Traditionally, stereochemical models designed to explain the selectivity of a particular reaction class relied on catalyst or auxiliary rigidity which emphasizes steric repulsion as a dominant contributing factor to the overall reaction outcome. However, some amount of flexibility can be beneficial for selectivity, as has been noted for <i>tropos </i>ligands, hydrogen bond donating catalysts, and low molecular weight peptide catalysts. To gain understanding of likely key substrate-catalyst interactions in systems employing dynamic catalysts, modern physical organic tools such as multivariate linear regression analysis can be used. For example, in a comparative study between the more rigid BINOL-derived and the more flexible tetrapeptidic chiral phosphoric acid catalysts, we have shown through multivariate linear regression modeling that although each catalyst performs well, the tetrapeptidic catalyst, by virtue of its flexibility, is likely better able to adapt to the steric demands of the substrate. This work describes the development and application of statistical modeling tools in order to gain further understanding of conformationally dynamic catalysts used in asymmetric catalysis.

Development of statistical modeling tools for the analysis of conformationally flexible chiral catalysts

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

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