Protein-ligand binding affinity prediction: Is 3D binding pose needed?

Protein-ligand binding affinity prediction: Is 3D binding pose needed? | 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 Protein-ligand binding affinity prediction: Is 3D binding pose needed? Degui Zhi, Ming Hsiu Wu, Ziqian Xie This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4277933/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Apr, 2025 Read the published version in Communications Chemistry → Version 1 posted You are reading this latest preprint version Abstract Accurate protein-ligand binding affinity prediction is crucial in drug discovery. Existing methods are predominately docking-free, without explicitly considering atom-level interaction between proteins and ligands in scenarios where crystallized protein-ligand binding conformations are unavailable. Now, with breakthroughs in deep learning AI-based protein folding and binding conformation prediction, can we improve binding affinity prediction? This study introduces a framework, Folding-Docking-Affinity (FDA), which folds proteins, determines protein-ligand binding conformations and predicts binding affinities from three-dimensional protein-ligand binding structures. Our experiments demonstrate that the FDA outperforms state-of-the-art docking-free models in the DAVIS dataset, showcasing the potential of explicit modeling of three-dimensional binding conformations for enhancing binding affinity prediction accuracy. Biological sciences/Computational biology and bioinformatics/Computational models Biological sciences/Computational biology and bioinformatics/Machine learning Full Text Additional Declarations There is NO Competing Interest. Supplementary Files supplementaryinformation.pdf supplementary_information Cite Share Download PDF Status: Published Journal Publication published 07 Apr, 2025 Read the published version in Communications Chemistry → 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-4277933","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":294509120,"identity":"0802b141-3287-46b1-bb6d-846bc47d6db3","order_by":0,"name":"Degui Zhi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYBACexCRwPAvgY29gYEZJiqBT4thA1gLWwIbzwHGZgYGA8JaDA6AKbYEBokEYrUcb78m8YCBJ49P8o354wKGP9H8DcwHb/Pg03LmTJkE0IpiNukcw+YZDAa5Mw6wJVvj1XIjJw2oxSCxDaSFB6hlAwOPmTReLfffgLQkJLZJnoFp4f+GX8sN9mNALQcS2yR44Law4dVi2JPDbJFgANTCk1Y4m8fAOHfGYTZjyzl4tNizH39480fFv8T57Yc3fOapkMvtb29+eOMNHi0MDDwGsNhggDCY8SiGAPYHBJWMglEwCkbBCAcAYMtHXZ6DwfAAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-7754-1890","institution":"University of Texas Health Science Center at Houston","correspondingAuthor":true,"prefix":"","firstName":"Degui","middleName":"","lastName":"Zhi","suffix":""},{"id":294509121,"identity":"97bc6c26-b9f1-4659-998d-68935b24b32b","order_by":1,"name":"Ming Hsiu Wu","email":"","orcid":"","institution":"The University of Texas Health Science Center at Houston","correspondingAuthor":false,"prefix":"","firstName":"Ming","middleName":"Hsiu","lastName":"Wu","suffix":""},{"id":294509122,"identity":"d12bd854-5f33-4d38-af94-d38d556cc611","order_by":2,"name":"Ziqian Xie","email":"","orcid":"","institution":"The University of Texas Health Science Center at Houston","correspondingAuthor":false,"prefix":"","firstName":"Ziqian","middleName":"","lastName":"Xie","suffix":""}],"badges":[],"createdAt":"2024-04-16 19:25:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4277933/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4277933/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s42004-025-01506-1","type":"published","date":"2025-04-07T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80119047,"identity":"378b2634-847a-4a1e-a56c-08c1b38cf557","added_by":"auto","created_at":"2025-04-08 07:07:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":731049,"visible":true,"origin":"","legend":"","description":"","filename":"Proteinligandbindingaffinityprediction.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4277933/v1_covered_c0bbeb59-8843-4579-9257-ab9df5d1902b.pdf"},{"id":55950976,"identity":"21d1e0f8-ee7c-462f-b8b0-42957128da60","added_by":"auto","created_at":"2024-05-06 18:12:05","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":221878,"visible":true,"origin":"","legend":"\u003cp\u003esupplementary_information\u003c/p\u003e","description":"","filename":"supplementaryinformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4277933/v1/77a6a069fd4de42f20c2d813.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Protein-ligand binding affinity prediction: Is 3D binding pose needed?","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":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4277933/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4277933/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Accurate protein-ligand binding affinity prediction is crucial in drug discovery. Existing methods are predominately docking-free, without explicitly considering atom-level interaction between proteins and ligands in scenarios where crystallized protein-ligand binding conformations are unavailable. Now, with breakthroughs\r\nin deep learning AI-based protein folding and binding conformation prediction, can we improve binding affinity prediction? This study introduces a framework,\r\nFolding-Docking-Affinity (FDA), which folds proteins, determines protein-ligand binding conformations and predicts binding affinities from three-dimensional\r\nprotein-ligand binding structures. Our experiments demonstrate that the FDA outperforms state-of-the-art docking-free models in the DAVIS dataset, showcasing\r\nthe potential of explicit modeling of three-dimensional binding conformations for enhancing binding affinity prediction accuracy.","manuscriptTitle":"Protein-ligand binding affinity prediction: Is 3D binding pose needed?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-06 18:12:00","doi":"10.21203/rs.3.rs-4277933/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-chemistry","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commschem","sideBox":"Learn more about [Communications Chemistry](http://www.nature.com/commschem/)","snPcode":"","submissionUrl":"","title":"Communications Chemistry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e87993a6-8055-4783-9341-62c36713b0c9","owner":[],"postedDate":"May 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":31045533,"name":"Biological sciences/Computational biology and bioinformatics/Computational models"},{"id":31045534,"name":"Biological sciences/Computational biology and bioinformatics/Machine learning"}],"tags":[],"updatedAt":"2025-04-08T07:07:07+00:00","versionOfRecord":{"articleIdentity":"rs-4277933","link":"https://doi.org/10.1038/s42004-025-01506-1","journal":{"identity":"communications-chemistry","isVorOnly":false,"title":"Communications Chemistry"},"publishedOn":"2025-04-07 04:00:00","publishedOnDateReadable":"April 7th, 2025"},"versionCreatedAt":"2024-05-06 18:12:00","video":"","vorDoi":"10.1038/s42004-025-01506-1","vorDoiUrl":"https://doi.org/10.1038/s42004-025-01506-1","workflowStages":[]},"version":"v1","identity":"rs-4277933","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4277933","identity":"rs-4277933","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}