Rethinking catalyst design by using data science

Abstract

In this issue of Chem, Sigman, Toste, and co-workers use data science tools to target the development of a novel family of chiral phosphoric acid scaffolds. These conformationally flexible catalysts were designed to induce enantioselectivity through predictable non-covalent interactions, offering an attractive alternative to state-of-the-art chiral catalyst scaffolds that rely on rigid chiral pockets. In this issue of Chem, Sigman, Toste, and co-workers use data science tools to target the development of a novel family of chiral phosphoric acid scaffolds. These conformationally flexible catalysts were designed to induce enantioselectivity through predictable non-covalent interactions, offering an attractive alternative to state-of-the-art chiral catalyst scaffolds that rely on rigid chiral pockets. Data science enables the development of a new class of chiral phosphoric acid catalystsLiles et al.ChemMarch 24, 2023In BriefAlthough catalyst design represents a fundamental challenge in asymmetric catalysis, data science can streamline the process of optimization by enabling the discovery of key catalyst structure-activity relationships. By carefully designing a training set and subsequent reactivity profiling, we herein demonstrate the successful application of data science tools for the design of a new class of adaptable phosphoric acids. These catalysts exhibit a single example of point chirality and can induce high levels of selectivity in a transfer hydrogenation reaction. Full-Text PDF