Metapath-based Feature Aggregated Heterogeneous Graph Neural Network for Adverse Drug Reactions Predicting

Metapath-based Feature Aggregated Heterogeneous Graph Neural Network for Adverse Drug Reactions Predicting | 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 Metapath-based Feature Aggregated Heterogeneous Graph Neural Network for Adverse Drug Reactions Predicting Wei Wei, Jinghao Fang, Bowen Shi, Changdong Wang, Jin Yang, Yongming Cai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6287822/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 Backgrount: Predicting adverse drug reactions (ADRs) is crucial for drug development, discovery, and design. Although traditional pharmacological experiments can accurately detect the ADRs and have high reliability, the process is costly in terms of time and monetary. With the development and rise of computational methods, graph neural network (GNN)-based ADR prediction methods have gained attention recently since they effectively save research and development costs. Nevertheless, the performance of existing methods is often limited by the size and variety of data they can effectively handle. Furthermore, existing GNN-based methods cannot fully and accurately capture the complex structures and rich semantics of drugs and ADRs in bio-heterogeneous graphs. Result: In this paper, a novel more effective GNN framework named metapath-based feature aggregated heterogeneous graph neural network model (MFAHGN) is proposed for ADRs prediction to achieve more effective and accurate results. In MFAHGN, a drugs-ADRs heterogeneous graph is constructed by integrating multiple well-known biological databases. Then a high-quality feature learning method of drugs and ADRs is designed by using a delicately customized short metapath strategy to capture the complete semantic information of all metapaths, which avoids information decay along the long metapath and fully considers the mutual influence between various metapaths. Conclusions: Extensive experiments are conducted on two biological heterogeneous datasets across six metrics to demonstrate the effectiveness of the proposed model. Medical literature validation confirms 80% of top predictions, including eight novel drug-ADR associations. The code is available at https://github.com/FJH886/MFAHGN. Heterogeneous Graph Graph neural network Metapath Adverse drug reactions prediction 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-6287822","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":443574593,"identity":"a8d09a83-3885-4f20-9609-7d117c2a53ff","order_by":0,"name":"Wei Wei","email":"","orcid":"","institution":"Guangdong Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Wei","suffix":""},{"id":443574594,"identity":"2eaa1e39-b2b4-490b-9309-44d60083c5c1","order_by":1,"name":"Jinghao Fang","email":"","orcid":"","institution":"Guangdong Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Jinghao","middleName":"","lastName":"Fang","suffix":""},{"id":443574595,"identity":"d7f2507a-f02c-4ad3-8285-74e600137ec9","order_by":2,"name":"Bowen Shi","email":"","orcid":"","institution":"Guangdong Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Bowen","middleName":"","lastName":"Shi","suffix":""},{"id":443574596,"identity":"595028ec-c585-472d-a262-cda50ffd05ba","order_by":3,"name":"Changdong Wang","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Changdong","middleName":"","lastName":"Wang","suffix":""},{"id":443574597,"identity":"d5d13499-be24-48b4-b36a-2d6ff00c87e1","order_by":4,"name":"Jin Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYHCChAMMBmxyQBrEYSZWSwWfMUlagOCMXGID0VoMDjA8PFzYZpY+3z33mARDhXViA/vZA4S0JBye2ZaWu/HMuzQJhjPpiQ08eQmEtfC2HcvdOCPHTIKx7XBigwSPATFa/qcbgrX8I1YLzxm2BHkJkJYGIrRIHgZpqWAz3MDzLtki4Vi6cRtPDn4tfMd7kj/zGLDJy7fnHrzxocZatp/9DH4tCod5EqAu5IHEJhte9UAg38B+AMrgIaR2FIyCUTAKRioAAMuJRwTUg8ZlAAAAAElFTkSuQmCC","orcid":"","institution":"Guangdong Pharmaceutical University","correspondingAuthor":true,"prefix":"","firstName":"Jin","middleName":"","lastName":"Yang","suffix":""},{"id":443574598,"identity":"cb8065ae-7d50-44be-9934-220ca63fea6f","order_by":5,"name":"Yongming Cai","email":"","orcid":"","institution":"Guangdong Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Yongming","middleName":"","lastName":"Cai","suffix":""}],"badges":[],"createdAt":"2025-03-23 10:53:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6287822/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6287822/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84522350,"identity":"4f06d99c-baaf-4385-9d86-8092cb3fe399","added_by":"auto","created_at":"2025-06-13 04:01:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":968267,"visible":true,"origin":"","legend":"","description":"","filename":"BMCMFAHGN.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6287822/v1_covered_6b4e1735-9858-47e5-a9bb-385c85f5bc26.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metapath-based Feature Aggregated Heterogeneous Graph Neural Network for Adverse Drug Reactions Predicting","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":"Heterogeneous Graph, Graph neural network, Metapath, Adverse drug reactions prediction","lastPublishedDoi":"10.21203/rs.3.rs-6287822/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6287822/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackgrount: \u003c/strong\u003ePredicting adverse drug reactions (ADRs) is crucial for drug development, discovery, and design. Although traditional pharmacological experiments can accurately detect the ADRs and have high reliability, the process is costly in terms of time and monetary. With the development and rise of computational methods, graph neural network (GNN)-based ADR prediction methods have gained attention recently since they effectively save research and development costs. Nevertheless, the performance of existing methods is often limited by the size and variety of data they can effectively handle. Furthermore, existing GNN-based methods cannot fully and accurately capture the complex structures and rich semantics of drugs and ADRs in bio-heterogeneous graphs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult: \u003c/strong\u003eIn this paper, a novel more effective GNN framework named metapath-based feature aggregated heterogeneous graph neural network model (MFAHGN) is proposed for ADRs prediction to achieve more effective and accurate results. 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