DRACON: disconnected graph neural network for atom mapping in chemical reactions. Issue 45 (13th November 2020)

• Record Type: Journal Article
• Authors: Nikitin, Filipp; Isayev, Olexandr; Strijov, Vadim
• Is Part Of: Physical chemistry chemical physics. Volume 22:Issue 45(2020)
• Page Start: 26478
• Page End: 26486
• Publication Date: 2020-11-13
• Subjects: Chemistry, Physical and theoretical -- Periodicals; 541.3
• DOI: 10.1039/d0cp04748a
• Languages: English
• ISSNs: 1463-9076

Abstract

Abstract : 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. 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.