Enhancing Molecular Property Prediction with Gaussian-Enhanced Graph Matching

Enhancing Molecular Property Prediction with Gaussian-Enhanced Graph Matching | 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 Enhancing Molecular Property Prediction with Gaussian-Enhanced Graph Matching Li xiaonan, Li Guanyu, Ning Bo, zhou Xin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4137400/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 In recent years, graph neural network technology has revolutionized molecular graph matching methods, offering new opportunities for drug discovery. Central to this research are two key steps: latent representation learning of molecules and molecular graph matching. However, the challenge lies in effectively capturing and expressing the intricate features of molecular graphs, given their strict chemical rules and high structural complexity. To address this, we propose a gaussian-enhanced graph matching method. This approach combines a dual-channel message-passing neural network, based in the GIN algorithm, to encode both nodes and edges of molecules, enabling precise representations of molecular graphs. Additionally, an end-to-end graph similarity calculation model is introduced, assessing similarity at both the node and global levels. By integrating these evaluations, we obtain a comprehensive similarity score for molecular graph pairs. This method enhances the accuracy and interpretability of molecular structural similarity. Experimental results demonstrate the superior performance of our model compared to state-of-the-art baseline methods, marking a significant step forward in molecular property prediction. Drug discovery Molecular property prediction Molecular graph matching Gaussian embedding Graph edit distance 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. 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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-4137400","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":282197085,"identity":"c66ea9c8-4cdb-498e-a4d7-1cde38cedbaf","order_by":0,"name":"Li xiaonan","email":"","orcid":"","institution":"Dalian Maritime University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"xiaonan","suffix":""},{"id":282197086,"identity":"592c59e9-6a4f-4a17-a576-4836ba28ddce","order_by":1,"name":"Li Guanyu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYBACPmYog58huQFEMzYQ0sIG0yLZkEisFhjD4ADRWtjZr0n83HE4cfPxxNbNPAw2shsOMD97gN9hPGWSvWcOJ24787DtNg9DmvGGA2zmBgS0pEnwtgG13EgEaTmcuOEAD5sEIS2Sf4FaNs8Aa/lPjBb2Y9IgWzZIgLUcIMoWZmvZtnTjGUC/3JxjkGw88zCbGV4t/PzHH95822Yt29+efOzGmwo72b7jzc/wamFg4AEFTzOUA2Iz41EMAewPgEQdQWWjYBSMglEwggEAewdKLoRBhZIAAAAASUVORK5CYII=","orcid":"","institution":"Dalian Maritime University","correspondingAuthor":true,"prefix":"","firstName":"Li","middleName":"","lastName":"Guanyu","suffix":""},{"id":282197087,"identity":"ea0cce3e-5044-411c-a74f-fa4a87394e77","order_by":2,"name":"Ning Bo","email":"","orcid":"","institution":"Dalian Maritime University","correspondingAuthor":false,"prefix":"","firstName":"Ning","middleName":"","lastName":"Bo","suffix":""},{"id":282197088,"identity":"67567617-e8af-4026-883e-519173a34447","order_by":3,"name":"zhou Xin","email":"","orcid":"","institution":"Dalian Maritime University","correspondingAuthor":false,"prefix":"","firstName":"zhou","middleName":"","lastName":"Xin","suffix":""}],"badges":[],"createdAt":"2024-03-20 13:12:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4137400/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4137400/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57313853,"identity":"937e6e37-fcf0-4f87-9a2d-919c2fa11339","added_by":"auto","created_at":"2024-05-29 03:51:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":676572,"visible":true,"origin":"","legend":"","description":"","filename":"GEMol.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4137400/v1_covered_2baa6c83-c2f9-473f-b21d-a10a042fa11f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhancing Molecular Property Prediction with Gaussian-Enhanced Graph Matching","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 discovery, Molecular property prediction, Molecular graph matching, Gaussian embedding, Graph edit distance","lastPublishedDoi":"10.21203/rs.3.rs-4137400/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4137400/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In recent years, graph neural network technology has revolutionized molecular graph matching methods, offering new opportunities for drug discovery. 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