Prediction of Solvent Effects on Rate Constant of [2+2] Cycloaddition Reaction of Diethyl Azodicarboxylate with Ethyl Vinyl Ether Using Artificial Neural Networks

Bulletin of the Korean chemical society

Aziz Habibi-Yangjeh¹  Mahdi Nooshyar¹ 

关键词: Artificial neural networks;    Solvent effects;    Reaction kinetics;    Quantitative structure-activity relationship;    Multi-parameter linear regression;   

DOI  :  

学科分类：化学（综合）

来源: Korean Chemical Society

【 摘 要 】

Artificial neural networks (ANNs), for a first time, were successfully developed for the modeling and prediction of solvent effects on rate constant of [2+2] cycloaddition reaction of diethyl azodicarboxylate with ethyl vinyl ether in various solvents with diverse chemical structures using quantitative structure-activity relationship. The most positive charge of hydrogen atom (q+), dipole moment ( �?), the Hildebrand solubility parameter ( ヤH2) and total charges in molecule (qt) are inputs and output of ANN is log k2 . For evaluation of the predictive power of the generated ANN, the optimized network with 68 various solvents as training set was used to predict log k2 of the reaction in 16 solvents in the prediction set. The results obtained using ANN was compared with the experimental values as well as with those obtained using multi-parameter linear regression (MLR) model and showed superiority of the ANN model over the regression model. Mean square error (MSE) of 0.0806 for the prediction set by MLR model should be compared with the value of 0.0275 for ANN model. These improvements are due to the fact that the reaction rate constant shows non-linear correlations with the descriptors.

【 授权许可】

Unknown