Correlation analysis of the structures and pK(a) values of carboxylic acids

The quantum-chemical descriptors were used for QSPR study of the structures of carboxylic acids and their pK(a) values. The algorithm of "Leaps and Bounds" regression was performed for selection of the variables. The CoMFA method was carried out for 3D-QSPR. As the introduction of the charge of oxygen atom(Q(2)), the results obtained by CoMFA were improved greatly.

Structure-activity relationships for antiplasmodial activity among aminoquinolines

The extended gravitational index G(Q) and quantum-chemical descriptors were calculated for the relationship analysis of aminoquinolines. An evolutionary algorithm was described for variable selection and building QSAR models. And the quasi-newton neural networks were employed with better results.

Comparison of different methods for variable selection

In chemistry for chemical analysis of a multi-component sample or quantitative structure-activity/property relationship (QSAR/QSPR) studies, variable selection is a key step. In this study, comparisons between different methods were performed. These methods include three classical methods such as forward selection, backward elimination and stepwise regression; orthogonal descriptors; leaps-and-bounds regression and genetic algorithm. Thirty-five nitrobenzenes were taken as the data set. From these structures quantum chemical parameters, topological indices and indicator variable were extracted as the descriptors for the comparisons of variable selections. The interesting results have been obtained. (C) 2001 Elsevier Science B.V. All rights reserved.

Prediction of C-13-nuclear magnetic resonance chemical shifts for aliphatic amines

Prediction of C-13-nuclear magnetic resonance chemical shifts for aliphatic amines is performed. The topological, geological and electronic descriptors are generated. To reduce the variables, the best subsets of the descriptors are obtained by using leaps-and-bounds regression analysis. The model is achieved using multiple regression with satisfactory results.

Prediction of soil adsorption coefficients from retention parameters on three reversed-phase liquid chromatographic columns

Reversed-phase high-performance liquid chromatographic (RP-HPLC) retention parameters, which are determined by the intermolecular interactions in retention process, can be considered as the chemical molecular descriptors in linear free energy relationships (LFERs). On the basis of the characterization and comparison of octadecyl-bonded silica gel (ODS), cyano-bonded silica gel (CN), and phenyl-bonded silica gel (Ph) columns with linear solvation energy relationships (LSERs), a new multiple linear regression model using RP-HPLC retention parameters on ODS and CN columns as variables for estimation of soil adsorption coefficients was developed. It was tested on a set of reference substances from various chemical classes. The results showed that the multicolumn method was more promising than a single-column method was for the estimation of soil adsorption coefficients. The accuracy of the suggested model is identical with that of LSERs.