Extended Connectivity Fingerprints as a Chemical Reaction Representation for Enantioselective Organophosphorus-Catalyzed Asymmetric Reaction Prediction

Predicting the outcomes of organic reactions using data-driven
approaches aids in the acceleration of research. In laboratory-scale
experiments, only a small number of reaction data can be accessed
for machine learning model construction, where reaction representations
play a pivotal role in the success of model construction. Nevertheless,
representation comparison for a small data set is not adequate. Herein,
focusing on the enantioselectivity of phosphoric-acid-catalyzed reactions,
various two-dimensional and three-dimensional reaction representations
(descriptors) were compared. Overall, the concatenated form of the
extended connectivity fingerprints showed the best predictive capability
for the two types of data sets: high-throughput experimental data
and manually collected literature data sets. Furthermore, highlighting
the substructure contribution to the prediction outcome was shown
to be informative for guiding catalyst development.