Drug repurposing Based on the DTD-GNN Graph Neural Network: Revealing the Relationships among Drugs, Targets and Diseases

Drug repurposing Based on the DTD-GNN Graph Neural Network: Revealing the Relationships among Drugs, Targets and Diseases | 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 Drug repurposing Based on the DTD-GNN Graph Neural Network: Revealing the Relationships among Drugs, Targets and Diseases Wenjun Li, Wanjun Ma, Mengyun Yang, Xiwei Tang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4097710/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Jun, 2024 Read the published version in BMC Genomics → Version 1 posted 10 You are reading this latest preprint version Abstract Motivation : The rational modelling of the relationship among drugs, targets and diseases is crucial for drug retargeting. While significant progress has been made in studying binary relationships, further research is needed to deepen our understanding of ternary relationships. The application of graph neural networks in drug retargeting is increasing, but further research is needed to determine the appropriate modelling method for ternary relationships and how to capture their complex multi-feature structure. Results : The aim of this study was to construct relationships among drug, targets and diseases. To represent the complex relationships among these entities, we used a heterogeneous graph structure. Additionally, we propose a DTD-GNN model that combines graph convolutional networks and graph attention networks to learn feature representations and association information, facilitating a more thorough exploration of the relationships. The experimental results demonstrate that the DTD-GNN model outperforms other graph neural network models in terms of AUC, Precision, and F1-score. The study has important implications for gaining a comprehensive understanding of the relationships between drugs and diseases, as well as for further research and application in exploring the mechanisms of drug-disease interactions. The study reveals these relationships, providing possibilities for innovative therapeutic strategies in medicine. Drug repurposing Drug-Target-Disease ternary relationship DTD-GNN model Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 11 Jun, 2024 Read the published version in BMC Genomics → Version 1 posted Editorial decision: Revision requested 21 May, 2024 Reviews received at journal 20 May, 2024 Reviews received at journal 11 May, 2024 Reviewers agreed at journal 19 Apr, 2024 Reviewers agreed at journal 18 Apr, 2024 Reviewers invited by journal 18 Apr, 2024 Editor assigned by journal 18 Apr, 2024 Editor invited by journal 18 Mar, 2024 Submission checks completed at journal 17 Mar, 2024 First submitted to journal 14 Mar, 2024 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-4097710","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":280744515,"identity":"dd9b3e74-1c93-4726-848d-1c0d5b4bd415","order_by":0,"name":"Wenjun Li","email":"","orcid":"","institution":"Changsha University of Science and Technology Changsha","correspondingAuthor":false,"prefix":"","firstName":"Wenjun","middleName":"","lastName":"Li","suffix":""},{"id":280744516,"identity":"7f50d1ad-2f52-4421-b5bf-19b01538a0dd","order_by":1,"name":"Wanjun Ma","email":"","orcid":"","institution":"Changsha University of Science and Technology Changsha","correspondingAuthor":false,"prefix":"","firstName":"Wanjun","middleName":"","lastName":"Ma","suffix":""},{"id":280744519,"identity":"8d8bc074-d43e-4f5e-ae77-bd6826409a16","order_by":2,"name":"Mengyun Yang","email":"","orcid":"","institution":"Hunan First Normal University Changsha","correspondingAuthor":false,"prefix":"","firstName":"Mengyun","middleName":"","lastName":"Yang","suffix":""},{"id":280744520,"identity":"4aa9515d-9f36-44f9-91c4-d1388d516efb","order_by":3,"name":"Xiwei Tang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYHACAyCWYGBgZj5w4EMFcVoMGyBa2BIPzjhDvBYQ4DE+zNtChHpzieTtDxjbLBL72Xk+HOBtYJDnFzuAX4vljLTCBoYzEokzm3k3HJDcwWA4c3YCfi0GN3KADquQyN1wGKjF8AxDgsFtorQYSOTuP8zz4EBiG9FaQLYw8zAcOEiUljPPCmcA/VI/4zCbwcGGMxJE+OV48oYPjG11xvz9hx9//lNhI88vTUALCDD/QbAlCCsfBaNgFIyCUUAYAABwCEakdnpTBwAAAABJRU5ErkJggg==","orcid":"","institution":"Hunan First Normal University Changsha","correspondingAuthor":true,"prefix":"","firstName":"Xiwei","middleName":"","lastName":"Tang","suffix":""}],"badges":[],"createdAt":"2024-03-14 06:15:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4097710/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4097710/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12864-024-10499-5","type":"published","date":"2024-06-11T14:51:04+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":58822249,"identity":"9697827b-e73d-4140-af54-4c56a4a7ed5a","added_by":"auto","created_at":"2024-06-21 16:39:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1939717,"visible":true,"origin":"","legend":"","description":"","filename":"DTDGNN1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4097710/v1_covered_d58f7b12-d5d5-4d44-8ce5-d50a8e918e7d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Drug repurposing Based on the DTD-GNN Graph Neural Network: Revealing the Relationships among Drugs, Targets and Diseases","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Drug repurposing, Drug-Target-Disease ternary relationship, DTD-GNN model","lastPublishedDoi":"10.21203/rs.3.rs-4097710/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4097710/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eMotivation\u003c/strong\u003e: The rational modelling of the relationship among drugs, targets and diseases is crucial for drug retargeting. While significant progress has been made in studying binary relationships, further research is needed to deepen our understanding of ternary relationships. The application of graph neural networks in drug retargeting is increasing, but further research is needed to determine the appropriate modelling method for ternary relationships and how to capture their complex multi-feature structure.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The aim of this study was to construct relationships among drug, targets and diseases. To represent the complex relationships among these entities, we used a heterogeneous graph structure. Additionally, we propose a DTD-GNN model that combines graph convolutional networks and graph attention networks to learn feature representations and association information, facilitating a more thorough exploration of the relationships. The experimental results demonstrate that the DTD-GNN model outperforms other graph neural network models in terms of AUC, Precision, and F1-score. The study has important implications for gaining a comprehensive understanding of the relationships between drugs and diseases, as well as for further research and application in exploring the mechanisms of drug-disease interactions. The study reveals these relationships, providing possibilities for innovative therapeutic strategies in medicine.\u003c/p\u003e","manuscriptTitle":"Drug repurposing Based on the DTD-GNN Graph Neural Network: Revealing the Relationships among Drugs, Targets and Diseases","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-20 12:22:06","doi":"10.21203/rs.3.rs-4097710/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-21T12:29:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-20T09:27:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-11T14:53:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"9839eaef-13cb-484f-9cbc-c35aa3158876","date":"2024-04-19T05:49:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"c16bc18b-1cf4-4483-b269-898a681209c9_SNPRID","date":"2024-04-19T02:19:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-18T08:16:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-18T07:58:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-03-18T07:23:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-17T09:30:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2024-03-14T06:14:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9da4ca3e-9fd0-4747-ad44-92a1a7d51e98","owner":[],"postedDate":"March 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-06-21T14:51:04+00:00","versionOfRecord":{"articleIdentity":"rs-4097710","link":"https://doi.org/10.1186/s12864-024-10499-5","journal":{"identity":"bmc-genomics","isVorOnly":false,"title":"BMC Genomics"},"publishedOn":"2024-06-11 14:51:04","publishedOnDateReadable":"June 11th, 2024"},"versionCreatedAt":"2024-03-20 12:22:06","video":"","vorDoi":"10.1186/s12864-024-10499-5","vorDoiUrl":"https://doi.org/10.1186/s12864-024-10499-5","workflowStages":[]},"version":"v1","identity":"rs-4097710","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4097710","identity":"rs-4097710","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}