Support vector machines: development of QSAR models for predicting anti-HIV-1 activity of TIBO derivatives

European Journal of Medicinal Chemistry
R DarnagD Cherqaoui

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.

Citations

Jun 26, 2013·Journal of Chemical Information and Modeling·Hongming ChenIngemar Nilsson
Jul 28, 2012·International Journal of Molecular Sciences·Ming HaoGuohui Li
Aug 16, 2016·Journal of Cheminformatics·Jonathan AlvarssonOla Spjuth
Nov 30, 2018·Expert Opinion on Drug Discovery·Antonio Jesús Banegas-LunaHoracio Pérez-Sánchez
Apr 23, 2019·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Fatima GhrifiAzeddine Ibrahimi
Jul 18, 2018·Pharmaceuticals·Ismail HdoufaneDriss Cherqaoui
Jul 17, 2020·Theoretical Biology & Medical Modelling·Galina Samigulina, Zarina Samigulina
Dec 30, 2020·Journal of Cheminformatics·Gabriel IdakwoPing Gong
Jan 19, 2019·Journal of Chemical Information and Modeling·Beatrice PecoraroMatthew Traynor

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