multiGMF: A multi-similarity geometric matrix factorization for identifying drug-associated indications

multiGMF: A multi-similarity geometric matrix factorization for identifying drug-associated indications | 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 multiGMF: A multi-similarity geometric matrix factorization for identifying drug-associated indications Mengyun Yang, Bin Yang, Xiwei Tang, Guihua Duan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9409242/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Drug repositioning serves as a promising strategy in drug development, exploring the potential uses of existing drugs through numerical computation. Compared to experimental screening, drug repositioning proves to be more efficient and cost-effective, playing a vital role in the field of pharmaceutical development. Designing an effective approach to integrate multi-source prior information about drugs and diseases holds significance in drug repositioning, given the low coupling of latent features in existing methods for handling the associated information and multi-similarity information of drugs and diseases. In this article, we propose a novel method based on multi-similarity geometric matrix factorization (multiGMF) for identifying the potential indications of existing and new drugs. Through weighted k-nearest neighbors (WKNN) algorithm and soft regularization technique, it couples the multi-similarity features of drugs and diseases with associated features. Moreover, it explores their latent feature information in high-dimensional space using graph regularization technique aimed at inferring potential drug-disease associations. To evaluate the performance of multiGMF, we contrast it with five most advanced drug repositioning approaches in both $10$-fold cross-validation and cold-start tests. The numerical outcomes demonstrate that multiGMF exhibits outstanding predictive performance. Furthermore, case studies further support the viability of our method in practical applications. The multiGMF code is freely available at https://github.com/YangPhD84/multiGMF . Biological sciences/Computational biology and bioinformatics Biological sciences/Drug discovery Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 16 May, 2026 Reviews received at journal 07 May, 2026 Reviewers agreed at journal 04 May, 2026 Reviewers agreed at journal 29 Apr, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviewers invited by journal 21 Apr, 2026 Editor invited by journal 20 Apr, 2026 Editor assigned by journal 16 Apr, 2026 Submission checks completed at journal 16 Apr, 2026 First submitted to journal 13 Apr, 2026 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-9409242","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":628288377,"identity":"1767bf75-8030-476f-9605-5a43c1a79dd4","order_by":0,"name":"Mengyun Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYNCDDwY2cqTpYJxRkGZMmhZmng+HEwmq0m3vPfyat+0Og8Hxs4df2xgwJzCwHz66AZ8WszPn0qx5254xGJzJS7POMWDLY+BJS7uBV8uNHDNj3rbDDAYHgIwcA55iBgkeM/xa7r+BajkPZFgYSCQ2ENRyg8f4MVjLjRzjxwwGBkRoOZNjxjjn3GEGyRtvzBh7DBKM2Qj65fgZ4w9vyg4z8J3PMf7w489/OX72w8fwagECNikeBob6BQcY2CTAXALKQYD54w8gKd/AwPyBCNWjYBSMglEwAgEAd3FM5TzsMBEAAAAASUVORK5CYII=","orcid":"","institution":"Hunan First Normal University","correspondingAuthor":true,"prefix":"","firstName":"Mengyun","middleName":"","lastName":"Yang","suffix":""},{"id":628288378,"identity":"5a9ce0b9-009b-4b37-ae2a-a17e311aa66d","order_by":1,"name":"Bin Yang","email":"","orcid":"","institution":"Guangdong University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Yang","suffix":""},{"id":628288379,"identity":"a3ff31a6-c5f6-4a12-8133-f7b2ed72ec96","order_by":2,"name":"Xiwei Tang","email":"","orcid":"","institution":"Hunan First Normal University","correspondingAuthor":false,"prefix":"","firstName":"Xiwei","middleName":"","lastName":"Tang","suffix":""},{"id":628288380,"identity":"e9b412ac-059e-4c93-85d5-c10c85af4a02","order_by":3,"name":"Guihua Duan","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Guihua","middleName":"","lastName":"Duan","suffix":""}],"badges":[],"createdAt":"2026-04-14 01:39:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9409242/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9409242/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107638322,"identity":"154f62b6-e1ef-471f-b7cf-d20e5918f1da","added_by":"auto","created_at":"2026-04-23 12:56:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":429436,"visible":true,"origin":"","legend":"","description":"","filename":"MulitGMFScientificReportslatextexpage.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9409242/v1_covered_bcf50d5f-7f90-4194-a380-2fc70a1dc8e2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"multiGMF: A multi-similarity geometric matrix factorization for identifying drug-associated indications","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9409242/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9409242/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Drug repositioning serves as a promising strategy in drug development, exploring the potential uses of existing drugs through numerical computation. Compared to experimental screening, drug repositioning proves to be more efficient and cost-effective, playing a vital role in the field of pharmaceutical development. Designing an effective approach to integrate multi-source prior information about drugs and diseases holds significance in drug repositioning, given the low coupling of latent features in existing methods for handling the associated information and multi-similarity information of drugs and diseases. In this article, we propose a novel method based on multi-similarity geometric matrix factorization (multiGMF) for identifying the potential indications of existing and new drugs. Through weighted k-nearest neighbors (WKNN) algorithm and soft regularization technique, it couples the multi-similarity features of drugs and diseases with associated features. Moreover, it explores their latent feature information in high-dimensional space using graph regularization technique aimed at inferring potential drug-disease associations. To evaluate the performance of multiGMF, we contrast it with five most advanced drug repositioning approaches in both $10$-fold cross-validation and cold-start tests. The numerical outcomes demonstrate that multiGMF exhibits outstanding predictive performance. Furthermore, case studies further support the viability of our method in practical applications. 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