Data Science Enables the Development of a New Class of Chiral Phosphoric Acid Catalysts.

The widespread success of BINOL-chiral phosphoric acids (CPAs) has led to the development of several high molecular weight, sterically encumbered variants. Herein, we disclose an alternative, minimalistic chiral phosphoric acid backbone incorporating only a single instance of point chirality. Data science techniques were used to select a diverse training set of catalysts, which were benchmarked against the transfer hydrogenation of an 8-aminoquinoline. Using a univariate classification algorithm and multivariate linear regression, key catalyst features necessary for high levels of selectivity were deconvoluted, revealing a simple catalyst model capable of predicting selectivity for out-of-set catalysts. This workflow enabled extrapolation to a catalyst providing higher selectivity than both reported peptide-type and BINOL-type catalysts (up to 95:5 er). These techniques were then successfully applied towards two additional transforms. Taken together, these examples illustrate the power of combining rational design with data science (ab initio) to efficiently explore reactivity during catalyst development.

Mechanistic Investigation of Ni-Catalyzed Reductive Cross-Coupling of Alkenyl and Benzyl Electrophiles.

Mechanistic investigations of the Ni-catalyzed asymmetric reductive alkenylation of N-hydroxyphthalimide (NHP) esters and benzylic chlorides are reported. Investigations of the redox properties of the Ni-bis(oxazoline) catalyst, the reaction kinetics, and mode of electrophile activation show divergent mechanisms for these two related transformations. Notably, the mechanism of C(sp3) activation changes from a Ni-mediated process when benzyl chlorides and Mn0 are used to a reductant-mediated process that is gated by a Lewis acid when NHP esters and tetrakis(dimethylamino)ethylene is used. Kinetic experiments show that changing the identity of the Lewis acid can be used to tune the rate of NHP ester reduction. Spectroscopic studies support a NiII-alkenyl oxidative addition complex as the catalyst resting state. DFT calculations suggest an enantiodetermining radical capture step and elucidate the origin of enantioinduction for this Ni-BOX catalyst.

Exploring Structure-Function Relationships of Aryl Pyrrolidine-Based Hydrogen-Bond Donors in Asymmetric Catalysis Using Data-Driven Techniques.

Hydrogen bond-based organocatalysts rely on networks of attractive noncovalent interactions (NCIs) to impart enantioselectivity. As a specific example, aryl pyrrolidine substituted urea, thiourea, and squaramide organocatalysts function cooperatively through hydrogen bonding and difficult-to-predict NCIs as a function of the reaction partners. To uncover the synergistic effect of the structural components of this catalyst class, we applied data science tools to study various model reactions using a derivatized, aryl pyrrolidine-based, hydrogen-bond donor (HBD) catalyst library. Through a combination of experimentally collected data and data mined from previous reports, statistical models were constructed, illuminating the general features necessary for high enantioselectivity. A distinct dependence on the identity of the electrophilic reaction partner and HBD catalyst is observed, suggesting that a general interaction is conserved throughout the reactions analyzed. The resulting models also demonstrate predictive capability by the successful improvement of a previously reported reaction using out-of-sample reaction components. Overall, this study highlights the power of data science in exploring mechanistic hypotheses in asymmetric HBD catalysis and provides a prediction platform applicable in future reaction optimization.