World Health Organization has reported that 14 million people worldwide are infected with active tuberculosis and over 1.7 million deaths occur every year. There are many drugs available in the market for treating tuberculosis, but the emergence of tuberculosis is due to the appearance of Multi Drug Resistance (MDR) against one or more of the 1st line antimycobacterial drug. Therefore, there is a need to explore and develop newer structural moiety as antitubercular drug. In the present study CORAL software was used for constructing large-scale QSAR models for predicting the antitubercular activity of 24 pyrazoline and benzoxazole based chalcones on the Monte Carlo approach. Further these 24 target molecules were subjected to docking for finding out the interactions of the molecules with various targets of mycobacterium species. Computational results indicated that this approach can satisfactorily predict the desired activity with very good statistical significance. For best built model statistical parameters were R2=0.8813 and Q2=0.8031 for test set and R2=0.6124 and Q2=0.4914 for training set. Additionally, molecular docking study was performed for finding out the interactions of the molecules with various targets of mycobacterium species. Monte Carlo method proved to be an efficient approach to build up a robust model for estimating. Based on QSAR and molecular docking studies, some important physicochemical parameters of pyrazoline moiety could be assessed for antitubercular drugs.
