| dc.description.abstract | Leishmaniasis is a disease caused by a protozoan parasites belonging to the genus Leishmania. It causes morbidity and mortality in the tropical and subtropical regions. Current drugs are toxic, expensive, and require long-term treatment. Thus, identification and development of novel, cheap, efficient, and safe antileishmanial compounds as drug candidates are important from pharmaceutical point of view. Quantitative structure-activity relationship (QSAR) methods are used to predict the pharmaceutically relevant properties of drug candidates whenever it is applicable. The aim of this study was to use two different techniques, namely multiple linear regression (MLR) and artificial neural networks (ANNs) in predicting the antileishmanial activity (i.e. pIC(50)) of 5-(5-nitroheteroaryl-2-yl)-1,3,4-thiadiazole derivatives. To this end, genetic algorithm-coupled partial least square and backward multiple regression method were used to select a number of calculated molecular descriptors to be used in MLR and ANN-based QSAR studies. The predictive power of the models was also assessed using leave-one-out and leave-group-out cross validation methods. Also, molecular modeling studies were conducted based on DNA topoisomerase I to identify the binding interactions responsible for antileishmanial activity of those analogs. The results suggest that hydrogen bonding interactions and several hydrophobic interactions of ligands with the active site of Leishmania major topoisomerase IB are responsible for their potent antileishmanial activity. These results can be exploited for structure-based computer-aided drug designing of new and selective leishmania topoisomerase inhibitors. Copyright (C) 2016 John Wiley & Sons, Ltd. | |
