MOLECULAR MODELLING OF 1,3,4-OXADIAZOLE DERIVATIVES AS ANTIMYCOBACTERIAL AGENTS

Abstract

Recently several 1,3,4-oxadiazole derivatives have been identified as potentially active antimycobacterial agents. Various 5-aryl-2-thio-1,3,4-oxadiazoles have been reported having good antimycobacterial activity against Mycobacterium tuberculosis H37Rv (ATCC 27294). In this paper we report 3D QSAR studies for the 41 molecules of 1,3,4-oxadiazoles by using k-Nearest Neighbor Molecular Field Analysis (kNN-MFA) combined with various selection procedures. Using kNN-MFA approach 52 3D-QSAR models were generated; one of these models was selected on the basis of q2 and pred_r2 values. The selected model had shown good internal and external predictivity for the training set of 33 molecules and test set of 8 molecules with validation (q2) and cross validation (pred_r2) values of 0.5022 and 0.2898, respectively. This model can be used for preliminary screening of large diversified compound libraries. The model has shown that presence of sulphur is must for activity, however the larger bulky substituents reduce the activity. The presence of halogen and other non-halogen groups have also contributed to the activity. Hence the future schemes with smaller groups on sulphur and electronegative groups in the molecule would result in potentially active molecules.