Based on Self-Attention Mechanism | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Research on Drug-Drug Interaction Prediction Using Capsule Neural Network Based on Self-Attention Mechanism Xing-xin Chen, Zhen Miao, Bin Nie This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5006876/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Dec, 2025 Read the published version in BMC Bioinformatics → Version 1 posted 11 You are reading this latest preprint version Abstract Multi-drug combinations are an effective strategy for the teatment of complex diseases. Due to the numerous unknown interactions between drugs, accurate prediction of drug-drug interactions (DDIs) is essential to avoid adverse drug reactions that can cause significant harm to patients. Therefore, DDI prediction is crucial in pharmacology.Methods: In this paper, we propose a multi-source feature fusion DDI prediction method based on the self-attention mechanism of a capsule neural network (ACaps-DDI). This method effectively integrates the chemical information of a drug's internal substructure, as well as the bioinformation of the drug's external targets and enzymes, to predict drug-drug interactions.Results: Comparison experiments on two benchmark datasets show that the six classification metrics of the ACaps-DDI model outperform those of the other seven comparison models, demonstrating the superior performance and generalization ability of the ACaps-DDI model. Ablation studies further validate the effectiveness of certain ACaps-DDI modules. Finally, case validation with three drugs—cannabidiol, torasemide, and dexamethasone—demonstrates the model's effectiveness in predicting unknown drug interactions. Conclusion: The ACaps-DDI model has demonstrated a good predictive effect on known drugs and some predictive ability on unseen drugs, which is of great practical significance for clinical drug interaction studies. drug-drug interactions self-attention mechanism capsule neural network Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Dec, 2025 Read the published version in BMC Bioinformatics → Version 1 posted Editorial decision: Revision requested 11 Nov, 2024 Reviews received at journal 29 Oct, 2024 Reviewers agreed at journal 27 Oct, 2024 Reviews received at journal 09 Oct, 2024 Reviewers agreed at journal 27 Sep, 2024 Reviewers agreed at journal 26 Sep, 2024 Reviewers invited by journal 26 Sep, 2024 Editor invited by journal 06 Sep, 2024 Editor assigned by journal 04 Sep, 2024 Submission checks completed at journal 04 Sep, 2024 First submitted to journal 31 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5006876","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":360396919,"identity":"b678fcd3-9047-45a8-be01-e3ec50626d86","order_by":0,"name":"Xing-xin Chen","email":"","orcid":"","institution":"Jiangxi University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xing-xin","middleName":"","lastName":"Chen","suffix":""},{"id":360396920,"identity":"0022b2f4-e6fe-492a-91d1-03a40403be0a","order_by":1,"name":"Zhen Miao","email":"","orcid":"","institution":"Jiangxi University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Miao","suffix":""},{"id":360396923,"identity":"2df7b444-0c26-4e0b-9a05-93ae03d49b99","order_by":2,"name":"Bin Nie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAk0lEQVRIiWNgGAWjYJACZoYKCTl5ErWcsTA2bCBJC2NbRSLDAWKVm7OfMXtcOE8igbGB+eGjG8RosezJMTeeuU0ij52Bzdg4hxgtBjd4zKR5t0kUMzbwsEmToGWORGLDAdK0NJCk5UxamfSMYxLGhs1E++X44W3SBTV1cvLszQ8fE6UFAZhJUz4KRsEoGAWjAB8AANGhKH+hhn+iAAAAAElFTkSuQmCC","orcid":"","institution":"Jiangxi University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Bin","middleName":"","lastName":"Nie","suffix":""}],"badges":[],"createdAt":"2024-08-31 04:23:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5006876/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5006876/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12859-025-06308-9","type":"published","date":"2025-12-23T15:58:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":99172854,"identity":"ef0ddd03-ec10-4e97-8cbd-25e090853255","added_by":"auto","created_at":"2025-12-29 16:11:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":649396,"visible":true,"origin":"","legend":"","description":"","filename":"ResearchonDrugDrugInteractionPredictionUsingCapsuleNeuralNetworkBasedonSelfAttentionMechanism.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5006876/v1_covered_08eb97e8-721f-4d4e-8ba8-b935e85cabb5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on Drug-Drug Interaction Prediction Using Capsule Neural Network Based on Self-Attention Mechanism","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-bioinformatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"binf","sideBox":"Learn more about [BMC Bioinformatics](http://bmcbioinformatics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/binf","title":"BMC Bioinformatics","twitterHandle":"@BMC_Bioinformatics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"drug-drug interactions, self-attention mechanism, capsule neural network","lastPublishedDoi":"10.21203/rs.3.rs-5006876/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5006876/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMulti-drug combinations are an effective strategy for the teatment of complex diseases. 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