Developing QSAR Models for the Identification of Inhibitors Targeting <i>Mycobacterium tuberculosis</i> Enoyl-ACP Reductase Enzyme

ABSTRACT Tuberculosis is a global concern due to its high prevalence in developing countries and the ability of mycobacteria to develop resistance to current treatment regimens. In this project, we propose the use of QSAR (Quantitative Structure-Activity Relationships) modeling as a means to identify and evaluate the inhibitory activity of candidate molecules for molecular improvement stages and/or in vitro assays. This approach allows for in silico estimation, reducing research time and costs. To achieve this, we utilized the SAR (Structure-Activity Relationships) study conducted by He, Alian, and Montellano (2007), which focused on a series of arylamides tested as inhibitors of the enzyme enoyl-ACP-reductase (InhA) in Mycobacterium tuberculosis . We developed both the Hansh-Fujita (classical) and CoMFA (Comparative Molecular Field Analysis) QSAR models. The classical QSAR model produced the most favorable statistical results using Multiple Linear Regression (MLR). It achieved an internal validation correlation factor R 2 of 0.9012 and demonstrated predictive quality with a Stone-Geisser indicator Q 2 of 0.8612. External validation resulted in a correlation factor R 2 of 0.9298 and Q 2 of 0.720, indicating a highly predictive mathematical model. The CoMFA Model obtained a Q 2 of 0.6520 in internal validation, enabling the estimation of energy fields around the molecules. This information is crucial for molecular improvement efforts. We constructed a library of small molecules, analogous to those used in the SAR study, and subjected them to the classic QSAR function. As a result, we identified ten molecules with high estimated biological activity. Molecular docking analysis suggests that these ten analogs, identified by the classical QSAR model, exhibit favorable estimated free energy of binding. In conclusion, the QSAR methodology proves to be an efficient and effective tool for searching and identifying promising drug-like molecules.