Receptor and ligand Conformations Dictate Docking Success: Insights from Physics-Based and Data-Driven Redocking and Cross-docking Studies on the Binding Pose Prediction for ligands on Alzheimer’s Disease Targets

Receptor and ligand Conformations Dictate Docking Success: Insights from Physics-Based and Data-Driven Redocking and Cross-docking Studies on the Binding Pose Prediction for ligands on Alzheimer’s Disease Targets | 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 Receptor and ligand Conformations Dictate Docking Success: Insights from Physics-Based and Data-Driven Redocking and Cross-docking Studies on the Binding Pose Prediction for ligands on Alzheimer’s Disease Targets Kapali Suri, Anshul Yadav, Abhishek Tripathi, N. Arul Murugan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9441998/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract A key challenge in structure-based drug discovery is accurately predicting protein-ligand binding due to the complex interplay between receptor flexibility, ligand conformational diversity, and the limitations of current sampling and scoring methods. In this study, we employed standardised redocking and cross-docking protocols to compare traditional physics-based docking tools (AutoDock, AutoDock Vina, and DOCK 6) with several data-driven approaches, including a deep learning (DL) method (GNINA 1.3) and diffusion-based models (AlphaFold 3, Boltz-2, and DiffDock-L). The performance was evaluated on three relevant targets to Alzheimer’s disease (AD), i.e., acetylcholinesterase (AChE), β-secretase 1 (BACE1), and glycogen synthase kinase-3β (GSK-3β). In redocking, rigid docking achieved an accuracy of over 50% for the top-ranked pose, indicating that physics-based methods remain competitive under ideal conditions. However, cross-docking shows a drop in performance across all these classical methods, indicating that they are sensitive to protein and ligand conformation state. The apo structure yielded improved accuracy for GSK-3β, whereas the holo structures for AChE and BACE1 highlight intrinsic differences in binding-site architecture and flexibility across targets. Further, rigid docking consistently outperforms flexible protocols, indicating that current methods of receptor flexibility introduce conformational noise without improving accuracy. DL-based methods exhibit strong performance in pose generation in the cross-docking scenario but demonstrate strong target dependence. Despite extensive training-test overlap, we observed significant failure rates in binding pose prediction in the case of cross-docking, prominently in GNINA 1.3. This indicates that current models merely memorise the geometries of ligands. Together, these findings indicate that receptor and ligand conformations are the primary factors influencing docking accuracy. Moreover, it was established that cross-docking is a rigorous standard for realistic assessment. Our results reveal major limitations in current binding pose predictions and emphasise the necessity of methods that better capture conformational variability for reliable drug discovery. Rigid and flexible molecular docking Alzheimer’s disease (AD) AutoDock AutoDock Vina DOCK 6 GNINA 1.3 Beta-site amyloid precursor protein cleaving enzyme (BACE1) acetylcholinesterase (AChE) glycogen synthase kinase-3 Beta (GSK-3β) Binding pose prediction Data leakage Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryFile.docx TableS3.csv TableS4.csv TableS5.csv TableS6.csv Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 11 May, 2026 Reviews received at journal 26 Apr, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviewers invited by journal 20 Apr, 2026 Editor assigned by journal 19 Apr, 2026 Submission checks completed at journal 18 Apr, 2026 First submitted to journal 16 Apr, 2026 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-9441998","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":629653912,"identity":"06240833-2b82-4aad-bba1-7a93886ca61e","order_by":0,"name":"Kapali Suri","email":"","orcid":"","institution":"Indraprastha Institute of Information Technology Delhi","correspondingAuthor":false,"prefix":"","firstName":"Kapali","middleName":"","lastName":"Suri","suffix":""},{"id":629653913,"identity":"899918f1-2f1e-4a5c-ac8e-bd3907b90055","order_by":1,"name":"Anshul Yadav","email":"","orcid":"","institution":"Indraprastha