Graph Transformation Policy Network for Chemical Reaction Prediction

We address a fundamental problem in chemistry known as chemical reaction product prediction. Our main insight is that the input reactant and reagent molecules can be jointly represented as graphs, and the process of generating product molecules from reactant molecules can be formulated as a set of graph transformations. To this end, we propose Graph Transformation Policy Network (GTPN) a novel generic method that combines the strengths of graph neural networks and reinforcement learning to learn reactions directly from data with minimal chemical knowledge. Compared to previous methods, GTPN has some appealing properties such as: end-to-end learning, and making no assumption about the length or the order of graph transformations. In order to guide our model search through the complex discrete space of sets of graph transformations effectively, we extend the standard policy gradient loss by adding useful constraints. Evaluation results show that GTPN improves the top-1 accuracy over the current state-of-the-art method by about 3% on the large USPTO dataset.