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A graph-convolutional neural network model for the prediction of chemical reactivity
Name
c8sc04228d.pdf
Description
Published version
Size
1.94 MB
Format
Adobe PDF
Checksum (MD5)
7a9060c0b49ff7e7884ee43e8ec94d00
Author(s)
Coley, Connor W
Jin, Wengong
Rogers, Luke
Jamison, Timothy F
Jaakkola, Tommi S
Green, William H
Barzilay, Regina
Jensen, Klavs F
Date Issued
2019
Journal
Chemical Science
Publisher
Royal Society of Chemistry (RSC)
Version
Final published version
Abstract
© 2019 The Royal Society of Chemistry. We present a supervised learning approach to predict the products of organic reactions given their reactants, reagents, and solvent(s). The prediction task is factored into two stages comparable to manual expert approaches: considering possible sites of reactivity and evaluating their relative likelihoods. By training on hundreds of thousands of reaction precedents covering a broad range of reaction types from the patent literature, the neural model makes informed predictions of chemical reactivity. The model predicts the major product correctly over 85% of the time requiring around 100 ms per example, a significantly higher accuracy than achieved by previous machine learning approaches, and performs on par with expert chemists with years of formal training. We gain additional insight into predictions via the design of the neural model, revealing an understanding of chemistry qualitatively consistent with manual approaches.
Terms of Use
Creative Commons Attribution 3.0 unported license
Persistent DSpace Link
DOI of Published Version
10.1039/c8sc04228d
