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Graph_Neural_Network_for_Atom_Mapping__ACS2.pdf (4.9 MB)

DRACON: Disconnected Graph Neural Network for Atom Mapping in Chemical Reactions

revised on 16.07.2020, 14:55 and posted on 17.07.2020, 07:20 by Filipp Nikitin, Olexandr Isayev, Vadim Strijov

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 reaction outcome and atom-mapping during a chemical transformation. 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.


NSF CHE-1802789


Email Address of Submitting Author


Carnegie Mellon University


United States

ORCID For Submitting Author


Declaration of Conflict of Interest