AI Article Synopsis
- The study explores the effectiveness of tetrahydroimidazo[4,5,1-jk][1,4]benzodiazepinone (TIBO) derivatives as non-nucleoside reverse transcriptase inhibitors for HIV treatment.
- The researchers utilized support vector machines (SVM) to assess the anti-HIV activity of 82 TIBO compounds based on four molecular descriptors, yielding better statistical results than other methods like multiple linear regression.
- The analysis highlighted the significance of the hydrophobic parameter in understanding structure-activity relationships, allowing for the prediction of anti-HIV properties using only four directly calculable molecular descriptors.
Article Abstract
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.
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| http://dx.doi.org/10.1016/j.ejmech.2010.01.002 | DOI Listing |
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