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Abstract

Accurate prediction of chemical reaction performance is crucial for automated chemical synthesis. In this study, a novel multi-modal chemical reaction prediction model is proposed, using graph and textual information, eliminating the need for computationally intensive DFT parameters with capability to handling reactions involving a fluctuating number of molecules. This model outperforms at least 7 generalized methods on 4 datasets, while maintaining chemical interpretability towards atomic and feature importance. Further details are comprehensively discussed in the article by Li et al. on pages 1230—1238.