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Stereoselective reactions (both chemical and enzymatic reactions) have beenessential for origin of life, evolution, human biology and medicine. Since late1960s, there have been numerous successes in the exciting new frontier ofasymmetric catalysis. However, most industrial and academic asymmetriccatalysis nowadays do follow the trial-and-error model, since the energeticdifference for success or failure in asymmetric catalysis is incredibly small.Our current understanding about stereoselective reactions is mostly qualitativethat stereoselectivity arises from differences in steric effects and electroniceffects in multiple competing mechanistic pathways. Quantitativelyunderstanding and modulating the stereoselectivity of for a given chemicalreaction still remains extremely difficult. As a proof of principle, we hereinpresent a novel machine learning technique, which combines a LASSO model andtwo Random Forest model via two Gaussian Mixture models, for quantitativelypredicting stereoselectivity of chemical reactions. Compared to the recentground-breaking approach [1], our approach is able to capture interactionsbetween features and exploit complex data distributions, which are importantfor predicting stereoselectivity. Experimental results on a recently publisheddataset demonstrate that our approach significantly outperform [1]. The insightobtained from our results provide a solid foundation for further exploration ofother synthetically valuable yet mechanistically intriguing stereoselectivereactions.

Predicting the Stereoselectivity of Chemical Transformations by Machine Learning