Practitioners are generally not willing to explore modern reactions where considerable synthetic effort is required to generate materials and the results are not certain. Organocatalysis exemplifies this, in which a broad set of enantioselective reactions have been successfully developed but further applications to include additional substrates are often not performed. Herein we demonstrate how statistical models can be utilized to accurately distinguish between different catalysts and reactions to guide the selection of efficient synthetic routes to obtain a target molecule.

Organometallic intermediates participate in many multi-catalytic enantioselective transformations directed by a chiral catalyst, but the requirement of optimizing two catalyst components is a significant barrier to widely adopting this approach...

Selecting the optimal catalyst to impart high levels of enantioselectivity in a new transformation is challenging because the ideal molecular requirements of the catalyst for one reaction do not always simply translate to another. In these reaction scenarios practitioners typically use the most general catalyst structure as a starting point for optimization. However, for many reaction systems and catalyst chemotypes the most general catalyst structure may be largely unknown presenting a significant limitation in catalyst application to new reaction space. Herein, we demonstrate that comprehensive statistical models can be applied to identify the most general catalyst for many chemical systems. These inclusive statistical models that encompass many reaction types can provide information about the relevant structural requirements necessary for high enantioselectivity across a broad reaction range. By validating this approach on diverse regions of organocatalyzed reaction space we discovered structurally distinct catalysts can in some cases provide similar levels of enantioselectivity. The second curious finding determined that the best and most popular catalyst systems may not be equivalent. Validation of this approach on a multi-catalytic dearomatization reaction resulted in the discovery that our general catalyst findings allowed for streamlined reaction development for highly complex transformations.

Organometallic intermediates participate in many multi-catalytic enantioselective transformations directed by a chiral catalyst, but the requirement of optimizing two catalyst components is a significant barrier to widely adopting this approach for chiral molecule synthesis. Algorithms can potentially accelerate the screening process by developing quantitative structure-function relationships from large experimental datasets. However, the chemical data available in this catalyst space is limited. We report a data-driven strategy that effectively translates selectivity relationships trained on enantioselectivity outcomes derived from one catalyst reaction systems where an abundance of data exists, to synergistic catalyst space. We describe three case studies involving different modes of catalysis (Brønsted acid, chiral anion, and secondary amine) that substantiate the prospect of this approach to predict and elucidate selectivity in reactions where more than one catalyst is involved. Ultimately, the success in applying our approach to diverse areas of asymmetric catalysis implies that this general workflow should find broad use in the study and development of new enantioselective, multi-catalytic processes.

The application of mechanistic generalizations is at the core of chemical reaction development and application. These strategies are rooted in physical organic chemistry where mechanistic understandings can be derived from...