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A graph-convolutional neural network model for the prediction of chemical reactivity

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Abstract

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.

Graphical abstract: A graph-convolutional neural network model for the prediction of chemical reactivity

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Publication details

The article was received on 22 Sep 2018, accepted on 23 Nov 2018 and first published on 26 Nov 2018


Article type: Edge Article
DOI: 10.1039/C8SC04228D
Citation: Chem. Sci., 2019, Advance Article
  • Open access: Creative Commons BY license
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    A graph-convolutional neural network model for the prediction of chemical reactivity

    Connor W. Coley, W. Jin, L. Rogers, T. F. Jamison, T. S. Jaakkola, W. H. Green, R. Barzilay and K. F. Jensen, Chem. Sci., 2019, Advance Article , DOI: 10.1039/C8SC04228D

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