SynSimPred: Leveraging Cell Line Similarities for Drug Combination Synergy Prediction

SynSimPred: Leveraging Cell Line Similarities for Drug Combination Synergy Prediction | 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 SynSimPred: Leveraging Cell Line Similarities for Drug Combination Synergy Prediction Soodabeh Zakeri, Fatemeh Yassaee Meybodi, Changiz Eslahchi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4589706/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 Cancer treatment faces significant challenges, necessitating innovative approaches to predict and enhance drug efficacy. Computational models, particularly advanced machine learning methods, have shown promise in customizing drug response predictions and improving patient outcomes. However, predicting synergistic drug combinations remains complex due to the vast number of potential interactions and the limitations of both data availability and traditional research methodologies. In response, our study introduces SynSimPred, a computational approach that leverages cell line similarities, gene expression, mutations, dependencies, and copy number variations to predict drug synergy scores. SynSimPred employs a suite of sophisticated regression methodologies to navigate the complex interplay between these biological characteristics and their impact on drug efficacy. A key component of our research involved a detailed case study where SynSimPred predicted the synergistic and antagonistic interactions of drug pairs such as Vinblastine and Veliparib, and Deforolimus and Dactolisib, demonstrating its practical utility and accuracy in real-world scenarios. We rigorously evaluated SynSimPred against leading methods including DeepSynergy, HypergraphSynergy, and SynPred, across two comprehensive datasets, O'NEIL and ALMANAC. Our evaluations, using metrics such as MSE, Precision, F-Measure, and Accuracy, demonstrate that SynSimPred consistently outperforms existing models, establishing it as a frontrunner in the field of predictive methodologies for cancer treatment. Biological sciences/Computational biology and bioinformatics/Computational models Biological sciences/Computational biology and bioinformatics/Data integration Biological sciences/Computational biology and bioinformatics/Machine learning Drug combination Cancer Synergy Therapeutic efficacy Full Text Additional Declarations No competing interests reported. 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4589706","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":321382656,"identity":"a2ddd07a-f69f-4215-b2fa-54be1ef036a7","order_by":0,"name":"Soodabeh Zakeri","email":"","orcid":"","institution":"School of Biological Sciences, Institute for Research in Fundamental Sciences","correspondingAuthor":false,"prefix":"","firstName":"Soodabeh","middleName":"","lastName":"Zakeri","suffix":""},{"id":321382657,"identity":"f09d0eff-c316-4c01-be68-8b15aba38985","order_by":1,"name":"Fatemeh Yassaee Meybodi","email":"","orcid":"","institution":"School of Biological Sciences, Institute for Research in Fundamental Sciences","correspondingAuthor":false,"prefix":"","firstName":"Fatemeh","middleName":"Yassaee","lastName":"Meybodi","suffix":""},{"id":321382658,"identity":"25e62915-fd8a-4590-966b-8c6a92ed2c74","order_by":2,"name":"Changiz Eslahchi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIie3RsWrCQBzH8d8R0OVC1j8E7Cv8xbHgs9zh4KYtXSwdjATS0TXgSzg6Jtzgcg8Q0EEfIUtJttqUToVTN4f7Dv/h4MP/Dwf4fI8Y/YwXKJBYnUTy+1hcJ9yRlO8l6NEfcRZtEnFueDyLNmn23u4Mos9CmIVrybEIRpInb3Qss0NoDcgqlNZBmOZfMTjQW9LZQWQGqIDSdSCT6rcNLzvy2l7I0w2kB8mmIwgvhK8RqlQQS97rvNJpHNqpHFqdOEmUK1E3iw+9zqfnut09DwZ7Y2oX+ZcEbvodn8/n87n6BjNLTvwpgJZtAAAAAElFTkSuQmCC","orcid":"","institution":"Shahid Beheshti University","correspondingAuthor":true,"prefix":"","firstName":"Changiz","middleName":"","lastName":"Eslahchi","suffix":""}],"badges":[],"createdAt":"2024-06-16 12:36:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4589706/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4589706/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64435739,"identity":"a3409166-24f6-48df-9fbe-7409980d0487","added_by":"auto","created_at":"2024-09-13 07:07:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":349052,"visible":true,"origin":"","legend":"","description":"","filename":"SynSimPred.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4589706/v1_covered_8f3e297a-57ec-445d-bf6c-aa5408556e57.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"SynSimPred: Leveraging Cell Line Similarities for Drug Combination Synergy Prediction","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":"Drug combination, Cancer, Synergy, Therapeutic efficacy","lastPublishedDoi":"10.21203/rs.3.rs-4589706/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4589706/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Cancer treatment faces significant challenges, necessitating innovative approaches to predict and enhance drug efficacy. Computational models, particularly advanced machine learning methods, have shown promise in customizing drug response predictions and improving patient outcomes. However, predicting synergistic drug combinations remains complex due to the vast number of potential interactions and the limitations of both data availability and traditional research methodologies. In response, our study introduces SynSimPred, a computational approach that leverages cell line similarities, gene expression, mutations, dependencies, and copy number variations to predict drug synergy scores. SynSimPred employs a suite of sophisticated regression methodologies to navigate the complex interplay between these biological characteristics and their impact on drug efficacy. A key component of our research involved a detailed case study where SynSimPred predicted the synergistic and antagonistic interactions of drug pairs such as Vinblastine and Veliparib, and Deforolimus and Dactolisib, demonstrating its practical utility and accuracy in real-world scenarios. We rigorously evaluated SynSimPred against leading methods including DeepSynergy, HypergraphSynergy, and SynPred, across two comprehensive datasets, O'NEIL and ALMANAC. Our evaluations, using metrics such as MSE, Precision, F-Measure, and Accuracy, demonstrate that SynSimPred consistently outperforms existing models, establishing it as a frontrunner in the field of predictive methodologies for cancer treatment.","manuscriptTitle":"SynSimPred: Leveraging Cell Line Similarities for Drug Combination Synergy Prediction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-03 04:28:35","doi":"10.21203/rs.3.rs-4589706/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"d2dd3dd2-bee9-477f-8007-1bb9d3d3fdfb","owner":[],"postedDate":"July 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":33978315,"name":"Biological sciences/Computational biology and bioinformatics/Computational models"},{"id":33978316,"name":"Biological sciences/Computational biology and bioinformatics/Data integration"},{"id":33978317,"name":"Biological sciences/Computational biology and bioinformatics/Machine learning"}],"tags":[],"updatedAt":"2024-09-13T06:59:49+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-03 04:28:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4589706","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4589706","identity":"rs-4589706","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}