Novel Molecule design with POWGAN, a Policy-Optimized Wasserstein Generative Adversarial Networks

Novel Molecule design with POWGAN, a Policy-Optimized Wasserstein Generative Adversarial Networks | 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 Novel Molecule design with POWGAN, a Policy-Optimized Wasserstein Generative Adversarial Networks Bruno Macedo, Tiago Taveira-Gomes, Inês Ribeiro-Vaz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6149551/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 This study introduces Policy Optimized Wasserstein GAN (POWGAN), a novel generative model that integrates reinforcement learning policy-driven optimization. POWGAN employs a dynamically scaled reward function that adaptively adjusts the training focus, promoting the generation of novel molecules with targeted properties, such as graph connectivity to deliver non-fragmented molecules. The results demonstrated substantial improvements over previous approaches, highlighting the model's ability to achieve nearly 100% connectivity and significantly enhance generative capacity by up to eight-fold, producing more than 10,000 novel molecules. R-MedGAN, utilizing POWGAN's capability to produce structurally diverse molecules, facilitated the exploration of novel chemical regions and substantially expanded the accessible chemical space. These findings underscore the effectiveness of adaptive reinforcement-driven strategies in generative adversarial networks oriented by rewards for molecular discovery. Biological sciences/Drug discovery/Medicinal chemistry/Drug discovery and development Health sciences/Molecular medicine Physical sciences/Mathematics and computing/Computer science Physical sciences/Chemistry/Cheminformatics Full Text Additional Declarations There is NO Competing Interest. 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-6149551","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":431112846,"identity":"5d85fd6b-4de5-47c2-9e82-c24554cd512e","order_by":0,"name":"Bruno Macedo","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-0127-6573","institution":"University of Porto","correspondingAuthor":true,"prefix":"","firstName":"Bruno","middleName":"","lastName":"Macedo","suffix":""},{"id":431112847,"identity":"682021f7-21b7-4f3b-aaf9-e00f56fe3a7b","order_by":1,"name":"Tiago Taveira-Gomes","email":"","orcid":"","institution":"Faculty of Medicine, University of Porto","correspondingAuthor":false,"prefix":"","firstName":"Tiago","middleName":"","lastName":"Taveira-Gomes","suffix":""},{"id":431112848,"identity":"4a4e01a0-d447-40ec-84cd-9b42c670ddeb","order_by":2,"name":"Inês Ribeiro-Vaz","email":"","orcid":"","institution":"Faculty of Medicine, University of Porto","correspondingAuthor":false,"prefix":"","firstName":"Inês","middleName":"","lastName":"Ribeiro-Vaz","suffix":""}],"badges":[],"createdAt":"2025-03-03 23:50:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6149551/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6149551/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79228079,"identity":"11cc3c18-c92f-4a62-a4e9-3e947f217712","added_by":"auto","created_at":"2025-03-26 01:36:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":950845,"visible":true,"origin":"","legend":"","description":"","filename":"MedGANRSubmissionFINAL03032025.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6149551/v1_covered_73958686-31b8-466c-8e7b-404f6695d541.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Novel Molecule design with POWGAN, a Policy-Optimized Wasserstein Generative Adversarial Networks","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-6149551/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6149551/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study introduces Policy Optimized Wasserstein GAN (POWGAN), a novel generative model that integrates reinforcement learning policy-driven optimization. POWGAN employs a dynamically scaled reward function that adaptively adjusts the training focus, promoting the generation of novel molecules with targeted properties, such as graph connectivity to deliver non-fragmented molecules. The results demonstrated substantial improvements over previous approaches, highlighting the model's ability to achieve nearly 100% connectivity and significantly enhance generative capacity by up to eight-fold, producing more than 10,000 novel molecules. R-MedGAN, utilizing POWGAN's capability to produce structurally diverse molecules, facilitated the exploration of novel chemical regions and substantially expanded the accessible chemical space. These findings underscore the effectiveness of adaptive reinforcement-driven strategies in generative adversarial networks oriented by rewards for molecular discovery.\u003c/p\u003e","manuscriptTitle":"Novel Molecule design with POWGAN, a Policy-Optimized Wasserstein Generative Adversarial Networks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-26 00:56:18","doi":"10.21203/rs.3.rs-6149551/v1","editorialEvents":[],"status":"published","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}}],"origin":"","ownerIdentity":"a3aa9524-7ed4-4479-9e1e-e36aaf23d760","owner":[],"postedDate":"March 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":45918250,"name":"Biological sciences/Drug discovery/Medicinal chemistry/Drug discovery and development"},{"id":45918251,"name":"Health sciences/Molecular medicine"},{"id":45918252,"name":"Physical sciences/Mathematics and computing/Computer science"},{"id":45918253,"name":"Physical sciences/Chemistry/Cheminformatics"}],"tags":[],"updatedAt":"2025-05-18T10:53:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-26 00:56:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6149551","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6149551","identity":"rs-6149551","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}