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Nucleophilicity Prediction via Multivariate Linear Regression Analysis.

ABSTRACT: The concept of nucleophilicity is at the basis of most transformations in chemistry. Understanding and predicting the relative reactivity of different nucleophiles is therefore of paramount importance. Mayr's nucleophilicity scale likely represents the most complete collection of reactivity data, which currently includes over 1200 nucleophiles. Several attempts have been made to theoretically predict Mayr's nucleophilicity parameters N based on calculation of molecular properties, but a general model accounting for different classes of nucleophiles could not be obtained so far. We herein show that multivariate linear regression analysis is a suitable tool for obtaining a simple model predicting N for virtually any class of nucleophiles in different solvents for a set of 341 data points. The key descriptors of the model were found to account for the proton affinity, solvation energies, and sterics.

SUBMITTER: Orlandi M

PROVIDER: S-EPMC7901016 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Nucleophilicity Prediction <i>via</i> Multivariate Linear Regression Analysis.

Orlandi Manuel M Escudero-Casao Margarita M Licini Giulia G

The Journal of organic chemistry 20210203 4

The concept of nucleophilicity is at the basis of most transformations in chemistry. Understanding and predicting the relative reactivity of different nucleophiles is therefore of paramount importance. Mayr's nucleophilicity scale likely represents the most complete collection of reactivity data, which currently includes over 1200 nucleophiles. Several attempts have been made to theoretically predict Mayr's nucleophilicity parameters <i>N</i> based on calculation of molecular properties, but a g ...[more]

PMID: 33534569

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