Combination Therapy Synergism Prediction for Virus Treatment Using Machine and Deep Learning Models

Combination Therapy Synergism Prediction for Virus Treatment Using Machine and Deep Learning Models | 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 Article Combination Therapy Synergism Prediction for Virus Treatment Using Machine and Deep Learning Models Shayan Majidifar, Arash Zabihian, Mohsen Hooshmand This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4389305/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Combining different therapies synergistically is an essential aspect of developing effective treatments. Although there is a wealth of research on computational prediction for new combination therapies, there is limited to no research on combination therapies in the treatment of viral diseases. This paper proposes AI-based models for predicting novel antiviral combinations to treat virus diseases synergistically. To do this, we assembled a comprehensive dataset comprising information on viral strains, drug compounds, and their known interactions. As far as we know, this is the first dataset and learning model on combination therapy for viruses. Our proposal includes a deep model and two machine learning models to optimize combination therapy. The machine learning models showed the highest performance among all methods, and the predicted values were validated by t-test, indicating the effectiveness of the proposed methods. One of the predicted combinations of acyclovir and ribavirin has been experimentally confirmed to have a synergistic antiviral effect against herpes simplex type-1 virus, as described in the literature. Biological sciences/Computational biology and bioinformatics Physical sciences/Mathematics and computing Biological sciences/Drug discovery Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementary.pdf Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. 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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-4389305","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":303285314,"identity":"d25144c7-8437-47a7-a52d-3080d9644e8c","order_by":0,"name":"Shayan Majidifar","email":"","orcid":"","institution":"Institute for Advanced Studies in Basic Sciences","correspondingAuthor":false,"prefix":"","firstName":"Shayan","middleName":"","lastName":"Majidifar","suffix":""},{"id":303285315,"identity":"bef37e73-5ccc-4ebd-8480-d03f995601d3","order_by":1,"name":"Arash Zabihian","email":"","orcid":"","institution":"Kimia Zist Parsian Pharmaceutical Company","correspondingAuthor":false,"prefix":"","firstName":"Arash","middleName":"","lastName":"Zabihian","suffix":""},{"id":303285316,"identity":"f2ade607-c369-43e2-b747-7ec1cbce26b8","order_by":2,"name":"Mohsen Hooshmand","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFklEQVRIiWNgGAWjYFACHjCZAMRsIAZjPzMDAzNEirGBOC0zm0nWsuEAXAt2oNvee/jDxx0Mefzs7c8e3ai5I7v5OPPjzwV/GOT5G5jbPmDRYnbmXJrkzDMMxZI9Z8yNc449M952mM1MemYbg+GMA4zNM7BpuZFjxszbxpC44UYOm3QO2+HEbYcZgCINQBcyMDZjcxhQi/Hnv2At6c+kc/4dTtzczP75M88fBns8WgykGcFaEsykc9sOJ25g5jGQ5mEDiuDScuaMmWRvm0TizJ4zQC19h41nHOYpA/pFInnGYRxajvcYf/jZZpPYDwwx6Zxvh2X7+49vBoaYjW1/e/tjXAENBBLYRPDGzigYBaNgFIwCfAAAzyNlswTOn8oAAAAASUVORK5CYII=","orcid":"","institution":"Institute for Advanced Studies in Basic Sciences","correspondingAuthor":true,"prefix":"","firstName":"Mohsen","middleName":"","lastName":"Hooshmand","suffix":""}],"badges":[],"createdAt":"2024-05-08 12:32:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4389305/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4389305/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57242039,"identity":"c04a28bf-e155-42f7-953e-65257b87af6d","added_by":"auto","created_at":"2024-05-28 04:03:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":541343,"visible":true,"origin":"","legend":"","description":"","filename":"kvtbfkhzjjygvjxwtmzfxdkhtqbdvcxk.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4389305/v1_covered_705ed5d7-c824-4ebb-b7df-53eb4d26d612.pdf"},{"id":56857340,"identity":"8b36f1e2-7a9f-42a3-89dd-8d97c97528c2","added_by":"auto","created_at":"2024-05-21 10:22:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":219783,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4389305/v1/f000da1dbe7b75cf1fc969f0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Combination Therapy Synergism Prediction for Virus Treatment Using Machine and Deep Learning Models","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4389305/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4389305/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Combining different therapies synergistically is an essential aspect of developing effective treatments. 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