Title

Authors

Affiliation

¹Department of Biotechnology and Bioinformatics, North Eastern Hill University, Permanent campus, Shillong-793022, India; ²Centre for Advanced studies in Crystallography and Biophysics, University of Madras, Guindy (Maraimalai) Campus, Chennai- 600 025, India.

Email

atanubioinfo@gmail.com; *Corresponding author

Article Type

Hypothesis

Date

Received April 13, 2012; Accepted April 16, 2012; Published April 30, 2012

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 q2 (90%) for MR model and an external test set of (pred_r2) -1.7535, though the external predictivity showed to improve using kNN-MFA method with pred_r2 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.

Keywords

3D-QSAR, Mycobacterium tuberculosis, fluroquinolones, k-nearest neighbor molecular field analysis, Multiple regression, Partial least square regression, Principle component regression.

Citation

Bhattacharjee et al. Bioinformation 8(8): 381-387 (2012)
 

Edited by

P Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.