Machine Learning-Aided Molecular Dynamics Simulation for Prediction of Binding Kinetics

Machine Learning-Aided Molecular Dynamics Simulation for Prediction of Binding Kinetics | 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 Machine Learning-Aided Molecular Dynamics Simulation for Prediction of Binding Kinetics Fatemeh Shahbazi, Mohammad Nasr Esfahani, Amir Keshmiri, Masoud Jabbari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3879169/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Sep, 2024 Read the published version in Scientific Reports → Version 1 posted 9 You are reading this latest preprint version Abstract Chemical sensors provide new solutions to address some of the world’s biggest challenges, including climate change, energy and healthcare. Understanding molecule binding kinetics and thermodynamics is essential in enhancing the design and functionality of chemical sensors. To contribute to this field, we have developed a numerical framework to predict the binding kinetics without requiring experimental inputs. Once the target molecules and fictional surface are identified, the details alongside the environment and mass transport are included as input to this code. The output would be the predictive model of target molecule behaviour passing by the surface. This framework comprises an all-atom molecular dynamics model and a Bayesian machine learning model for predicting affinity. Different predictive models have been trained, and the Bayesian-based Gaussian process regression (GPR) best predicts the binding reaction amongst them all. The predictive model is validated for an aluminium-based platform. The proposed numerical framework has the potential to be generalised and, therefore, contribute to future low-cost binding reaction estimations, providing a valuable tool for industry and experimentalists. Physical sciences/Engineering/Mechanical engineering Physical sciences/Mathematics and computing/Computational science Full Text Additional Declarations No competing interests reported. Supplementary Files PaperforScientificReportsSupplimentary.pdf Cite Share Download PDF Status: Published Journal Publication published 03 Sep, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 10 Feb, 2024 Reviews received at journal 09 Feb, 2024 Reviewers agreed at journal 08 Feb, 2024 Reviewers agreed at journal 01 Feb, 2024 Reviewers invited by journal 26 Jan, 2024 Editor assigned by journal 26 Jan, 2024 Editor invited by journal 22 Jan, 2024 Submission checks completed at journal 22 Jan, 2024 First submitted to journal 19 Jan, 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. 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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-3879169","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":268521164,"identity":"9e46f85e-5fab-4fdf-8ec0-9baf0f854446","order_by":0,"name":"Fatemeh Shahbazi","email":"data:image/png;base64,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","orcid":"","institution":"University of Warwick","correspondingAuthor":true,"prefix":"","firstName":"Fatemeh","middleName":"","lastName":"Shahbazi","suffix":""},{"id":268521165,"identity":"612ae785-9666-4afc-8759-1d4918b5cc9c","order_by":1,"name":"Mohammad Nasr Esfahani","email":"","orcid":"","institution":"University of York","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Nasr","lastName":"Esfahani","suffix":""},{"id":268521166,"identity":"f5388e0d-7cea-4e8b-8351-79ca639b1cca","order_by":2,"name":"Amir Keshmiri","email":"","orcid":"","institution":"University of Manchester","correspondingAuthor":false,"prefix":"","firstName":"Amir","middleName":"","lastName":"Keshmiri","suffix":""},{"id":268521167,"identity":"65f3ea11-0ad6-45c6-b05f-5947d86f8676","order_by":3,"name":"Masoud Jabbari","email":"","orcid":"","institution":"University of Leeds","correspondingAuthor":false,"prefix":"","firstName":"Masoud","middleName":"","lastName":"Jabbari","suffix":""}],"badges":[],"createdAt":"2024-01-19 15:44:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3879169/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3879169/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-71007-z","type":"published","date":"2024-09-03T16:08:22+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64186217,"identity":"69d6df4f-bb38-4cc5-a52e-614fdda2508e","added_by":"auto","created_at":"2024-09-09 16:25:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1898113,"visible":true,"origin":"","legend":"","description":"","filename":"PaperforScientificReportsMachineLearningAidedMolecularDynamicsSimulationforPredictionofBindingKinetics.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3879169/v1_covered_4e6016b7-4ec6-42d3-bd7d-9096e5b3f586.pdf"},{"id":50093428,"identity":"fcf4461d-0579-48e0-966b-805350554482","added_by":"auto","created_at":"2024-01-24 12:40:17","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":7623143,"visible":true,"origin":"","legend":"","description":"","filename":"PaperforScientificReportsSupplimentary.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3879169/v1/ddebe903701c540a79c9b429.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Machine Learning-Aided Molecular Dynamics Simulation for Prediction of Binding Kinetics","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3879169/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3879169/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Chemical sensors provide new solutions to address some of the world’s biggest challenges, including climate change, energy and healthcare. 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