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Recognition of nucleophilic substitution reaction mechanisms of carboxylic esters based on support vector machine

机译:基于载体载体机的羧酸酯的亲核取代反应机理识别

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The nucleophilic substitution reactions of carboxylic esters ((RCOOR2)-C-1) follow 2 possible mechanisms, namely, stepwise mechanism and concerted mechanism. The reaction mechanism is affected by the structures of the nonleaving group (NLG, ie, R-1), the leaving group (LG, ie, OR2) of carboxylic esters, and the nucleophiles (Nu). The traditional approach for the determination of reaction mechanism by dynamic experiments is not only laborious and time consuming but also only applicable for the situation with single structural factor variation. Benefiting from the ample existing kinetic data, this article aimed to construct a molecular structure-based classification model by support vector machine (SVM). The polarizability effect index of the NLG (PI(NLG)), the pK(a) of the conjugate acid of the LG (pK(a)(LG)), and the pK(a) of the conjugate acid of the Nu (pK(a)(Nu)) were used to characterize the structural information of the 3 influencing factors, respectively. On the basis of these structural descriptors, the SVM classification model was established for the reaction mechanisms of 225 nucleophilic substitution reactions in the training set. The total prediction accuracy of 99.11% was achieved by 5-fold cross validation. Then the reaction mechanisms of 56 reactions in test set A were predicted by this SVM model, and the prediction accuracy was up to 98.21%. Furthermore, test set B consisting of 24 reactions were used as another external data set for prediction, where the reaction mechanisms were controversially reported by different studies. The prediction results of test set B by the SVM model built in this article were amazingly consistent with the conclusions drawn by Um et al.
机译:羧酸酯((RCOOR2)-C-1)的亲核取代反应遵循2种可能的机制,即逐步的机制和齐节机制。反应机制受羧酸羧酸酯和亲核试剂(NU)的非遗料基团(NLG,IE,R-1)的结构的影响,使羧酸酯和亲核试剂(NU)的结构影响。通过动态实验确定反应机制的传统方法不仅是费力且耗时的,而且仅适用于单一结构因子变异的情况。从充分的现有动力学数据受益,本文旨在通过支持向量机(SVM)构建基于分子结构的分类模型。 NLG(PI(NLG))的极化性效应指数,LG的缀合酸的PK(A)(PK(A)(LG))和NU的缀合酸的PK(A)( PK(a)(nu))分别用于表征3个影响因素的结构信息。在这些结构描述符的基础上,建立了SVM分类模型,用于训练集中的225个亲核取代反应的反应机制。通过5倍交叉验证实现99.11%的总预测精度。然后通过该SVM模型预测测试组A中56反应的反应机制,预测精度高达98.21%。此外,用24个反应组成的试验组B作为预测的另一种外部数据集,其中反应机制是由不同研究报告的。本文中建立的SVM模型的测试集B的预测结果令人惊讶地符合UM等人的结论。

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