Title: Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network,
Abstract: The prediction of organic reaction outcomes is a fundamental problem in
computational chemistry. Since a reaction may involve hundreds of atoms, fully
exploring the space of possible transformations is intractable. The current
solution utilizes reaction templates to limit the space, but it suffers from
coverage and efficiency issues. In this paper, we propose a template-free
approach to efficiently explore the space of product molecules by first
pinpointing the reaction center -- the set of nodes and edges where graph edits
occur. Since only a small number of atoms contribute to reaction center, we can
directly enumerate candidate products. The generated candidates are scored by a
Weisfeiler-Lehman Difference Network that models high-order interactions
between changes occurring at nodes across the molecule. Our framework
outperforms the top-performing template-based approach with a 10\% margin,
while running orders of magnitude faster. Finally, we demonstrate that the
model accuracy rivals the performance of domain experts.