Chemistry Seminar with Dr. Scott Denmark from the University of Illinois Urbana-Champaign for a chemistry seminar at 4:00pm

Abstract:

The development of synthetic methods in organic chemistry has historically
been driven by Edisonian empiricism. Catalyst design is no exception wherein
experimentalists attempt to qualitatively recognize patterns in catalyst
structures to improve catalyst selectivity and efficiency. However, this
approach is hindered by the inherent limitations of the human brain to find
patterns in large collections of data, and the lack of quantitative guidelines to
aid catalyst selection. Machine learning provides an attractive alternative for
several reasons: no mechanistic information is needed; catalyst structures can
be characterized by conformer-averaged, grid-based descriptors which
quantify the steric and electronic properties of thousands of candidate
molecules; and the suitability of a given catalyst candidate can be quantified by
comparing its properties to a computationally derived model on the basis of
experimental data. This lecture will describe our chemoinformatic workflow
which was validated in a test case involving the enantioselective addition of
thiols to acyl imines and then applied to the discovery of enantioselective
catalysts for different transformations representing multiple catalyst families

Hosted by Prof Zach Zheng