Enhancing atom mapping with multitask learning and symmetry-aware deep graph matching

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. This study introduces SAMMNet, a novel Symmetry-Aware Multitask Atom Mapping Network, advancing atom mapping methodologies by integrating multitask learning and post-prediction symmetry refinement. Unlike prior approaches, SAMMNet leverages auxiliary self-supervised tasks to enhance molecular graph representations, improving mapping accuracy while addressing imbalanced reactions through graph padding techniques.