QSPR STUDY OF THE SOLUTE POLARITY PARAMETER IN REVERSED-PHASE LIQUID CHROMATOGRAPHY USING PARTIAL LEAST SQUARES AND ARTIFICIAL NEURAL NETWORK

Abstract

A quantitative structure-property relationship (QSPR) study based on artificial neural network (ANN) was carried out for the prediction of the solute polarity parameters of a set of 146 compounds of a very different chemical nature in reversed-phase liquid chromatography (RPLC). The genetic algorithm-partial least squares (GA-PLS) method was applied as a variable selection tool. A PLS method was used to select the best descriptors and the selected descriptors were used as input neurons in neural network model. These descriptors are: topological electronic index (), total hybridization components of the molecular dipole (μ_h), total dipole moment of the molecule (μ), PPSA-3 atomic charge weighted PPSA (PPSA-3), Kier & Hall index, order 0 (⁰χ), and Molecular volume (M_V). The results obtained showed the ability of developed artificial neural network to predict the solute polarity parameter of various compounds. Moreover, results reveal the superiority of the ANN over the PLS model.

Keywords:

• artificial neural network
• liquid chromatography
• partial least squares
• quantitative structure-property relationship
• reversed-phase
• solute polarity parameter

Notes

^a The definitions of the descriptors are given in Table 2.

^a c refers to the calibration (training) set; t refers to test set; v refers to validation set; R is the correlation coefficient; SE is standard error; and F is the statistical F value.