Prediction of log kw of disubstituted benzene derivatives in reversed-phase high-performance liquid chromatography using multiple linear regression and radial basis function neural network

Abstract

A study of the relationships between the extrapolated capacity factor (log k w) of a group of 54 disubstituted benzene derivatives and a set of eight molecular descriptors was made. By using multiple linear regression (MLR), we obtained an empirical function, which included five descriptors. The performance of a radial basis function neural network (RBFNN) was evaluated. The network used thin plate spline and multi-quadratic functions, which showed better than MLR. Semi-empirical quantum chemical method PM3 implemented in HyperChem 4.0 was employed to calculate the molecular descriptors of the compounds. The results gave a relative minor root mean squared (rms) error (0.070 and 0.084) and indicated that the quantitative structure–retention relationships (QSRR) models proposed were very satisfactory.