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CGR generation-arXiv_P_17.01.2020.pdf (1.85 MB)

Discovery of Novel Chemical Reactions by Deep Generative Recurrent Neural Network

submitted on 17.01.2020, 09:31 and posted on 22.01.2020, 05:26 by William Bort, Igor I. Baskin, Pavel Sidorov, Gilles Marcou, Dragos Horvath, Timur Madzhidov, Alexandre Varnek, Timur Gimadiev, Ramil Nugmanov, Artem Mukanov
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.


Email Address of Submitting Author


University of Strasbourg



ORCID For Submitting Author


Declaration of Conflict of Interest