Quantifying Structural Effects of Amino Acid Ligands in Pd(II)-Catalyzed Enantioselective C–H Functionalization Reactions

Delineating complex ligand effects on enantioselectivity is a longstanding challenge in asymmetric catalysis. With α-amino acid ligands, the essential difficulty lies in accurately describing integrated perturbations induced by simultaneous variation about the α side chain and N protecting group of the ligand, which hampers an intuitive understanding of the structure–enantioselectivity relationships. To deconvolute such complexity in chiral amino acid enabled enantioselective C–H functionalization reactions, a computational organometallic model system was developed. Whereas a model based only on a conventional results in diminished predictive power, the ground state Pd­(II)-based models display an excellent ability to describe the observed enantioselectivity. These structures were leveraged using a multivariate modeling approach to successfully describe Pd­(II)-catalyzed C–H alkylation, alkenylation, and two C–H arylation reactions, wherein descriptors of torsion angle, percent buried volume, and NBO charge showed quantitative relevance to predict enantiomeric excess. On the basis of the insights revealed in these case studies, an optimal set of amino acid ligands is suggested to provide maximum information in a screening campaign.