Journal
EUROPEAN JOURNAL OF MEDICINAL CHEMISTRY
Volume 44, Issue 5, Pages 2158-2171Publisher
ELSEVIER FRANCE-EDITIONS SCIENTIFIQUES MEDICALES ELSEVIER
DOI: 10.1016/j.ejmech.2008.10.021
Keywords
QSAR; HIV-1 non-nucleoside reverse transcriptase inhibitors; NNRTI; SVM; PPR
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Funding
- National Natural Science Foundation of China [90612016, 60773108]
- Ministry of Science and Technology of China [2005DKA64001]
- China Research Council
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A quantitative structure-activity relationship study of a series of HIV-1 reverse transcriptase inhibitors (2-amino-6-arylsulfonylbenzonitriles and their thio and sulfinyl congeners) was performed. Topological and geometrical, as well as quantum mechanical energy-related and charge distribution-related descriptors generated from CODESSA, were selected to describe the molecules. Principal component analysis (PCA) was used to select the training set. Six techniques: multiple linear regression (MLR), multivariate adaptive regression splines (MARS), radial basis function neural networks (RBFNN), general regression neural networks (GRNN), projection pursuit regression (PPR) and support vector machine (SVM) were used to establish QSAR models for two data sets: anti-HIV-1 activity and HIV-1 reverse transcriptase binding affinity. Results showed that PPR and SVM models provided powerful capacity of prediction. (C) 2008 Elsevier Masson SAS. All rights reserved.
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