A Predictive Betaine Organocatalyst Platform for Asymmetric 1,3-Dipolar Cycloadditions Enabled by Interaction-Aware Modeling

Abstract

Predicting enantioselectivity in asymmetric (organo)catalysis remains a longstanding challenge, particularly in systems where stereocontrol is governed by weak, highly flexible, and substrate-dependent noncovalent interactions. In such regimes, simplified transition-state-based DFT analysis can become unreliable due to the presence of multiple low-energy conformations, competing binding modes, and alternative reaction pathways. We introduce a previously unexplored chiral imidazolium/aryloxide betaine organocatalyst platform for asymmetric 1,3-dipolar cycloadditions of CH-acidic imines with maleimides that operates with exceptional efficiency, reaching turnover numbers of up to 3000 while maintaining high enantiocontrol. To overcome the limitations of transition-state-guided catalyst optimization and enable predictive control beyond the limits of mechanistic intuition, we combined combinatorial catalyst-substrate screening with descriptor-based statistical modeling. Analysis of the resulting data set reveals that no single catalyst is universally optimal across substrate space; instead, enantioselectivity is governed by a catalyst-substrate matching effect. The resulting models identify the molecular features associated with selectivity and enable prospective prediction of enantioselectivity for previously unseen catalysts and a structurally distinct substrate class. These findings establish highly productive metal-free azomethine ylide cycloadditions and demonstrate how interaction-aware data-driven models can provide predictive control in complex asymmetric catalytic systems.