Comparative QSRR Modeling of Nitrobenzene Derivatives Based on Original Molecular Descriptors and Multivariate Image Analysis Descriptors

A comparative quantitative structure-retention relationship (QSRR) study has been carried out to predict the retention time of nitrobenzene derivatives using original molecular descriptors and multivariate image analysis (MIA) descriptors. First, original molecular descriptors were generated from molecular structures and applied to construct QSRR models using multiple linear regression (MLR), partial least squares (PLS), and artificial neural network (ANN) modeling methods. Then, multivariate image analysis (MIA) descriptors were generated from pixels of images and analyzed using correlation ranking-principal component regression (CR-PCR) and correlation ranking-principal component-artificial neural network (CR-PC-ANN) methods. In this paper, the CR-PC-ANN method presented better results than the other methods for predicting the retention time of the studied compounds. Coefficients of determination (R (2)) using the CR-PC-ANN method for the training, test, and validation sets were 0.989, 0.999, and 0.999, respectively.