PharmRL: Pharmacophore elucidation with Deep Geometric Reinforcement Learning

PharmRL: Pharmacophore elucidation with Deep Geometric Reinforcement Learning | 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 PharmRL: Pharmacophore elucidation with Deep Geometric Reinforcement Learning Rishal Aggarwal, David Ryan Koes This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5033986/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Molecular interactions between proteins and their ligands are important for drug design. A pharmacophore consists of favorable molecular interactions in a protein binding site and can be utilized for virtual screening. Pharmacophores are easiest to identify from co-crystal structures of a bound protein-ligand complex. In this work, however, we develop a deep learning method that can identify pharmacophores in the absence of a ligand. Specifically, we train a CNN model to identify potential favorable interactions in the the binding site, and develop a deep geometric Q-learning algorithm that attempts to select an optimal subset of these interaction points to form a pharmacophore. With this algorithm, we show better prospective virtual screening performance, in terms of F1 scores, on the DUD-E dataset than random selection of ligand identified features from co-crystal structures. We also conduct experiments on the LIT-PCBA dataset and show that it provides efficient solutions for identifying active molecules. Finally, we test our method by screening the COVID moonshot dataset and show that it would be effective in identifying prospective lead molecules even in the absence of fragment screening experiments. Alongside, we provide a Google Colab notebook for ease of use of the developed method. Pharmacophores Virtual Screening Protein-Ligand interactions Machine Learning Full Text Additional Declarations No competing interests reported. Supplementary Files supplementary.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 30 Sep, 2024 Reviews received at journal 19 Sep, 2024 Reviews received at journal 16 Sep, 2024 Reviewers agreed at journal 11 Sep, 2024 Reviewers agreed at journal 11 Sep, 2024 Reviewers invited by journal 11 Sep, 2024 Editor assigned by journal 05 Sep, 2024 Submission checks completed at journal 05 Sep, 2024 First submitted to journal 04 Sep, 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. 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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-5033986","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":356059231,"identity":"807313f7-f321-4757-a972-7eef7704a649","order_by":0,"name":"Rishal Aggarwal","email":"","orcid":"","institution":"University of Pittsburgh","correspondingAuthor":false,"prefix":"","firstName":"Rishal","middleName":"","lastName":"Aggarwal","suffix":""},{"id":356059233,"identity":"2f6eeca2-f517-48e0-b090-c244d9af8432","order_by":1,"name":"David Ryan Koes","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArElEQVRIiWNgGAWjYDCCMyCigoEHzOEhXssZBh4eNpK0MLYBVROthe/M4YMPf867I2Mv38D44G0bEVokz7YlG/NuewZyGLPhXGK0GJznMZNm3HYYpIVNmpdILeY/f84Ba2H/TZyWsz1mDLwNEFuYidIieeZYsjTPMaBfjiU2S845R4QWvjPJBz/+qLljz958+OCHN2VEaIGCA0DM2EC8eqiWUTAKRsEoGAU4AABOFTNsI9iQoAAAAABJRU5ErkJggg==","orcid":"","institution":"University of Pittsburgh","correspondingAuthor":true,"prefix":"","firstName":"David","middleName":"Ryan","lastName":"Koes","suffix":""}],"badges":[],"createdAt":"2024-09-04 21:38:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5033986/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5033986/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":65213098,"identity":"adee433e-37b6-4304-bc2c-2beba4b47e39","added_by":"auto","created_at":"2024-09-24 21:19:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4149266,"visible":true,"origin":"","legend":"","description":"","filename":"submit.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5033986/v1_covered_a13749f6-067c-4a5c-8b3c-773e29f6cad6.pdf"},{"id":65213077,"identity":"08a12f5c-38cd-4591-96dd-2854b4ba4b87","added_by":"auto","created_at":"2024-09-24 21:11:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":7226875,"visible":true,"origin":"","legend":"","description":"","filename":"supplementary.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5033986/v1/5fca7befd6eba04281d0402f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"PharmRL: Pharmacophore elucidation with Deep Geometric Reinforcement Learning","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-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Biology](https://bmcbiol.biomedcentral.com/)","snPcode":"12915","submissionUrl":"https://submission.springernature.com/new-submission/12915/3","title":"BMC Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Pharmacophores, Virtual Screening, Protein-Ligand interactions, Machine Learning","lastPublishedDoi":"10.21203/rs.3.rs-5033986/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5033986/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Molecular interactions between proteins and their ligands are important for drug design. 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