Prediction of Setschenow constants of organic compounds based on a 3D structure representation
writer：Jie Xu, Lei Wang, Luoxin Wang, Guijie Liang, Xiaolin Shen, Weilin Xu
keywords：QSPR
source：期刊
specific source：Chemometrics and Intelligent Laboratory Systems
Issue time：2011年
Quantitative structure–property relationship (QSPR) studies were performed between three-dimensional (3D) descriptors representing the molecular structures and Setschenow constants (Ksalt) by sodium chloride for a diverse set of organic compounds. The entire set of 101 compounds was divided into a training set of 71 compounds and a test set of 30 compounds according to Kennard and Stones algorithm. Multilinear regression (MLR) analysis was used to select the best subset of descriptors and to build linear models; while nonlinear models were developed by means of artificial neural network (ANN). The obtained models with five descriptors involved show a good predictive power: for the test set, a squared correlation coefficient (R2) of 0.8987 and a standard error of estimation (s) of 0.021 were achieved by the MLR analysis; while by the ANN, R2 and s were 0.9034 and 0.020, respectively.