Predicting anti-HIV-1 activities of HEPT-analog compounds by using support vector classification

Wencong Lu, Ning Dong, Gábor Náray-Szabó

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15 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1021-1025
Number of pages5
JournalQSAR and Combinatorial Science
Volume24
Issue number9
DOIs
Publication statusPublished - Nov 1 2005

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Keywords

  • HEPT-analogue compounds
  • PM3
  • Structure-activity relationship
  • Support vector classification
  • Support vector machine

ASJC Scopus subject areas

  • Drug Discovery
  • Computer Science Applications
  • Organic Chemistry

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