The (Not So) Simple Prediction of Enantioselectivity - A Pipeline for High-Fidelity Computations

The computation of reaction selectivity represents an appealing complementary route to experimental studies and a powerful mean to refine catalyst design strategies. Accurately establishing the selectivity of reactions facilitated by molecular catalysts, however, remains a challenging task for computational chemistry. The small free energy differences that lead to large variations in the enantiomeric ratio represent particularly tricky quantities to predict with sufficient accuracy to be helpful for prioritizing experi- ments. Further complicating this problem is the fact that standard approaches typically consider only one or a handful of conformers identified through human intuition as pars pro toto of the conformational space. Obviously, this assumption can potentially lead to dramatic failures should key energetic low-lying structures be missed. Here, we in- troduce a multi-level computational pipeline built upon the graph-based Molassembler library that combines conformer generation and tailored functionalization to facilitate high-throughput mechanistic investigations of chemical reactions. The capabilities of this approach are validated by examining a Rh(III) catalyzed asymmetric C-H activa- tion reaction and assessing the limitations associated with the underlying ligand design model. Specifically, the presence of remarkably flexible chiral Cp ligands, which induce the experimentally observed high level of selectivity, present a rich configurational landscape where multiple unexpected conformations contribute to the reported enan- tiomeric ratios (er). Using Molassembler, we show that considering about 20 transition state conformations per catalysts, which are generated with little human intervention and are not tied to “back of the envelope” models, accurately reproduces experimental er values with limited computational expense.