Issue 45, 2020

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

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

Article information

Article type
Paper
Submitted
08 sep. 2020
Accepted
05 nóv. 2020
First published
05 nóv. 2020

Phys. Chem. Chem. Phys., 2020,22, 26478-26486

Author version available

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

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements