Predicting organic reaction outcomes with weisfeiler-lehman network
Name
6854-predicting-organic-reaction-outcomes-with-weisfeiler-lehman-network.pdf
Description
Published version
Size
1.87 MB
Format
Adobe PDF
Checksum (MD5)
5e6a01612e71527a5c59bd9c01d42b1a
Author(s)
Jin, Wengong
Coley, Connor Wilson
Barzilay, Regina
Jaakkola, Tommi S
Date Issued
December 2017
Journal
Advances in Neural Information Processing Systems 30 (NIPS 2017)
Publisher
Neural Information Processing Systems Foundation, Inc.
Citation
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
Version
Final published version
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.
MIT Department
Massachusetts 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
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://papers.nips.cc/paper/6854-predicting-organic-reaction-outcomes-with-weisfeiler-lehman-network
