Department of Chemical Sciences, University of Padova, via Marzolo 1, 35131 Padova, Italy.
CIRCC-Consorzio Interuniversitario per le Reattività Chimiche e la Catalisi, Padova Unit, via Marzolo 1, 35131 Padova, Italy.
J Org Chem. 2021 Feb 19;86(4):3555-3564. doi: 10.1021/acs.joc.0c02952. Epub 2021 Feb 3.
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 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 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.
亲核性的概念是化学中大多数转化的基础。因此，理解和预测不同亲核试剂的相对反应性至关重要。Mayr 的亲核性标度可能代表了最完整的反应性数据集合，目前包括超过 1200 种亲核试剂。已经有几种尝试基于分子性质的计算来理论预测 Mayr 的亲核性参数，但到目前为止还没有得到能够涵盖不同亲核试剂类别的通用模型。我们在此表明，多元线性回归分析是获得简单模型以预测几乎任何类亲核试剂在不同溶剂中参数的合适工具，该模型适用于 341 个数据点的一组。该模型的关键描述符被发现可以解释质子亲和力、溶剂化能和立体位阻。
