Data-Driven Chemical Reaction Classification, Fingerprinting and Clustering using Attention-Based Neural Networks

Schwaller, Philippe; Probst, Daniel; Vaucher, Alain C.; Nair, Vishnu H; Laino, Teodoro; Reymond, Jean-Louis (26 December 2019). Data-Driven Chemical Reaction Classification, Fingerprinting and Clustering using Attention-Based Neural Networks ChemRxiv 10.26434/chemrxiv.9897365.v2

Preview

Text ChemRxiv preprint.pdf - Published Version Available under License Creative Commons: Attribution-Noncommercial-No Derivative Works (CC-BY-NC-ND). Download (8MB) | Preview

Organic reactions are usually assigned to classes grouping reactions with similar reagents and mechanisms. The classification process is a tedious task, requiring first an accurate mapping of the reaction (atom mapping) followed by the identification of the corresponding reaction class template. In this work, we present two transformer-based models that infer reaction classes from the SMILES representation of chemical reactions. Our best model reaches a classification accuracy of 98.2%. We study the incorrect predictions of the models and show that they reveal different biases and mistakes in the underlying data set. Using the embeddings of our classification model, we introduce reaction fingerprints that do not require knowing the reaction center or distinguishing between reactants and reagents. This conversion from chemical reactions to feature vectors enables efficient clustering and similarity search in the reaction space. We compare the reaction clustering for combinations of self-supervised, supervised, and molecular shingle-based reaction representations.

Interest & Impact

Downloads

266 since deposited on 06 Apr 2020
56 in the past 12 months

Citations

5 Citations in Web of Science ®
6 Citations in Scopus

Search

in Google Scholar™

Services

Actions (login required)

Edit item

Actions (login required)

Edit item