Discovery and Optimization of Enantioselective Catalysts Guided by Informatics and Machine Learning

University Of Illinois At Urbana-Champaign, Urbana IL

Investigators

Abstract

With support from the Chemical Catalysis Program in the NSF Division of Chemistry, Professor Scott E. Denmark of the Department of Chemistry at the University of Illinois, Urbana-Champaign (UIUC) and his team are working to develop a new approach to the design of catalytic processes that combines the creative power of diversity-oriented synthesis with the computational power of informatics. The foundational requirements for this program are the invention and implementation of high-resolution chemical descriptors that are able to accurately reflect the chemical properties of molecules in computationally readable form. The development of a truly general, computationally guided workflow for the optimization of molecular function could have broad scientific impact by providing the experimentalist with better tools to design new compounds with desirable chemical properties from target/therapeutic selectivity to optical and materials properties. Of paramount interest here is the discovery and optimization of catalysts for industrially relevant chemical processes. The planned research activities planned are ideal for the intellectual and practical training of students at the interface of preparative organic chemistry and data science. The unifying theme of this activity is the invention of new chemical reactions that challenge current thinking. In the context of educational outreach, funds are requested to support an undergraduate intern each of the three years under the auspices of the St. Elmo Brady Summer Research Scholars Program at UIUC. This program is a 10-week summer research experience to increase the percentages of students from underserved groups in the sciences. Professor Denmark and his UIUC team are will developing a research program that combines computational analysis with experimentation, in an iterative fashion, namely: (1) in silico generation of a large library of hypothetical catalyst structures, based on a given scaffold, followed by calculation of descriptors of each library member, (2) diversity analysis to generate a representative “training set”, (3) synthesis of the training set, (4) evaluation of the training set in a given reaction, (5) development and validation of a mathematical model of that correlates empirical output with molecular properties (6) application of that model to the virtual library of catalysts, (7) synthesis and evaluation of best predicted catalysts, and (8) the repetition of steps 4-7 until desired output is achieved. One of the most powerful features of this approach is that the training set is applicable to a wide variety of reactions that are susceptible to catalysis by that scaffold. Accordingly, all three objectives encompass the established chemoinformatic/experimental workflow. Within each objective, the training set generated for each scaffold will be evaluated for the optimization of several chemical reactions. Because the catalyst selection process is performed using catalyst descriptors alone, this process is reaction- and mechanism-agnostic, and chosen catalyst training sets can be used to evaluate many transformations. Each objective involves the generation, synthesis, evaluation and optimization a ligand or catalyst: Objective 1 – Brønsted acid catalysts for atropo-selective cyclohalogenation; Objective 2 – Enantioselective oxidative nitroso-ene reactions and reductive Heck reactions and Objective 3 – Enantioselective epoxide and aziridine desymmetrization under organometallic catalysis. 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.