论文信息 - Predicting Anti‐HIV‐1 Activities of HEPT‐analog Compounds by Using Support Vector Classification

Predicting Anti‐HIV‐1 Activities of HEPT‐analog Compounds by Using Support Vector Classification

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.

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