Graph-Based Link Prediction for Epilepsy Drug Discovery

Graph-Based Link Prediction for Epilepsy Drug Discovery | 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 Graph-Based Link Prediction for Epilepsy Drug Discovery Xiaolong Shi, Sundareswaran Raman, Shanmugapriya M., Nikilesh Jayaguptha, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6653352/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Sep, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Epilepsy is one of the most prevalent neurological disorders, affecting approximately 23 million people in Asia alone. It isa disorder with severe social impacts and is going to progressively damage the brain. It encompasses a wide range ofsyndromes and each one of them differs significantly in treatment options. seizures is the common symptom in all of them.Despite being one of the most researched clinical conditions, the exact mechanism is still unknown, and this poses challengesfor coming up with an effective treatment mechanism. Conventional treatment which includes Anti-Epileptic Drugs (AEDs)come with a lot of limitations. Inspired by Ayurveda, we propose a computational framework to predict phytochemical-proteininteractions for potential epilepsy treatment. We propose that the interaction can be modelled as a bipartite graph, wherenodes represent phytochemicals and proteins and edges are represented by interactions. We employ Graph neural networksto capture both local and global information about the graph. Initially, the entire graph was trained using Graph ConvolutionalNetwork (GCN), Graph Attention Network (GAT), and GraphSAGE. To enhance predictive performance, we then constructedone-hop enclosing subgraphs for both positive and negative samples and trained the same three models on this refined dataset.Our best-performing model achieved an accuracy of 0.9778, precision of 0.9574, F1-score of 0.9782, and ROC-AUC score of0.9994. Biological sciences/Drug discovery Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 30 Sep, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 09 Jul, 2025 Reviews received at journal 06 Jul, 2025 Reviewers agreed at journal 06 Jul, 2025 Reviews received at journal 08 Jun, 2025 Reviewers agreed at journal 28 May, 2025 Reviewers agreed at journal 28 May, 2025 Reviewers invited by journal 28 May, 2025 Editor assigned by journal 27 May, 2025 Editor invited by journal 19 May, 2025 Submission checks completed at journal 13 May, 2025 First submitted to journal 13 May, 2025 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. 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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-6653352","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":463342380,"identity":"9422aaca-31a3-4ff5-ab1b-dfe280debc16","order_by":0,"name":"Xiaolong Shi","email":"","orcid":"","institution":"Guangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Xiaolong","middleName":"","lastName":"Shi","suffix":""},{"id":463342381,"identity":"a7baa9bf-90e7-471f-8438-2662b46c0545","order_by":1,"name":"Sundareswaran Raman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYJACCSS2DYTiASPitKSRruUwXAtOID8j9+CNn3ts7A2On334uXDPeXv52Q2MD962MciY49BicCMv2bLnWVrihjPpxtIznt1O3HDnALPh3DYGHssGHFokcswkeA4cTjA7kMYgzXPgdoKBRAKbNC9Qi8EBXA7LMZP8c+C/vdn5Z8y/eQ6cs5efkcD+G58Whhs5ZkDDDzBuu5HGBmY03EhgY8anxeDMG2NrmQPJiftvPGOz5gEyNtxIbJacc04Ct8PacwxvvjlgZy/Zn8Z8mwfIkJ+RfPDDmzJgGOJyGBbA2MCAGlmjYBSMglEwCkgFAFc7WMwU4H5cAAAAAElFTkSuQmCC","orcid":"","institution":"Sri Sivasubramaniya Nadar College of Engineering","correspondingAuthor":true,"prefix":"","firstName":"Sundareswaran","middleName":"","lastName":"Raman","suffix":""},{"id":463342382,"identity":"7f6d0c00-f6ab-4ca6-9fcf-07721065c8a1","order_by":2,"name":"Shanmugapriya M.","email":"","orcid":"","institution":"Sri Sivasubramaniya Nadar College of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Shanmugapriya","middleName":"","lastName":"M.","suffix":""},{"id":463342384,"identity":"de8d7068-7a04-4f1b-82e9-c96d30040071","order_by":3,"name":"Nikilesh Jayaguptha","email":"","orcid":"","institution":"Sri Sivasubramaniya Nadar College of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Nikilesh","middleName":"","lastName":"Jayaguptha","suffix":""},{"id":463342387,"identity":"1d5b3c27-072c-410a-9af3-a7604dbf2814","order_by":4,"name":"Aysha Khan","email":"","orcid":"","institution":"University of Technology and Applied Sciences, Musanna","correspondingAuthor":false,"prefix":"","firstName":"Aysha","middleName":"","lastName":"Khan","suffix":""}],"badges":[],"createdAt":"2025-05-13 08:38:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6653352/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6653352/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-14550-7","type":"published","date":"2025-09-30T15:58:12+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":92883892,"identity":"6976a76e-15a4-4a19-9f54-b95f57d1a0fd","added_by":"auto","created_at":"2025-10-06 16:10:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":347048,"visible":true,"origin":"","legend":"","description":"","filename":"Template.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6653352/v1_covered_df2d5a6b-e567-4638-9913-6e04394b83ef.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eGraph-Based Link Prediction for Epilepsy Drug Discovery\u003c/p\u003e","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":"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-6653352/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6653352/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Epilepsy is one of the most prevalent neurological disorders, affecting approximately 23 million people in Asia alone. It isa disorder with severe social impacts and is going to progressively damage the brain. It encompasses a wide range ofsyndromes and each one of them differs significantly in treatment options. seizures is the common symptom in all of them.Despite being one of the most researched clinical conditions, the exact mechanism is still unknown, and this poses challengesfor coming up with an effective treatment mechanism. Conventional treatment which includes Anti-Epileptic Drugs (AEDs)come with a lot of limitations. Inspired by Ayurveda, we propose a computational framework to predict phytochemical-proteininteractions for potential epilepsy treatment. We propose that the interaction can be modelled as a bipartite graph, wherenodes represent phytochemicals and proteins and edges are represented by interactions. We employ Graph neural networksto capture both local and global information about the graph. 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