Bayesian Regularized Artificial Neural Network Modeling of the Anti‐protozoal Activities of 1‐Methylbenzimidazole Derivatives AgainstT. VaginalisInfection
Mansour Arab Chamjangali, Mahdi Mohammadrezaei, Zahra Kalantar, Amir Hossein Amin · Journal of the Chinese Chemical Society · 2012
Abstract In this work, we propose a quantitative structure‐activity relationship (QSAR) approach in order to model the anti‐protozoal activities of 24 derivatives of 1‐methylbenzimidazole against trichomoniasisvaginalis. A QSAR model was built using the Bayesian regularized artificial neural network (BR‐ANN). A total of 7 molecular descriptors, as the most feasible ones, were selected to build the QSAR model. Some of the selected descriptors seem to relate to the presence of ester group and chlorine atoms at the inner atmosphere of the compounds to increase the anti‐protozoal activity. The robustness and predictability of the proposed model were verified by the test data set and leave‐one‐out cross‐validation methods. The mean square errors (MSEs) and mean relative errors (MREs) for the test data set are 0.0049 and 0.82%, respectively, and for the leave‐one‐out method, they are 0.0031 and 0.75%, respectively. Finally, it was proved that the QSAR model is a useful tool in prediction of the anti‐protozoal activities of 1‐methylbenzimidazole compounds, and that it could be used to guide the synthesis of new 1‐methylbenzimidazole derivatives with better activities.