Experimental Lineage and Computational Analysis of a General Aminoxyl-Based Oxidation Catalyst: Generality from...

Traditionally, catalyst optimization in asymmetric catalysis is approached as an iterative, heuristic-based process, where point modifications of a hit catalyst are tested against one (or a small number of) model substrate(s). While optimization to high levels of selectivity is sometimes successful, catalyst generality with respect to substrate scope (i.e., its ability to deliver a diverse set of target products with consistently high levels of selectivity) is more elusive. This work describes and models computationally a successful peptide catalyst optimization campaign carried out on a verifiably diverse set of substrates, which delivered a highly selective and general chiral catalyst. The success of every generational improvement of the catalyst design is now rationalized with atomistic resolution by ab initio modeling of the individual substrate that mostly benefits from that generational improvement. Structural and topological insights about the noncovalent interaction networks orchestrating both the catalyst conformation and the substrate-catalyst interactions were dissected, culminating on the underpinnings of the optimized catalyst's generality. Surprisingly and significantly, the generality of high selectivity for many substrates was found to be consistent with alternative, and indeed substrate-specific, interactions, suggesting that functional generality need not be a result of mechanistic homology.