A Data-Driven Workflow for Assigning and Predicting Generality in Asymmetric Catalysis

The development of chiral catalysts that can provide
high enantioselectivities
across a wide assortment of substrates or reaction range is a priority
for many catalyst design efforts. While several approaches are available
to aid in the identification of general catalyst systems, there is
currently no simple procedure for directly measuring how general a
given catalyst could be. Herein, we present a catalyst-agnostic workflow
centered on unsupervised machine learning that enables the rapid assessment
and quantification of catalyst generality. The workflow uses curated
literature data sets and reaction descriptors to visualize and cluster
chemical space coverage. This reaction network can then be applied
to derive a catalyst generality metric through designer equations
and interfaced with other regression techniques for general catalyst
prediction. As validating case studies, we have successfully applied
this method to identify-through-quantification the most general catalyst
chemotype for an organocatalytic asymmetric Mannich reaction and predicted
the most general chiral phosphoric acid catalyst for the addition
of nucleophiles to imines. The mechanistic basis for catalyst generality
can then be gleaned from the calculated values by deconstructing the
contributions of chemical space and enantiomeric excess to the overall
result. Finally, our generality techniques permitted the development
of mechanistically informative catalyst screening sets that allow
experimentalists to rationally select catalysts that have the highest
probability of achieving a good result in the first round of reaction
development. Overall, our findings represent a framework for interrogating
catalyst generality, and this strategy should be relevant to other
catalytic systems widely applied in asymmetric synthesis.