Holistic prediction of enantioselectivity in asymmetric catalysis

Abstract

When faced with unfamiliar reaction space, synthetic chemists typically apply the reported conditions (reagents, catalyst, solvent and additives) of a successful reaction to a desired, closely related reaction using a new substrate type. Unfortunately, this approach often fails owing to subtle differences in reaction requirements. Consequently, an important goal in synthetic chemistry is the ability to transfer chemical observations quantitatively from one reaction to another. Here we present a holistic, data-driven workflow for deriving statistical models of one set of reactions that can be used to predict out-of-sample reactions. As a validating case study, we combined published enantioselectivity datasets that employ 1,1'-bi-2-naphthol (BINOL)-derived chiral phosphoric acids for a range of nucleophilic addition reactions to imines and developed statistical models. These models reveal the general interactions that impart asymmetric induction and allow the quantitative transfer of this information to new reaction components. This technique creates opportunities for translating comprehensive reaction analysis to diverse chemical space, streamlining both catalyst and reaction development.

Extended Data Figure 1 |. Reaction component comparison.

Parameterization challenges for the identification of numerical descriptors in reaction dimension, demonstrated using two reactions representing the extremes of multidimensional feature space. DCM, dichloromethane; MS, molecular sieves; ee, enantioselectivity.

Figure 1 |. Workflow for interrogating and applying mechanistic transferability.

(A) BINOL-based phosphoric acid catalyzed nucleophilic additions to imines as a general reaction for workflow development. (B) Streamline reaction performance predictions by employing a mechanistic transferability strategy implemented through correlation of all reaction variables to enantioselectivity. General correlations can be built to reveal the interactions between any reaction component in the relevant TS and enantioselectivity. The mechanistic principles leading to enantioselective catalysis captured by the statistical models can be transferred to genuinely different structural motifs not contained in the training dataset.

Figure 2 |. Comprehensive model development.

(A) Regression model containing 367 data entries facilitated by parameterization of every reaction variable. A positive %ee value indicates E-imine TS, a negative %ee Z-imine TS. LOO, leave-one-out cross-validation score; k-fold, average fourfold cross-validation score. (B) Illustration of mechanistic transferability in the data set via “leave one reaction out” (LORO) analysis. In which distinct reactions (as determined by individual publications) are defined as the validation set. (C) Visual analysis and interpretation of the model terms.

Figure 3 |. Development of focused correlations.

(A) Regression model containing 204 entries data-mined from nine literature sources. (B) Model emphasizes the importance of both steric and electronic factors. Reasonably large catalyst and imine substituents lead to high-levels of enantioselectivity, if these two components are matched any nucleophile should be compatible. (C) Regression model containing 147 entries data-mined from eight literature sources. (D) Overlapping steric terms describing catalyst and imine reinforce the notion that similar interactions remain within the two geometric imine stereoisomers. However, this model emphasizes the importance of steric contributions predominantly from the nucleophile for high enantioselectivities.

Figure 4 |. Out-of-sample predictions using two-tiered prediction workflow.

Comprehensive model first determines E or Z TS, configuration specific models are then utilized to refine predictions. A generic amine product denotes the stereochemical outcome predicted if the reaction proceeds via the E or Z TS and catalyzed by an (R)-CPA. Product stereochemistry is reversed if the (S)-catalyst is used. (A) Application to addition of enecarbamates to benzoyl imines and transfer hydrogenation of alkynyl ketimines. (B) Prediction of TCYP which has cyclohexyl groups at the 2,4,6 positions of the aromatic ring, as a highly selective catalyst for the addition of thiol to benzoyl imines.