posted on 2024-03-13, 13:03authored byNatalie
P. Romer, Daniel S. Min, Jason Y. Wang, Richard C. Walroth, Kyle A. Mack, Lauren E. Sirois, Francis Gosselin, Daniel Zell, Abigail G. Doyle, Matthew S. Sigman
Herein
we report a method for a stereoconvergent synthesis of trisubstituted
alkenes in two steps from simple ketone starting materials. The key
step is a nickel-catalyzed reduction of the corresponding enol tosylates
that predominantly relies on a monophosphine ligand to direct the
stereoconvergent formation of either the E- or Z-trisubstituted alkene products. Reaction optimization
was accomplished using a data science workflow including monophosphine
training set design, statistical modeling, and multiobjective Bayesian
optimization. The optimization campaign significantly improved access
to both the E- and Z-trisubstituted
products in up to ∼90:10 diastereoselectivity and >90% yield.
After identifying superior ligands using training set design, only
25 reactions were required for each objective (E-
and Z-isomer formation) to converge on improved reaction
parameters from a search space of ∼30,000 potential conditions
using the EDBO+ platform. Additionally, a hierarchical machine learning
model was developed to predict the stereoselectivity of untested monophosphine
ligands to achieve a validation mean absolute error (MAE) of 7.1%
selectivity (0.21 kcal/mol). Ultimately, we present a synergistic
data science workflow leveraging the integration of training set design,
statistical modeling, and Bayesian optimization, thereby expanding
access to stereodefined trisubstituted alkenes.