Modeling and predicting the solute polarity parameter in reversed-phase liquid chromatography using quantitative structure-property relationship approaches.

Abstract

A prediction of quantitative structure-property relationships is developed to model the polarity parameter of a set of 146 organic compounds in acetonitrile in reversed-phase liquid chromatography. Enhanced replacement method and support vector machine regressions were employed to build prediction models based on molecular descriptors calculated from the structure alone. The correlation coefficients between experimental and predicted values of polarity parameter for the test set by enhanced replacement method and support vector machine were 0.970 and 0.993, respectively. The obtained results demonstrated that the support vector machine model is more reliable and has a better prediction performance than the enhanced replacement method.