In a series of publications Estrada (Estrada, E. J. Chem. Inf. Comput. Sci. 1996, 36, 844-849; 1997 37, 320-328; 1998, 38, 23-27) employed spectral moments of line graphs in QSPR and QSAR relationship studies of various classes of compounds. A recent paper (Marković, S.; Gutman, I. J. Chem. Inf Comput. Sci. 1999, 39, 289-293) reported that in QSPR and QSAR investigations of benzenoid hydrocarbo...

The aim of the present paper is to present the methodology of the molecular descriptors family (MDF) as an integrative tool in molecular modeling and its abilities as a multivariate QSAR/QSPR modeling tool. An algorithm for extracting useful information from the topological and geometrical representation of chemical compounds was developed and integrated to calculate MDF members. The MDF method...

Background: Support vector machine (SVM), a novel powerful machine learning technology, was used to develop the non-linear quantitative structure-property relationship (QSPR) model of the G/11 xylanase based on the amino acid composition. The uniform design (UD) method was applied to optimize the running parameters of SVM for the first time. Results: Results showed that the predicted optimum te...

We develop the idea that the use of ad hoc molecular descriptors in QSAR/QSPR studies is not an optimal solution. Instead, we propose to optimize these descriptors for the specific properties under study. In the case of topological indices (TIs) we propose the use of the generalized topological indices (GTIs), which account for several of the classical TIs in one single graph invariant. GTIs re...

Aqueous solubility of drug compounds plays a very important role in drug research and development. In this study, we have collected 225 diverse druglike molecules with accurate aqueous solubility. Three commonly used methods, namely partial least squares (PLS), back-propagation network (BPN) and support vector regression (SVR), were employed to model quantitative structure–property relationship...