Enhancing Atom Mapping with Multitask Learning and Symmetry-Aware Deep Graph Matching

Enhancing Atom Mapping with Multitask Learning and Symmetry-Aware 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 Method Article Enhancing Atom Mapping with Multitask Learning and Symmetry-Aware Deep Graph Matching Maryam Astero, Juho Rousu This is a preprint; it has not been peer reviewed by a journal.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-5664604/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 May, 2025 Read the published version in Journal of Cheminformatics → Version 1 posted 4 You are reading this latest preprint version Abstract Atom mapping involves identifying the correspondence between individual atoms in reactant molecules and their counterparts in product molecules. This process is crucial for gaining deeper insight into reaction mechanisms, such as defining reaction templates and determining which chemical bonds are formed or broken during a reaction. However, reliable atom mapping data are often limited or incomplete within chemical databases, rendering manual annotation impractical for large-scale datasets. To address this limitation, we propose the Symmetry-Aware Multitask Atom Mapping Network (SAMMNet), a model designed to automatically infer atom correspondences by incorporating an auxiliary self-supervised task during training. SAMMNet employs molecular graph representations and leverages graph neural networks to capture both general and task-specific features, enabling enhanced predictive performance. Our experimental results demonstrate that the multitask learning framework, coupled with symmetry-aware atom mapping, improves accuracy and robustness in atom mapping predictions. This makes our method a promising advancement for computational chemistry and related fields. Atom mapping graph matching multitask learning graph representation learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 30 May, 2025 Read the published version in Journal of Cheminformatics → Version 1 posted Editorial decision: Revision requested 24 Dec, 2024 Editor assigned by journal 18 Dec, 2024 Submission checks completed at journal 18 Dec, 2024 First submitted to journal 17 Dec, 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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