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.阐明配体对映选择性的复杂影响，是非对称催化领域长期存在的一项核心挑战。针对α-氨基酸配体，其关键难点在于精准描述配体α侧链与N保护基同时变化所引发的综合扰动，这极大阻碍了研究者对结构-对映选择性关系的直观认知。为了解析手性氨基酸介导的对映选择性C-H官能化反应中的这类复杂机制，研究团队开发了一套计算有机金属模型体系。尽管仅基于常规策略构建的模型预测能力会显著下降，但基于基态Pd(II)的模型却能出色地复现所观测到的对映选择性结果。研究人员借助多变量建模方法对这些结构进行优化利用，成功描述了Pd(II)催化的C-H烷基化、烯基化以及两种C-H芳基化反应；其中，扭转角、配体埋藏体积百分比以及自然键轨道（Natural Bond Orbital, NBO）电荷等描述符，与对映体过量（enantiomeric excess, ee）的定量预测呈现出显著相关性。基于上述案例研究揭示的机制见解，研究人员提出了一套最优的氨基酸配体组合，可在配体筛选实验中获取最大化的有效信息。