Chemical reactions are a complex process, as they involve interaction between several molecular compounds. As a result, predicting the success of a reaction is a non-trivial task, which often requires running several experiments in the lab. This process is is expensive, time consuming, and inefficient. As a result, in recent years, researchers have explored the use of machine learning algorithms to predict reaction success. These methods mainly rely on chemical properties of the molecules involved in the reactions. Despite their promising success, none of existing methods explored the use of structural properties of molecules in predicting reaction success. In this work, we develop an Attributed Graph Neural Network model that integrates both structural properties as well as chemical properties of molecules for predicting reaction success. Our model shows remarkable performance on two hand-crafted datasets obtained from high-throughput experiments, as well as one real-world dataset.
This document will be frequently updated. This is version 0.0 of the manuscript.