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Nucleophilicity Prediction via Multivariate Linear Regression Analysis

机译:多元线性回归分析的亲核性预测

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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.
机译:亲核性的概念是化学中大多数转变的基础。因此,了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反应性数据的最完整集合,目前包括1200多个亲核细胞。在分子性质计算的基础上,人们曾多次尝试从理论上预测迈尔的亲核性参数N,但迄今为止还无法获得一个解释不同类亲核性的通用模型。我们在此表明,多元线性回归分析是一个合适的工具,可以获得一个简单的模型,预测341个数据点在不同溶剂中几乎任何类别的亲核试剂的N。发现该模型的关键描述符可以解释质子亲和力、溶剂化能和空间位阻。

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