Application of radial basis function networks and successive projections algorithm in a QSAR study of anti-HIV activity for a large group of HEPT derivatives

A series of 1-[2-hydroxyethoxy-methyl]-6-(phenylthio)thymine] (HEPT) derivatives, as normucloside reverse transcriptase inhibitors (NNRTIs), was investigated using a nonlinear quantitative structure-anti-HIV-1-activity relationship QSAR) study. Molecular descriptors derived solely from molecular structure were used to represent molecular structure. Utilizing successive projections algorithm (SPA) and a stepwise backward elimination, a subset of 11 descriptors were selected. Application of SPA minimizes the collinearity between the selected descriptors, which are known to be responsible for the anti-HIV-1 activity. Three layer radial basis function networks (RBFNs) were used to construct the nonlinear QSAR models in all stages of study. The relative standard error percent in anti-HIV activity predictions for the training set by the application of cross-validation (RSECV%) was 9.94%, and for prediction set (RSEP%) was 9.99%. The obtained model outperforms those given in the literature in both the fitting and predicting stages. RBFN analysis yielded predicted activities in the excellent agreement with the experimentally obtained values (cross-validation r = 0.927, prediction r = 0.925). Copyright (C) 2006 John Wiley & Sons, Ltd.