AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning
This work addresses interpretability and generalization issues in chemistry prediction for researchers in radical and atmospheric chemistry, though it is incremental as it applies existing methods to a new domain.
The paper tackles the problem of deep learning-based reaction predictors lacking interpretability and generalization to radical chemistry by introducing RMechRP, a system using contrastive learning with mechanistic pathways. The results show it provides accurate and interpretable predictions for radical reactions, establishing the first benchmark for this domain.
Deep learning-based reaction predictors have undergone significant architectural evolution. However, their reliance on reactions from the US Patent Office results in a lack of interpretable predictions and limited generalization capability to other chemistry domains, such as radical and atmospheric chemistry. To address these challenges, we introduce a new reaction predictor system, RMechRP, that leverages contrastive learning in conjunction with mechanistic pathways, the most interpretable representation of chemical reactions. Specifically designed for radical reactions, RMechRP provides different levels of interpretation of chemical reactions. We develop and train multiple deep-learning models using RMechDB, a public database of radical reactions, to establish the first benchmark for predicting radical reactions. Our results demonstrate the effectiveness of RMechRP in providing accurate and interpretable predictions of radical reactions, and its potential for various applications in atmospheric chemistry.