Machine Learning Enables a Top-Down Approach to Mechanistic Elucidation in Asymmetric Catalysis

General
reaction behavior is rarely reported in asymmetric catalysis,
not simply because it is difficult to achieve, but also due to the
methods used for its identification and study. Traditional approaches
involve compartmentalization, where the impact of individual components
is initially analyzed, followed by assimilation using simple response
and structure matching techniques. However, extending this method
to accommodate complex conditions and diverse reactions proves challenging.
Here, we present a data-driven method that relies on clusterwise linear
regression to derive and predictively apply general mechanistic models
of enantioinduction, with minimal human intervention. When applied
to the palladium-catalyzed decarboxylative asymmetric allylic alkylation
(DAAA) reaction, unexpected interactions governing enantioselectivity
are revealed, supported by high-level computations and additional
experiments. Our results demonstrate this workflow as a powerful tool
for automating mechanistic elucidation and effectively identifying
general reaction performance.