Issue 43, 2022

A broadly applicable quantitative relative reactivity model for nucleophilic aromatic substitution (SNAr) using simple descriptors

Abstract

We report a multivariate linear regression model able to make accurate predictions for the relative rate and regioselectivity of nucleophilic aromatic substitution (SNAr) reactions based on the electrophile structure. This model uses a diverse training/test set from experimentally-determined relative SNAr rates between benzyl alcohol and 74 unique electrophiles, including heterocycles with multiple substitution patterns. There is a robust linear relationship between the experimental SNAr free energies of activation and three molecular descriptors that can be obtained computationally: the electron affinity (EA) of the electrophile; the average molecular electrostatic potential (ESP) at the carbon undergoing substitution; and the sum of average ESP values for the ortho and para atoms relative to the reactive center. Despite using only simple descriptors calculated from ground state wavefunctions, this model demonstrates excellent correlation with previously measured SNAr reaction rates, and is able to accurately predict site selectivity for multihalogenated substrates: 91% prediction accuracy across 82 individual examples. The excellent agreement between predicted and experimental outcomes makes this easy-to-implement reactivity model a potentially powerful tool for synthetic planning.

Graphical abstract: A broadly applicable quantitative relative reactivity model for nucleophilic aromatic substitution (SNAr) using simple descriptors

Supplementary files

Article information

Article type
Edge Article
Submitted
19 7月 2022
Accepted
17 10月 2022
First published
17 10月 2022
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY-NC license

Chem. Sci., 2022,13, 12681-12695

A broadly applicable quantitative relative reactivity model for nucleophilic aromatic substitution (SNAr) using simple descriptors

J. Lu, I. Paci and D. C. Leitch, Chem. Sci., 2022, 13, 12681 DOI: 10.1039/D2SC04041G

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