Learning symmetry-aware atom mapping in chemical reactions through deep graph matching

Learning symmetry-aware atom mapping in chemical reactions through deep graph matching | 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 Learning symmetry-aware atom mapping in chemical reactions through deep graph matching Maryam Astero, Juho Rousu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3865935/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Apr, 2024 Read the published version in Journal of Cheminformatics → Version 1 posted 7 You are reading this latest preprint version Abstract Accurate atom mapping, which establishes correspondences between atoms in reactants and products, is a crucial step in analyzing chemical reactions. In this paper, we present a novel end-to-end approach that formulates the atom mapping problem as a deep graph matching task. Our proposed model, AMNet (Atom Matching Network), utilizes molecular graph representations and employs various atom and bond features using graph neural networks to capture the intricate structural characteristics of molecules, ensuring precise atom correspondence predictions. Notably, AMNet incorporates the consideration of molecule symmetry, enhancing accuracy while simultaneously reducing computational complexity. The integration of the Weisfeiler-Lehman isomorphism test for symmetry identification refines the model's predictions. Furthermore, our model maps the entire atom in a chemical reaction, offering a comprehensive approach beyond focusing solely on the main molecules in reactions. We evaluated the performance of the model on a subset of USPTO reaction datasets, and the result reveals an average accuracy of 97.3% on mapped atoms and a top@10 accuracy of 99.7%, indicating 99.7% of reactions are correctly mapped when the correct mapped atom is in the first top 10 predicted atom. Atom mapping graph matching deep learning graph representation learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 22 Apr, 2024 Read the published version in Journal of Cheminformatics → Version 1 posted Editorial decision: Revision requested 23 Feb, 2024 Reviews received at journal 06 Feb, 2024 Reviewers agreed at journal 27 Jan, 2024 Reviewers invited by journal 27 Jan, 2024 Submission checks completed at journal 17 Jan, 2024 Editor assigned by journal 17 Jan, 2024 First submitted to journal 15 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. 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-3865935","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":267567222,"identity":"7e4fbddd-2e8b-4730-a822-c79843ab30a9","order_by":0,"name":"Maryam Astero","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYPCDCgYGPjBtQIxqNhBxBkiDGGeI1sLYBtOCR6Fu+9lnEj9zGOTl5zcf+/Bxnp08m3zzAYYDBbi1mJ1JN5Ps3cZguOEYW/LMmduSDdvY2BIYDuBxmNmBNDYJ3m0MjBvYeIyZebcxM7ax8Rgwf8Cn5fwzNsm/2xjs57fxf2bmnVNvD9KC35YbaWzSQFsSG47xMDPzNhxOJELLM2Zr2W0SyRuOpRkzzjh2PLmNLS3hAF4t59MYb77dZmM7v/nwY4YPNdW2/cyHDz448Ae3FiBgkWBgkEAVOoBXAwMD8wcCCkbBKBgFo2CkAwA6fkr870vMOgAAAABJRU5ErkJggg==","orcid":"","institution":"Aalto University","correspondingAuthor":true,"prefix":"","firstName":"Maryam","middleName":"","lastName":"Astero","suffix":""},{"id":267567223,"identity":"79921a0e-885a-46db-b441-1bd6bff73f7e","order_by":1,"name":"Juho Rousu","email":"","orcid":"","institution":"Aalto University","correspondingAuthor":false,"prefix":"","firstName":"Juho","middleName":"","lastName":"Rousu","suffix":""}],"badges":[],"createdAt":"2024-01-15 08:59:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3865935/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3865935/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13321-024-00841-0","type":"published","date":"2024-04-22T22:08:12+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":55689533,"identity":"081f002e-ab12-47cd-9c7e-91e38b316864","added_by":"auto","created_at":"2024-05-01 22:08:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":935072,"visible":true,"origin":"","legend":"","description":"","filename":"Atommapping.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3865935/v1_covered_1152a2cc-4c24-4ca1-bc2a-65680fc40fea.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Learning symmetry-aware atom mapping in chemical reactions through deep graph matching","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":"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":"Atom mapping, graph matching, deep learning, graph representation learning","lastPublishedDoi":"10.21203/rs.3.rs-3865935/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3865935/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Accurate atom mapping, which establishes correspondences between atoms in reactants and products, is a crucial step in analyzing chemical reactions. 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