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.

Uncertainty Quantification of Reactivity Scales**

“…We propose a third way to exploiting empirical parameter distributions. A series of theoretical models predicting Mayr‐type reactivity parameters were proposed in the past [34‐43] . The predictive power of these models was assessed with respect to some summary statistic (e. g., mean absolute error or root‐mean‐square error).…”

“…A series of theoretical models predicting Mayrtype reactivity parameters were proposed in the past. [34][35][36][37][38][39][40][41][42][43] The predictive power of these models was assessed with respect to some summary statistic (e. g., mean absolute error or rootmean-square error). However, to put the resulting statistics into context, it is necessary to know the uncertainty in the underlying reference values.…”

“…A consequence of this skewness is that log k MPE calculated from version 2.0 reactivity parameters may Table 3. Predictions of nucleophilicity, N (O21) , by Orlandi et al [42] for nucleophiles of the reference set. Differences with respect to parametrizations 1.0 (Mayr and co-workers [24,25] ) and 2.0 (this work) are provided, DN ð1:0=2:0Þ ¼ N ð1:0=2:0Þ À N (O21) .…”

Gut Microbiota of Individuals Could Be Balanced by a 14-Day Supplementation With Laminaria japonica and Differed in Metabolizing Alginate and Galactofucan

“…The multiple linear regression analysis is a regression model with more than one independent variable which is applied to determine the correlations among variables having cause-effect relationships and to make predictions for the topic by using the relationships ( 31 , 34 , 35 ). To identify the determinants of bacterial OTUs to Alg or GF utilization, the association of fecal bacterial OTUs with the consumption of the Alg or GF was analyzed by multiple linear regression analysis.…”

Basicity as a Thermodynamic Descriptor of Carbanions Reactivity with Carbon Dioxide: Application to the Carboxylation of α,β-Unsaturated Ketones

“…As reported by Mayr and coworkers ( Li et al, 2020 ), the prediction of the reactivity of carbon-based nucleophiles with CO 2 is problematic using linear-free energy relationships based on nucleophilicity and electrophilicity parameters ( Li et al, 2020 ; Orlandi et al, 2021 ); the failure to observe carboxylation products with a variety of nucleophilic carbanions may be caused by unfavorable thermodynamics of the reaction ( Li et al, 2020 ).…”