Learning Chemical Rules of Retrosynthesis with Pre-Training

Abstract

Retrosynthesis aided by artificial intelligence has been a very active and bourgeoning area of research, for its critical role in drug discovery as well as material science. Three categories of solutions, i.e., template-based, template-free, and semi-template methods, constitute mainstream solutions to this problem. In this paper, we focus on template-free methods which are known to be less bothered by the template generalization issue and the atom mapping challenge. Among several remaining problems regarding template-free methods, failing to conform to chemical rules is pronounced. To address the issue, we seek for a pre-training solution to empower the pre-trained model with chemical rules encoded. Concretely, we enforce the atom conservation rule via a molecule reconstruction pre-training task, and the reaction rule that dictates reaction centers via a reaction type guided contrastive pre-training task. In our empirical evaluation, the proposed pre-training solution substantially improves the single-step retrosynthesis accuracies in three downstream datasets.