Predicting and optimizing asymmetric catalyst performance using the principles of experimental design and steric parameters.

Using a modular amino acid based chiral ligand motif, a library of ligands was synthesized systematically varying the substituents at two positions. The effects of these changes on ligand structure were probed in the enantioselective allylation of benzaldehyde, acetophenone, and methylethyl ketone under Nozaki-Hiyama-Kishi conditions. The resulting three-dimensional datasets allowed for the construction of mathematical surface models which describe the interplay of substituent effects on enantioselectivity for a given reaction. The surface models were both extrapolated and manipulated to predict the enantioselective outcomes of several previously untested ligands. Analyses were also used to predict optimal ligand structure of a minimal dataset. Within the dataset, a linear free energy relationship was also discovered and a direct comparison of both the linear prediction as well as the three-dimensional prediction illustrates the potential predictive power of using a three-dimensional model approach to asymmetric catalyst development.