3D-QSAR studies on fluroquinolones derivatives as inhibitors for tuberculosis

Abstract

A quantitative structure activity relationship (QSAR) study was performed on the fluroquinolones known to have anti-tuberculosis activity. The 3D-QSAR models were generated using stepwise variable selection of the four methods-multiple regression (MR), partial least square regression (PLSR), principal component regression (PCR) and artificial neural networks (kNN-MFA). The statistical result showed a significant correlation coefficient q 2 (90%) for MR model and an external test set of (pred_r 2)-1.7535, though the external predictivity showed to improve using kNN-MFA method with pred_r 2 of-0.4644. Contour maps showed that steric effects dominantly determine the binding affinities. The QSAR models may lead to a better understanding of the structural requirements of anti-tuberculosis compounds and also help in the design of novel molecules.