# Development of Statistical Models to Identify Structural Features and Noncovalent Interactions Influencing Asymmetric Catalysis

> **NIH NIH F32** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2020 · $65,310

## Abstract

Project Summary
 This proposal describes the development of new statistical methods to evaluate the weak, noncovalent interactions
involved in asymmetric catalysis. Noncovalent interactions are essential to biological recognition as well as to the function
of biological catalysts—enzymes. The project will be conducted in the context of two enantioconvergent substitution
reactions of α-chloroglycine esters. The proposed reactions would provide facile access to valuable aryl and allylic unnatural
α-amino acids, whereas current synthetic methods can result in low enantioselectivities or yields. Preliminary results show
the proposed reactions to be capable of high enantioselectivities when catalyzed by arylpyrrolidino squaramide derivatives,
but testing for the optimal catalyst has revealed nonintuitive trends in enantioselectivities and low yields. To improve current
capabilities for reaction optimization, new statistical methods will be developed to allow simultaneous optimization of yield
and enantioselectivity. A set of good-, modest-, and poor-performing catalysts will be selected, and reactions will be
performed using each catalyst. Enantioselectivities as well as reaction rates will be measured to build the data set. Steric
and electronic features of the catalysts will be modeled computationally to generate parameters for the statistical analysis.
A multivariate linear regression will then be conducted to generate predictive models for both enantioselectivity and yield.
After optimizing these reactions, enantioselectivity measurements will be made with each catalyst over a range of
temperatures. From this data, ΔΔH‡ and ΔΔS‡ can be calculated for individual catalysts in each reaction. Predictive statistical
models will then be created for ΔΔH‡ and ΔΔS‡, drawing from the computational parameter library developed for the
optimization models. Based on the parameters that appear in these models, structural features relevant to the
enantioselectivities of the catalysts can be identified. The enthalpic and entropic contributions of each relevant structural
feature could also be quantitatively assessed. This outcome would contribute to a deeper understanding of how noncovalent
interactions operate in the enantiodetermining step of these reactions. As a result of this knowledge, improved catalysts and
substrates could be designed. Application of the proposed methodology to any catalytic transformation would result in more
efficient reaction optimization and catalyst design for that reaction. Eventually, this work could bring about de novo catalyst
design following the creation of a comprehensive library of computational parameters and statistical models encompassing
ΔΔG‡, rate, ΔΔH‡, and ΔΔS‡ for representative groups of reactions. The impact of the proposed work would not only
contribute to the field of asymmetric catalysis, but it would also provide improved methods of accessing unnatural α-amino
acids. These compounds are essential to biological st...

## Key facts

- **NIH application ID:** 9928963
- **Project number:** 5F32GM128351-03
- **Recipient organization:** UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH
- **Principal Investigator:** Jacquelyne Aliscia Read
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $65,310
- **Award type:** 5
- **Project period:** 2018-06-01 → 2021-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9928963

## Citation

> US National Institutes of Health, RePORTER application 9928963, Development of Statistical Models to Identify Structural Features and Noncovalent Interactions Influencing Asymmetric Catalysis (5F32GM128351-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9928963. Licensed CC0.

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