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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
chemistry
Support vector machines: Development of QSAR models for predicting anti-HIV-1 activity of TIBO derivatives
European Journal of Medicinal Chemistry, Volume 45, No. 4, Year 2010
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Description
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. © 2010 Elsevier Masson SAS. All rights reserved.
Authors & Co-Authors
Darnag, Rachid
Morocco, Marakech
Faculté Des Sciences Semlalia
Mostapha Mazouz, E. L.
Morocco, Marakech
Faculté Des Sciences Semlalia
Schmitzer, Andréea Ruxandra
Canada, Montreal
University of Montreal
Villemin, Didier
France, Caen
École Nationale Supérieure D’ingénieurs de Caen
Jarid, Abdellah
Morocco, Marakech
Faculté Des Sciences Semlalia
Cherqaoui, Driss
Morocco, Marakech
Faculté Des Sciences Semlalia
Statistics
Citations: 62
Authors: 6
Affiliations: 3
Identifiers
Doi:
10.1016/j.ejmech.2010.01.002
ISSN:
02235234
e-ISSN:
17683254
Research Areas
Infectious Diseases
Study Approach
Quantitative