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Issue 45, 2020
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DRACON: disconnected graph neural network for atom mapping in chemical reactions

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Abstract

Machine learning solved many challenging problems in computer-assisted synthesis prediction (CASP). We formulate a reaction prediction problem in terms of node-classification in a disconnected graph of source molecules and generalize a graph convolution neural network for disconnected graphs. Here we demonstrate that our approach can successfully predict centres of reaction and atoms of the main product. A set of experiments using the USPTO dataset demonstrates excellent performance and interpretability of the proposed model. Implicitly learned latent vector representation of chemical reactions strongly correlates with the class of the chemical reaction. Reactions with similar templates group together in the latent vector space.

Graphical abstract: DRACON: disconnected graph neural network for atom mapping in chemical reactions

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Article information


Submitted
08 Sep 2020
Accepted
05 Nov 2020
First published
05 Nov 2020

Phys. Chem. Chem. Phys., 2020,22, 26478-26486
Article type
Paper

DRACON: disconnected graph neural network for atom mapping in chemical reactions

F. Nikitin, O. Isayev and V. Strijov, Phys. Chem. Chem. Phys., 2020, 22, 26478
DOI: 10.1039/D0CP04748A

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