Mapping the Space of Chemical Reactions using Attention-Based Neural Networks

11 December 2020, Version 4
This content is a preprint and has not undergone peer review at the time of posting.


Organic reactions are usually assigned to classes grouping reactions with similar reagents and mechanisms. Reaction classes facilitate communication of complex concepts and efficient navigation through chemical reaction space. However, the classification process is a tedious task, requiring the identification of the corresponding reaction class template via annotation of the number of molecules in the reactions, the reaction center and the distinction between reactants and reagents. In this work, we show that transformer-based models can infer reaction classes from non-annotated, simple text-based representations of chemical reactions. Our best model reaches a classification accuracy of 98.2%. We also show that the learned representations can be used as reaction fingerprints which capture fine-grained differences between reaction classes better than traditional reaction fingerprints. The unprecedented insights into chemical reaction space enabled by our learned fingerprints is illustrated by an interactive reaction atlas providing visual clustering and similarity searching.



Interactive reaction atlas:


machine learning
deep learning
organic chemistry
organic synthesis
SMILES-Encoded Molecular Structures
SMILES string representation
Chemical Reactions
Clustering analysis
reaction atlas
reaction fingerprints

Supplementary weblinks


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