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In this work a multi-objective quantitative structure-property relationship (QSPR) analysis approach was reported based on the study on three partition properties of 50 aromatic sulfur-containing carboxylates. Here multi-objectives (properties) were taken as a vector for QSPR modeling. The quantitative correlations for partition properties were developed using a genetic algorithm-based variable-selection approach with quantum chemical descriptors derived from AM1-based calculations. With the QSPR models, the aqueous solubility, octanol/water partition coefficients and reversed-phase HPLC capacity factors of sulfur-containing compounds were estimated and predicted. Using GA-based multivariate linear regression with cross-validation procedure, a set of the most promising descriptors was selected from a pool of 28 quantum chemical semi-empirical descriptors, including steric and electronic types, to integrally build QSPR models. The selected molecular descriptors included the net charges on carboxyl group (Q OC), the 2nd power of net charges on nitrogen atoms (Q 2 N), the net atomic charge on the sulfur atoms (Q S), the van der Waals volume of molecule (V), the most positive net atomic charge on hydrogen atoms (Q H) and the measure of polarity and polarizability (π), which were main factors affecting the distribution processes of the compounds under study. The statistically best QSPR models of six descriptors were simultaneously obtained by GA-based linear regression analysis. With the selected descriptors and the QSPR equations, mechanisms of partition action of the Sulfur-containing carboxylates were able to be investigated and interpreted.
In this work a multi-objective quantitative structure-property relationship (QSPR) analysis approach was reported based on the study on three partition properties of 50 aromatic sulfur-containing carboxylates. Here multi-objective (properties) were taken as a vector for QSPR modeling . The quantitative correlations for partition properties were developed using a genetic algorithm-based variable-selection approach with quantum chemical descriptors derived from AM1-based calculations. With the QSPR models, the aqueous solubility, octanol / water partition coefficients Factors of sulfur-containing compounds were estimated and predicted. Using GA-based multivariate linear regression with cross-validation procedure, a set of the most promising descriptors was selected from a pool of 28 quantum chemical semi-empirical descriptors, including steric and electronic types , to built build QSPR models. The selected molecular descriptors included the net charges on carboxyl group (QOC), the 2nd power of net charges on nitrogen atoms (Q 2 N), the net atomic charge on the sulfur atoms (QS), the van der Waals volume of molecule (V), the most positive net atomic charge on hydrogen atoms (QH) and the measure of polarity and polarizability (π), which were main factors affecting the distribution processes of the compounds under study. The statistically best QSPR models of six descriptors were simultaneously obtained by GA-based linear regression With the selected descriptors and the QSPR equations, mechanisms of partition action of the Sulfur-containing carboxylates were able to be investigated and interpreted.