Using classification structure pharmacokinetic relationship (SCPR) method to predict drug bioavailability based on grid-search support vector machine

Abstract

The linear discriminant analysis (LDA) and the grid-search support vector machine (GS-SVM) were used to develop classification structure pharmacokinetic relationship models for predicting drug bioavailability. Bioavailability data for 167 compounds were taken from the literature, and the molecular descriptors were generated from the software CODESSA solely from molecular structures. Five descriptors selected by LDA were used to build the linear and nonlinear models. The obtained results confirmed the discriminative capacity of the calculated descriptors and the relationship with the drug bioavailability. The result of GS-SVM (total accuracy of 85.6%) was better than that of LDA (total accuracy of 72.4%), which indicated that the GS-SVM model was more reliable in the recognition of the drug bioavailability. The proposed method was very useful for the selection of new drugs products, and can also be extended in other classification structure pharmacokinetic relationship (CSPR) and classification structure activity relationship (CSAR) investigation.