Support vector machines: development of QSAR models for predicting anti-HIV-1 activity of TIBO derivatives.
2010
The tetrahydroimidazo[4,5,1-jk][1,4]benzodiazepinone (TIBO) derivatives, as non-nucleoside reverse transcriptase inhibitors, acquire a significant place in the treatment of the infections by the HIV. In the present paper, the support vector machines (SVM) are used to develop quantitative relationships between the anti-HIV activity and four molecular descriptors of 82 TIBO derivatives. The results obtained by SVM give good statistical results compared to those given by multiple linear regressions and artificial neural networks. The contribution of each descriptor to structure-activity relationships was evaluated. It indicates the importance of the hydrophobic parameter. The proposed method can be successfully used to predict the anti-HIV of TIBO derivatives with only four molecular descriptors which can be calculated directly from molecular structure alone.
Keywords:
- Support vector machine
- Linear regression
- Organic chemistry
- Artificial intelligence
- Structure–activity relationship
- Molecular descriptor
- Stereochemistry
- Quantitative structure–activity relationship
- Pattern recognition
- Chemistry
- Derivative (finance)
- human immunodeficiency virus
- Artificial neural network
- anti hiv 1
- Correction
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