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Experimental Lineage and Computational Analysis of a General Aminoxyl-Based Oxidation Catalyst: Generality from

Soren D Rozema¹, Nicolò Tampellini¹, Jonas Rein²

¹Department of Chemistry, Yale University, New Haven, Connecticut 06520, United States.

ACS Catalysis

|October 23, 2025

Summary

This study introduces a computational approach to optimize peptide catalysts for asymmetric catalysis. The developed catalyst demonstrates high selectivity and broad substrate scope, improving upon traditional methods.

Keywords:

DFT asymmetric catalysis catalyst optimization computational modeling substrate generality

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Area of Science:

Organic Chemistry
Catalysis
Computational Chemistry

Background:

Traditional catalyst optimization relies on iterative, heuristic methods with limited substrate scope.
Achieving general catalyst selectivity across diverse substrates remains a significant challenge in asymmetric catalysis.

Purpose of the Study:

To computationally model and optimize a peptide catalyst for asymmetric catalysis.
To develop a highly selective and general chiral catalyst applicable to a diverse range of substrates.

Main Methods:

Computational modeling using ab initio methods to rationalize catalyst design improvements.
Atomistic resolution analysis of substrate-catalyst interactions and noncovalent networks.
Testing the optimized catalyst across a verifiably diverse set of substrates.

Main Results:

A highly selective and general peptide catalyst was successfully developed.
Computational modeling provided atomistic insights into each generational improvement.
The catalyst's generality was linked to noncovalent interaction networks and substrate-specific interactions.

Conclusions:

Computational modeling accelerates catalyst optimization for improved selectivity and generality.
Catalyst generality can arise from diverse, substrate-specific interactions, not necessarily mechanistic homology.
This work provides a rational framework for designing general asymmetric catalysts.