Exploring QSAR of non-nucleoside reverse transcriptase inhibitors by artificial neural networks: HEPT derivatives

Abstract

Artificial neural networks (ANNs) can be utilized to generate predictive models of quantitative structure-activity relationships (QSAR) between a set of molecular descriptors and activity. In the present work, QSAR analysis for a set of 95 1-[(2-hydroxyethoxy)-methyl]-6-(phenylthio)thymine (HEPT) derivatives has been investigated by means of a three-layered neural network (NN). It has been shown that NN can be a potential tool in the investigation of QSAR analysis compared with the models given in the literature. The results obtained by using the NN adopted for QSAR models showing not only good statistical significance in fitting, but also high predictive ability. (0.916< r <0.968 and q 2 = 0.8779). The relevant factors controlling the anti-HIV-1 activity of HEPT derivatives have been identified. The results are along the same lines as those of our previous studies on HEPT derivatives and indicate the importance of the hydrophobic parameter in modelling the QSAR for HEPT derivatives

Evaluation of the Model Prediction Toxicity (Ld50) for Series of 42 Organophosphorus Pesticides

“…Given the large number of 63 descriptors used to code each molecule, we subjected our data to Stepwise stepwise selection [14,15,16], in order to highlight the most relevant descriptors. The model is statistically significant and accounts for up to 63% of initial information.…”

Chemistry and Biological Activity of Coumarins at Molecular Level

“…Non-nucleoside inhibitors block RT by binding to a pocket adjacent to the catalytic site of the enzyme and thereby disrupting the conformation of several amino acids essential for a proper function. In this context, the NNRTIs have increased their importance by their specificity and low cytotoxicity [14]. Another potential target is DNA polymerases, which represent important cellular targets in the development of anticancer and antiviral agents [15,16].…”