Enhancing atom mapping with multitask learning and symmetry-aware deep graph matching
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 bo...
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| Format: | Article |
| Language: | English |
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BMC
2025-05-01
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| Series: | Journal of Cheminformatics |
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| Online Access: | https://doi.org/10.1186/s13321-025-01030-3 |
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| _version_ | 1850231144474214400 |
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| author | Maryam Astero Juho Rousu |
| author_facet | Maryam Astero Juho Rousu |
| author_sort | Maryam Astero |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-46a9a175efe74b28bd12bfb3f8120c5e |
| institution | OA Journals |
| issn | 1758-2946 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Cheminformatics |
| spelling | doaj-art-46a9a175efe74b28bd12bfb3f8120c5e2025-08-20T02:03:38ZengBMCJournal of Cheminformatics1758-29462025-05-0117111710.1186/s13321-025-01030-3Enhancing atom mapping with multitask learning and symmetry-aware deep graph matchingMaryam Astero0Juho Rousu1Computer Science, Aalto UniversityComputer Science, Aalto UniversityAbstract 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.https://doi.org/10.1186/s13321-025-01030-3Atom mappingGraph matchingMultitask learningGraph representation learning |
| spellingShingle | Maryam Astero Juho Rousu Enhancing atom mapping with multitask learning and symmetry-aware deep graph matching Journal of Cheminformatics Atom mapping Graph matching Multitask learning Graph representation learning |
| title | Enhancing atom mapping with multitask learning and symmetry-aware deep graph matching |
| title_full | Enhancing atom mapping with multitask learning and symmetry-aware deep graph matching |
| title_fullStr | Enhancing atom mapping with multitask learning and symmetry-aware deep graph matching |
| title_full_unstemmed | Enhancing atom mapping with multitask learning and symmetry-aware deep graph matching |
| title_short | Enhancing atom mapping with multitask learning and symmetry-aware deep graph matching |
| title_sort | enhancing atom mapping with multitask learning and symmetry aware deep graph matching |
| topic | Atom mapping Graph matching Multitask learning Graph representation learning |
| url | https://doi.org/10.1186/s13321-025-01030-3 |
| work_keys_str_mv | AT maryamastero enhancingatommappingwithmultitasklearningandsymmetryawaredeepgraphmatching AT juhorousu enhancingatommappingwithmultitasklearningandsymmetryawaredeepgraphmatching |