Predicting Anti‐HIV‐1 Activities of HEPT‐analog Compounds by Using Support Vector Classification
Wencong Lu, Ning Dong, Gábor Náray‐Szabó · QSAR & Combinatorial Science · 2005
Abstract The support vector classification (SVC), as a novel approach, was employed to make a distinction within a class of non‐nucleoside reverse transcriptase inhibitors. 1‐[(2‐hydroxyethoxy) methyl]‐6‐(phenyl thio)‐thymine (HEPT) derivatives with high anti‐HIV‐1 activities and those with low anti‐HIV‐1 activities were compared on the basis of the following molecular descriptors: net atomic charge on atom 4, molecular volume, partition coefficient, molecular refractivity, molecular polarisability and molecular weight. By using the SVC, a mathematical model was constructed, which can predict the anti‐HIV‐1 activities of the HEPT‐analogue compounds, with an accuracy of 100% as calculated on the basis of the leave‐one‐out cross‐validation (LOOCV) test. The results indicate that the performance of the SVC model exceeds that of the stepwise discriminant analysis (SDA) model, for which a prediction accuracy of 94% was reported.