Institute of Information Technology Delhi","correspondingAuthor":false,"prefix":"","firstName":"Anshul","middleName":"","lastName":"Yadav","suffix":""},{"id":629653915,"identity":"b5b01ab1-eda1-4332-a174-450be95c04c6","order_by":2,"name":"Abhishek Tripathi","email":"","orcid":"","institution":"Indraprastha Institute of Information Technology Delhi","correspondingAuthor":false,"prefix":"","firstName":"Abhishek","middleName":"","lastName":"Tripathi","suffix":""},{"id":629653916,"identity":"76058ec0-223f-428e-8738-19c97db2f864","order_by":3,"name":"N. Arul Murugan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYHACxgNw5gcDCR4GhgQQUwKvHrgWxhlALTwkaWEGWsEA1YIb8M8+Y3CAMedwvsH5xc8e2xRYyNizJzB++MFgkYdLi8S5HKCWbYctN9x4Zm6cA3IYzwNmyR4GiWKc1pzhAWsxMLhxwEwarEUigUEaaFZiAw4d8ggtx79JW0C0MP/Gp8UAruV8j5k0A0QLG15bDM+wFRxI3JZuIHmDp0yyB6TlzMM2SyADpxa5M8wbH3zcZm3Ad/74Nokff+rs2duTD9/4UVGHUwsYJIAIiQQYlxGo2ACfehjgP0CMqlEwCkbBKBiJAABHuE/yiIxrAAAAAABJRU5ErkJggg==","orcid":"","institution":"Indraprastha Institute of Information Technology Delhi","correspondingAuthor":true,"prefix":"","firstName":"N.","middleName":"Arul","lastName":"Murugan","suffix":""}],"badges":[],"createdAt":"2026-04-16 20:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9441998/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9441998/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108183531,"identity":"a1bed63a-c79f-4779-bf35-da567175f16f","added_by":"auto","created_at":"2026-04-30 09:01:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1935862,"visible":true,"origin":"","legend":"","description":"","filename":"finaldraft.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9441998/v1_covered_1d512f18-86da-45d7-8ea1-671e47908e70.pdf"},{"id":108031511,"identity":"3c4ee8ab-0bf2-401e-be1f-6d50b0c22cb7","added_by":"auto","created_at":"2026-04-28 15:59:43","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":8585,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile.docx","url":"https://assets-eu.researchsquare.com/files/rs-9441998/v1/36cfbb5779f9a47f4b5d0244.docx"},{"id":108181886,"identity":"ed450c2c-5110-4898-bdf5-13679fe2f881","added_by":"auto","created_at":"2026-04-30 08:58:59","extension":"csv","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1431594,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.csv","url":"https://assets-eu.researchsquare.com/files/rs-9441998/v1/25ce5bbdd95b24e61f71c180.csv"},{"id":108031513,"identity":"90f5b412-96b5-486c-983b-4e3d7fcbc328","added_by":"auto","created_at":"2026-04-28 15:59:43","extension":"csv","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":653408,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.csv","url":"https://assets-eu.researchsquare.com/files/rs-9441998/v1/825078307303928dfb3f34d5.csv"},{"id":108181187,"identity":"18c5fb47-bb3b-4afa-96e8-4fc4cf86d48b","added_by":"auto","created_at":"2026-04-30 08:58:19","extension":"csv","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":639689,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5.csv","url":"https://assets-eu.researchsquare.com/files/rs-9441998/v1/8da90b94bee58474b73fc6e2.csv"},{"id":108031514,"identity":"9b778bb8-20c9-4d94-a25d-d4539974609c","added_by":"auto","created_at":"2026-04-28 15:59:43","extension":"csv","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":639689,"visible":true,"origin":"","legend":"","description":"","filename":"TableS6.csv","url":"https://assets-eu.researchsquare.com/files/rs-9441998/v1/30a0552cf9f36dfd0bb8cae8.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Receptor and ligand Conformations Dictate Docking Success: Insights from Physics-Based and Data-Driven Redocking and Cross-docking Studies on the Binding Pose Prediction for ligands on Alzheimer’s Disease Targets","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"journal-of-cheminformatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"chin","sideBox":"Learn more about [Journal of Cheminformatics](https://jcheminf.biomedcentral.com/)","snPcode":"13321","submissionUrl":"https://submission.nature.com/new-submission/13321/3","title":"Journal of Cheminformatics","twitterHandle":"@jcheminf","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Rigid and flexible molecular docking, Alzheimer’s disease (AD), AutoDock, AutoDock Vina, DOCK 6, GNINA 1.3, Beta-site amyloid precursor protein cleaving enzyme (BACE1), acetylcholinesterase (AChE), glycogen