Thu, Jan. 16, 2020, 3:30pm
Edward C Taylor Auditorium, Frick B02
Data Science Tools for Selectivity Prediction in Chiral Phosphoric Acid Catalysis
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 from one reaction to another. This talk will introduce data science workflows which combine experimental data and DFT calculations to enable models to be generated from one set of reactions that can be deployed to predict another. I will discuss how these tools have been applied to the prediction of enantioselectivity outcomes in chiral phosphoric acid catalysis. The first portion of the talk will focus on the development of qualitative models for the prediction of the optimal chiral phosphoric acid catalyst for a particular nucleophilic addition to an imine. The common mechanistic patterns revealed for this class of transformations, inspired the use of quantitative, statistical models for comprehensive analysis of entire reaction classes. These models reveal the general interactions that impart asymmetric induction and allow the quantitative transfer of this information to new reaction components. Furthermore, I discuss how these reaction transferability strategies have guided successful substrate scope extension in catalytic enantioselective Minisci reactions of diazines. Ultimately, these techniques create opportunities for translating comprehensive reaction analysis to diverse chemical space, streamlining both catalyst and reaction development.