CHPC - Research Computing and Data Support for the University

Applying Modern Tools of Data Analysis to Current Challenges in Chemistry

By Jacquelyne Read and Matthew Sigman

 Department of Chemistry, University of Utah

What if there were no such thing as "bad data"? In this case, we are not referring to the quality of the data, but the experimental outcome. In the field of asymmetric catalysis, a flourishing area in organic chemistry, a central goal is to develop reactions are able to form one enantiomer (a chiral molecule which has a non-superimposable mirror image) of product in preference to the other enantio-mer possible. A helpful analogy to understand the concept of enantiomers is the right and left hand—mirror images, but not identical. Enantioselective reactions have many important applications, such as the more efficient synthesis of drug molecules, which often need to be made as just one enantiomer because different enantiomers sometimes result in drastically different biological responses. Often, when enantioselective reactions are published, only data meeting or exceeding the gold standard of 95% desired enantiomer to 5% undesired enantiomer are reported. This results in useful, but non-optimal, data never being published.

The Sigman lab takes a different approach. We seek to make use of a wider range of data collected during the reaction optimization process because results showing low enantioselectivity and high enantioselectivity can be equally information-rich and help us learn about a given reaction. Our workflow involves collecting molecular properties (such as size, shape, and electronic nature) relevant to a reaction we seek to study. These properties are then used as parameters in a multivariable linear regression algorithm and correlated to the experimentally determined reaction selectivity. The resulting equations are applied to the prediction of molecules should lead to higher (and sometimes lower) enantioselectivity, which are then synthesized and validated experimentally. Ultimately, a deeper understanding of the reaction can be garnered through analysis of the statistical model. This workflow has been successfully applied to reaction properties beyond enantioselectivity, such as regioselectivity (where a reaction occurs on a given molecule) and rates of chemical processes, which will be discussed further in the examples below [in the original newsletter article]. Central to our technology is the calculation of molecular properties using density functional theory (DFT), which is accomplished using the computational resources of the CHPC, among others.

Read more in the Spring 2019 newsletter.

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