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

17 July 2020, Version 2
This content is a preprint and has not undergone peer review at the time of posting.


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.


computer-assisted synthesis prediction
disconnected graphs
synthesis planning


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