AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways
  via Contrastive Learning
Mohammadamin Tavakoli Yin Ting T. Chiu Alexander Shmakov Ann Marie Carlton David Van Vranken Pierre Baldi
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.