A graph-convolutional neural network model for the prediction of chemical reactivity.
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ABSTRACT:
SUBMITTER: Coley CW
PROVIDER: S-EPMC6335848 | biostudies-literature | 2019 Jan
REPOSITORIES: biostudies-literature
Publications
A graph-convolutional neural network model for the prediction of chemical reactivity.
Coley Connor W CW Jin Wengong W Rogers Luke L Jamison Timothy F TF Jaakkola Tommi S TS Green William H WH Barzilay Regina R Jensen Klavs F KF
Chemical science 20181126 2
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 reactiv ...[more]
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