The (not so) simple prediction of enantioselectivity – a pipeline for high-fidelity computations

Chem Sci. 2022 May 18;13(23):6858-6864. doi: 10.1039/d2sc01714h. eCollection 2022 Jun 15.


The computation of reaction selectivity represents an appealing complementary route to experimental studies and a powerful means 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 (er) represent particularly tricky quantities to predict with sufficient accuracy to be helpful for prioritizing experiments. 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 introduce a multi-level computational pipeline leveraging the graph-based Molassembler library to construct an ensemble of molecular catalysts. The manipulation and interpretation of molecules as graphs provides a powerful and direct route to tailored functionalization and conformer generation that facilitates high-throughput mechanistic investigations of chemical reactions. The capabilities of this approach are validated by examining a Rh(iii) catalyzed asymmetric C-H activation 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 enantiomeric 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.

PMID:35774159 | PMC:PMC9200111 | DOI:10.1039/d2sc01714h


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