  Stereoselective reactions (both chemical and enzymatic reactions) have been
essential for origin of life, evolution, human biology and medicine. Since late
1960s, there have been numerous successes in the exciting new frontier of
asymmetric catalysis. However, most industrial and academic asymmetric
catalysis nowadays do follow the trial-and-error model, since the energetic
difference for success or failure in asymmetric catalysis is incredibly small.
Our current understanding about stereoselective reactions is mostly qualitative
that stereoselectivity arises from differences in steric effects and electronic
effects in multiple competing mechanistic pathways. Quantitatively
understanding and modulating the stereoselectivity of for a given chemical
reaction still remains extremely difficult. As a proof of principle, we herein
present a novel machine learning technique, which combines a LASSO model and
two Random Forest model via two Gaussian Mixture models, for quantitatively
predicting stereoselectivity of chemical reactions. Compared to the recent
ground-breaking approach [1], our approach is able to capture interactions
between features and exploit complex data distributions, which are important
for predicting stereoselectivity. Experimental results on a recently published
dataset demonstrate that our approach significantly outperform [1]. The insight
obtained from our results provide a solid foundation for further exploration of
other synthetically valuable yet mechanistically intriguing stereoselective
reactions.
