Learning Chemical Reaction Representation with Reactant-Product Alignment

Abstract: Organic synthesis stands as a cornerstone of the chemical industry. The development of robust machine learning models to support tasks associated with organic reactions is of significant interest. However, current methods rely on hand-crafted features or direct adaptations of model architectures from other domains, which lack feasibility as data scales increase or ignore the rich chemical information inherent in reactions. To address these issues, this paper introduces RAlign, a novel chemical reaction representation learning model for various organic reaction-related tasks. By integrating atomic correspondence between reactants and products, our model discerns the molecular transformations that occur during the reaction, thereby enhancing comprehension of the reaction mechanism. We have designed an adapter structure to incorporate reaction conditions into the chemical reaction representation, allowing the model to handle various reaction conditions and to adapt to various datasets and downstream tasks. Additionally, we introduce a reaction-center-aware attention mechanism that enables the model to concentrate on key functional groups, thereby generating potent representations for chemical reactions. Our model has been evaluated on a range of downstream tasks. Experimental results indicate that our model markedly outperforms existing chemical reaction representation learning architectures on most of the datasets. We plan to open-source the code contingent upon the acceptance of the paper.