Holistic Prediction of Enantioselectivity in Asymmetric Catalysis

When faced with unfamiliar reaction space, synthetic chemists typically apply reported conditions (reagents, catalyst, solvent, additives) from closely-related reactions to new substrate types. Unfortunately, this approach often fails due to subtle, albeit important, differences in reaction requirements. Consequently, a significant goal in synthetic chemistry is the ability to transfer chemical observations from one reaction to another, quantitatively. Here, we present such a platform by developing a holistic, data-driven workflow for deriving statistical models for one set of reactions that can be applied to predict out-of-sample examples. As a validating case study, published enantioselectivity data sets that employ BINOL-derived chiral phosphoric acids for a range of nucleophilic addition reactions to imines were combined and statistical models developed. These models reveal the general interactions imparting asymmetric induction and allow the quantitative transfer of this information to new reaction components. The disclosed techniques create opportunities for translating comprehensive reaction analysis to diverse chemical space, streamlining both catalyst and reaction development.