synthase kinase-3 Beta (GSK-3β), Binding pose prediction, Data leakage","lastPublishedDoi":"10.21203/rs.3.rs-9441998/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9441998/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA key challenge in structure-based drug discovery is accurately predicting protein-ligand binding due to the complex interplay between receptor flexibility, ligand conformational diversity, and the limitations of current sampling and scoring methods. In this study, we employed standardised redocking and cross-docking protocols to compare traditional physics-based docking tools (AutoDock, AutoDock Vina, and DOCK 6) with several data-driven approaches, including a deep learning (DL) method (GNINA 1.3) and diffusion-based models (AlphaFold 3, Boltz-2, and DiffDock-L). The performance was evaluated on three relevant targets to Alzheimer\u0026rsquo;s disease (AD), i.e., acetylcholinesterase (AChE), β-secretase 1 (BACE1), and glycogen synthase kinase-3β (GSK-3β). In redocking, rigid docking achieved an accuracy of over 50% for the top-ranked pose, indicating that physics-based methods remain competitive under ideal conditions. However, cross-docking shows a drop in performance across all these classical methods, indicating that they are sensitive to protein and ligand conformation state. The apo structure yielded improved accuracy for GSK-3β, whereas the holo structures for AChE and BACE1 highlight intrinsic differences in binding-site architecture and flexibility across targets. Further, rigid docking consistently outperforms flexible protocols, indicating that current methods of receptor flexibility introduce conformational noise without improving accuracy. DL-based methods exhibit strong performance in pose generation in the cross-docking scenario but demonstrate strong target dependence. Despite extensive training-test overlap, we observed significant failure rates in binding pose prediction in the case of cross-docking, prominently in GNINA 1.3. This indicates that current models merely memorise the geometries of ligands. Together, these findings indicate that receptor and ligand conformations are the primary factors influencing docking accuracy. Moreover, it was established that cross-docking is a rigorous standard for realistic assessment. Our results reveal major limitations in current binding pose predictions and emphasise the necessity of methods that better capture conformational variability for reliable drug discovery.\u003c/p\u003e","manuscriptTitle":"Receptor and ligand Conformations Dictate Docking Success: Insights from Physics-Based and Data-Driven Redocking and Cross-docking Studies on the Binding Pose Prediction for ligands on Alzheimer’s Disease Targets","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-28 15:59:36","doi":"10.21203/rs.3.rs-9441998/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"58767836626521650583369185146845070107","date":"2026-05-11T15:49:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-26T06:29:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"129149825997367476867142988812143218913","date":"2026-04-21T01:21:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-20T14:43:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-19T10:32:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-18T04:18:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Cheminformatics","date":"2026-04-16T20:43:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-cheminformatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"chin","sideBox":"Learn more about [Journal of Cheminformatics](https://jcheminf.biomedcentral.com/)","snPcode":"13321","submissionUrl":"https://submission.nature.com/new-submission/13321/3","title":"Journal of Cheminformatics","twitterHandle":"@jcheminf","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b3b8ffe5-2a1f-4b76-a589-16e44eeb50da","owner":[],"postedDate":"April 28th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"58767836626521650583369185146845070107","date":"2026-05-11T15:49:05+00:00","index":21,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T15:59:39+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-28 15:59:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9441998","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9441998","identity":"rs-9441998","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}