Comparison of quantum mechanics protocols during the evaluation of quantitative structure-retention relationships supported by genetic-algorithm multiple linear regression

Abstract

While developing Quantitative Structure-Retention Relationships (QSRR) models it is crucial to construct a data matrix, consisting of both experimentally designated values (the ones that are being modelled) and molecular descriptors, representing physicochemical properties in form of numbers. In order to designate those descriptors, studied compounds need to be modelled and optimized using molecular modelling methods. From a variety of possible choices, semi-empirical AM1 (being an often discredited method due to the fact that it is quick and provides various approximations) and DFT (being a complex, quantum mechanics-based method, which is more time-consuming yet still bases on approximations) are one of the most popular. In this study one took a set of carefully chosen compounds used to model retention on C18 column, and modelled their molecules using both of those techniques, which were afterwards subjected to Genetic-Algorithm Multiple Linear Regression (GA-MLR) in order to derive and compare achieved models and their parameters. Study found that for the tested chromatographic system (C18 column and a set of model fifteen compounds) there is no advantage in using DFT over AM1, despite common modelling principles. What is more important, the developed models are accurate and use very simple descriptors, which can be easily calculated without any need for complex 3D modelling of structures. The derived models enabled also to assess the physicochemical properties of the tested column finding slight similarities and dissimilarities between the applied models.