Learning Hierarchical Representations for Explainable Chemical Reaction Prediction

Abstract

This paper aims to propose an explainable and generalized chemical reaction representation method for accelerating the evaluation of the chemical processes in production. To this end, we designed an explainable coarse-fine level representation model that incorporates a small amount of easily available expert knowledge (i.e., coarse-level annotations) into the deep learning method to effectively improve the performances on reaction representation related tasks. We also developed a new probabilistic data augmentation strategy with contrastive learning to improve the generalization of our model. We conducted experiments on the Schneider 50k and the USPTO 1k TPL datasets for chemical reaction classification, as well as the USPTO yield dataset for yield prediction. The experimental results showed that our method outperforms the state of the art by just using a small-scale dataset annotated with both coarse-level and fine-level labels to pretrain the model.