Graph Transformation Policy Network for Chemical Reaction Prediction

Understand

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 a graph, and the process of generating product molecules from reactant molecules can be formulated as a sequence 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 the 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.