SynCat: A light weight graph neural network model to classify chemical reactions

Chemical reactions typically follow mechanistic templates and hence fall into a manageable number of clearly distinguishable classes that usually labeled by names of chemists who discovered or explored them. These ``named reactions'' form the core of reaction ontologies and are associated with specific synthetic procedures. Classification of chemical reactions, therefore, is an essential step for the construction and maintenance of reaction-template databases, in particular for the purpose of synthetic route planning. Large-scale reaction databases, however, typically do not annotate named reactions systematically. Although many methods have been proposed, most are sensitive to reagent variations and do not guarantee permutation invariance. Here, we propose SynCat, a graph‐based framework that leverages molecule‐level cross‐attention to perform precise reagent detection and role assignment, eliminating unwanted species. SynCat ensures permutation invariance by employing a pairwise summation of participant embeddings. This method balances mechanistic specificity derived from individual-molecule embeddings with the order-independent nature of the pairwise representation. Across multiple benchmark datasets, SynCat outperformed established reaction fingerprints DRFP and RXNFP, achieving a mean classification accuracy of 0.988, together with enhanced scalability.