Discovery of Novel Chemical Reactions by Deep Generative Recurrent Neural Network

22 January 2020, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Here, we report an application of Artificial Intelligence techniques to generate novel chemical reactions of the given type. A sequence-to-sequence autoencoder was trained on the USPTO reaction database. Each reaction was converted into a single Condensed Graph of Reaction (CGR), followed by their translation into on-purpose developed SMILES/GGR text strings. The autoencoder latent space was visualized on the two-dimensional generative topographic map, from which some zones populated by Suzuki coupling reactions were targeted. These served for the generation of novel reactions by sampling the latent space points and decoding them to SMILES/CGR.

Keywords

chemical reaction generation
sequence-to-sequence autoencoder
deep learning
Condensed Graph of Reaction
Generative Topographic Mapping

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