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Journal of Pathogens
Volume 2018, Article ID 1018694, 24 pages
Research Article

QSAR Modeling and Molecular Docking Analysis of Some Active Compounds against Mycobacterium tuberculosis Receptor (Mtb CYP121)

Department of Chemistry, Ahmadu Bello University, Zaria, Nigeria

Correspondence should be addressed to Shola Elijah Adeniji; moc.liamg@3434alohs

Received 28 January 2018; Revised 26 February 2018; Accepted 13 March 2018; Published 10 May 2018

Academic Editor: Nongnuch Vanittanakom

Copyright © 2018 Shola Elijah Adeniji et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A quantitative structure-activity relationship (QSAR) study was performed to develop a model that relates the structures of 50 compounds to their activities against M. tuberculosis. The compounds were optimized by employing density functional theory (DFT) with B3LYP/6-. The Genetic Function Algorithm (GFA) was used to select the descriptors and to generate the correlation model that relates the structural features of the compounds to their biological activities. The optimum model has squared correlation coefficient () of 0.9202, adjusted squared correlation coefficient () of 0.91012, and leave-one-out (LOO) cross-validation coefficient () value of 0.8954. The external validation test used for confirming the predictive power of the built model has pred value of 0.8842. These parameters confirm the stability and robustness of the model. Docking analysis showed the best compound with high docking affinity of −14.6 kcal/mol which formed hydrophobic interaction and hydrogen bond with amino acid residues of M. tuberculosis cytochromes (Mtb CYP121). QSAR and molecular docking studies provide valuable approach for pharmaceutical and medicinal chemists to design and synthesize new anti-Mycobacterium tuberculosis compounds.