An Integrated AI Framework for Targeted Depression Drug Discovery: Leveraging Cheminformatics and Genomics

An Integrated AI Framework for Targeted Depression Drug Discovery: Leveraging Cheminformatics and Genomics | 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 An Integrated AI Framework for Targeted Depression Drug Discovery: Leveraging Cheminformatics and Genomics First Ahmed Miloudi, Second Mohamed Chikri, Second Said Boujraf This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6868799/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 Artificial intelligence and machine learning offer transformative opportunities for drug discovery, particularly in complex psychiatric disorders such as major depressive disorder (MDD). In this study, we introduce a dual-pipeline framework that combines cheminformatics-based compound screening with genomics-informed target discovery. We first constructed a supervised learning pipeline utilizing SMILES strings, Lipinski descriptors, and PaDEL fingerprints to predict compound activity using multiple regressors and deep learning models. In parallel, we leveraged GWAS data to identify genes implicated in depression through p-value and risk allele frequency (RAF) analyses, retrieving corresponding bioactive compounds from ChEMBL. These compounds were processed for drug-likeness and molecular descriptor profiling. Our integrated framework enhances the predictive power of AI models by anchoring chemical screening in human genetic data. The results demonstrate the potential of our approach to inform biologically grounded and data-driven discovery of novel antidepressant agents. Biological sciences/Computational biology and bioinformatics Biological sciences/Drug discovery machine learning artificial intelligence drug discovery Computational science 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-6868799","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":492069723,"identity":"9c363ee5-fa75-4ca8-849d-e18bbd428183","order_by":0,"name":"First Ahmed Miloudi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYDACCSDmASI+EDuhAipClBY2sJYzJGhhAGthbCNCC//s7sQHbxi2ybCx9xjeeDjvsL3u7OYHDD9qGKL5G3BYcufsZsM5DLd52HjOGFskbjucuO3OMQPGnmMMuTMO4LDmRu42aR6QFokcMwmglgSzGwkGDLwNDLkNOLTI38jd/huhZc5he7Mb6R8Y/wK1zMehxQBoCzNCS8Nhxm03cgyYQbZswKHF8EbuZsk5BiC/HCu2SDiWDvTLmYLDMsckcjfi0CJ3I3fjhzcVt+352Zs33vxRY21vdrt948M3NTa583B5H+I8NP4BIiJ0FIyCUTAKRgEeAAARrl2g5WLhbwAAAABJRU5ErkJggg==","orcid":"","institution":"Faculté de médecine et de pharmacie","correspondingAuthor":true,"prefix":"","firstName":"First","middleName":"Ahmed","lastName":"Miloudi","suffix":""},{"id":492069725,"identity":"b0ad6299-8c1f-4806-a80d-9d6a548d90d2","order_by":1,"name":"Second Mohamed Chikri","email":"","orcid":"","institution":"Faculté de médecine et de pharmacie","correspondingAuthor":false,"prefix":"","firstName":"Second","middleName":"Mohamed","lastName":"Chikri","suffix":""},{"id":492069727,"identity":"486f87e7-6bd7-48f2-8907-972972764345","order_by":2,"name":"Second Said Boujraf","email":"","orcid":"","institution":"Faculté de médecine et de pharmacie","correspondingAuthor":false,"prefix":"","firstName":"Second","middleName":"Said","lastName":"Boujraf","suffix":""}],"badges":[],"createdAt":"2025-06-11 07:08:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6868799/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6868799/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88534566,"identity":"e0cd203c-e73d-42a2-ac6d-2d3dc32ff02d","added_by":"auto","created_at":"2025-08-07 12:16:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":814631,"visible":true,"origin":"","legend":"","description":"","filename":"SpringerNatureLaTeXTemplateMiloudi.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6868799/v1_covered_68e312a9-38cd-4e47-a0c3-79ce6b021276.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Integrated AI Framework for Targeted Depression Drug Discovery: Leveraging Cheminformatics and Genomics","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":"machine learning, artificial intelligence, drug discovery, Computational science","lastPublishedDoi":"10.21203/rs.3.rs-6868799/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6868799/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e Artificial intelligence and machine learning offer transformative opportunities for drug discovery, particularly in complex psychiatric disorders such as major depressive disorder (MDD). 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