Exploring QSAR of Non-Nucleoside Reverse Transcriptase Inhibitors by Neural Networks: TIBO Derivatives

Prediction of cytotoxicity data (CC50) of anti-HIV 5-pheny-l-phenylamino-1H-imidazole derivatives by artificial neural network trained with Levenberg–Marquardt algorithm

“…Some applications of ANNs to the QSAR studies of anti-HIV activity of novel compounds are as follows: study inhibition of HIV replication (IC 90 ) for 55 cyclic urea derivatives [20]; predicting anti-HIV activity for a set of 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine (HEPT) derivatives [21]; QSAR analysis for a set of 4,5,6,7-tetrahydro-5-methylimidazo[4,5,1-jk] [1,4]benzodiazepin-2(1H)-ones (TIBO) derivatives [22]; prediction of anti-HIV activity for a set of 107 inhibitors of the HIV-1 reverse transcriptase derivatives of 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine [23][24][25][26] and anti-HIV-1 activities prediction of 20 tetrapyrrole derivatives [27]. Some evidence show that ANNs modeling give better statistical results both in fitting and prediction, in comparison with linear modeling approaches in QSAR studies [22][23][24][25].…”

A hybrid decision trees-adaptive neuro-fuzzy inference system in prediction of anti-HIV molecules

“…For more investigation, stepwise MLR-ANN (Bazoui et al, 2002), 3D QSAR-PLS (Ravichandran et al, 2009) and ANN (Douali et al, 2004;Jalali-Heravi & Parastar, 2000) techniques are used to select the most important descriptors and predicting the anti-VIH activity. To find the best model, MLR and PLS were run sevral times with different settings of initial populations.…”

“…The results of DT-ANFIS were compared to those of stepwise multiple linear regression (MLR) (Bazoui, Zahouily, Boulaajaj, Sebti, & Zakarya, 2002), artificial neural networks (ANN) (Douali, Cherqaoui, & Villemin, 2004;Jalali-Heravi & Parastar, 2000) and 3D QSAR partial least-squares (PLS) (Ravichandran, Prashantha Kumar, Sankar, & Agrawal, 2009). It has been demonstrated that the DT is a useful tool for variable selection comparable to the stepwise MLR, ANN and 3D QSAR-PLS.…”

Computer-Aided Drug Design: An Innovative Tool for Modeling

“…They belong to the category of non-nucleoside inhibitors, which block the reverse transcriptase of the retrovirus and prevent its duplication. We studied a data set of 73 of those compounds, whose activity was previously modeled with several QSAR methods, including conventional neural networks [44], multi-linear regression, comparative molecular field analysis (CoMFA) [45], and the substructural molecular fragments (SMF) method. The latter approach is based on the representation of the molecules with graphs, which are split into fragments, Table 2.…”