Application of GA-MLR, GA-PLS and the DFT quantum mechanical (QM) calculations for the prediction of the selectivity coefficients of a histamine-selective electrode

Abstract

Quantitative structure–property relationships (QSPRs) were developed using a genetic algorithm (GA), based on the variable-selection approach with topological descriptors. The selectivity coefficients of 26 molecules (drug, amino-acid and organic compound) of a histamine-selective electrode were efficiently estimated and predicted with the QSPR models. The most important descriptors were selected from a set of 74 topological descriptors to build the QSPR models, using the multiple linear regressions (MLRs) and the partial least squares (PLS) regression. The predictive quality of the QSPR models was tested for an external prediction set of 7 compounds, randomly chosen from 26 compounds. The PLS regression method was used to model the structure-selectivity coefficient relationships. However, the results surprisingly showed more or less the same quality for MLR and PLS modeling, according to the squared regression coefficients R2 values, which were 0.918 and 0.915, respectively. In addition, the theoretical investigation on the interaction of the histamine and the other studied compounds with the ionophore was performed. The correlation between the interaction energies and the selectivity coefficients of the studied compounds was equal to 0.993, demonstrating the applicability of these results for the prediction of the selectivity coefficients.