Exploring QSAR of non-nucleoside reverse transcriptase inhibitors by artificial neural networks: HEPT derivatives
Mohamed Zahouily, Jamila Rakik, Mohamed Lazar, Moulay Abdellah Bahlaoui, Ahmed Rayadh, Najia Komiha · ARKIVOC · 2007
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