In this paper, the comparison of orthogonal descriptors and Leaps and Bounds regression analysis is performed. The results obtained by using orthogonal descriptors are better than that obtained by using Leaps and Bounds regression for the data set of nitrobenzenes used in this study. Leaps and Bounds regression can be used effectively for selection of variables in quantitative structure activity/property relationship(QSAR/QSPR) studies. Consequently, orthogonalisation of descriptors is also a good method for variable selection for studies on QSAR/QSPR.
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.
