QSPR Study of Setschenow Constants of Organic Compounds Using MLR, ANN, and SVM Analyses
writer：Jie Xu, Lei Wang, Luoxin Wang, Xiaolin Shen, Weilin Xu
keywords：Setschenow constants, QSPR
source：期刊
specific source：Journal of Computational Chemistry
Issue time：2011年
A quantitative structure-property relationship (QSPR) study was performed for the prediction of the Setschenow constants (Ksalt) by sodium chloride of organic compounds. The entire
set of 101 compounds was randomly divided into a training set of 71 compounds and a test set of 30 compounds. Multiple linear regression, artificial neural network (ANN), and support vector machine (SVM) were utilized to build the linear and nonlinear QSPR models, respectively. The obtained models with four descriptors involved show good predictive ability.
The linear model fits the training set with R2 = 0.8680, while ANN and SVM higher values of R2 = 0.8898 and 0.9302, respectively. The validation results through the test set indicate that the proposed models are robust and satisfactory. The QSPR study suggests that the molecular lipophilicity is closely related to the Setschenow constants.