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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/6-31G ⁎ . 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 ( R  2 ) of 0.9202, adjusted squared correlation coefficient ( R  adj ) of 0.91012, and leave-one-out (LOO) cross-validation coefficient ( Q  cv  2 ) value of 0.8954. The external validation test used for confirming the predictive power of the built model has  R  2 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.

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