Computational Prediction of Enantioselectivity in Metal-Catalyzed Reactions

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Many molecules are chiral, which means that they can exist in two forms that are mirror images, analogous to right and left hands. Very often the right and left hand forms interact differently with biological systems, sometimes with profound consequences. One hand may be beneficial while the other hand may also be beneficial, have no effect, or be harmful. As a result, the handedness property is an extremely important consideration in the design and manufacture of molecules such as pharmaceuticals. The most efficient syntheses of chiral molecules use catalysts that are also chiral. Unfortunately, the synthesis of chiral catalysts often requires inefficient trial and error approaches. In this project, Professors Wiest and Helquist at the University of Notre Dame employ computational chemistry, machine learning, and organic chemistry to develop efficient, fast, and accurate methods to predict how to make chiral molecules. In collaboration with a major pharmaceutical company, these methods are improving access to molecules of importance to the pharmaceutical industry. This new approach to computationally-driven synthesis and the close, long-term collaboration with industry creates unique experiences for their students. These students participate in industrial internships and interdisciplinary training at AstraZeneca in Sweden. The two senior researchers are actively engaging in broadening participation initiatives at the University of Notre Dame and in the Midwest. These activities include mentoring undergraduate students from underrepresented groups through the Building Bridges program and summer research for high school students. With funding from the Chemical Catalysis Program of the Chemistry Division, Dr. Wiest and Dr. Helquist of the University of Notre Dame continue the development of the quantum guided molecular mechanics (Q2MM) method for the automated parameterization of transition state force fields (TSFFs) which are then used for the high-throughput virtual screening of enantioselective catalysts (CatVS). A number of methodological improvements, such as machine learning, new interfaces, and automated parameterization procedures, and TSFFs for new reactions of interests for the synthetic organic community are developed. On the application side, the new methods are used for rapid prototyping of novel catalysts for hydrogen atom transfer reactions and for high throughput screening of virtual libraries for an enantioselective version of a novel cross-coupling reaction developed by the team. The Q2MM and CatVS code, as well as the validated force fields continue to be available to the scientific community free of charge via github.com/q2mm. The combined computational and experimental approach, combined with the close collaboration with AstraZeneca, provides a unique interdisciplinary training environment for the undergraduate and graduate students.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.