Predicting organic reaction outcomes with weisfeiler-lehman network
Author(s)Jin, Wengong; Coley, Connor Wilson; Barzilay, Regina; Jaakkola, Tommi S
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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.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Chemical Engineering; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Advances in Neural Information Processing Systems 30 (NIPS 2017)
Neural Information Processing Systems Foundation, Inc.
Jin, Wengong et al. "Predicting organic reaction outcomes with weisfeiler-lehman network." Advances in Neural Information Processing Systems 30 (NIPS 2017), December 2017, Long Beach, California, Neural Information Processing Systems Foundation, 2017. © 2017 Neural Information Processing Systems Foundation
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