In Silico Approach for Developing New Anti-Tuberculosis Drug Candidates: 3D-QSAR, Molecular Docking and ADME Studies of Pretomanid Derivatives

Tuberculosis (TB) is one of the top ten causes of mortality worldwide, necessitating the discovery of new molecules with potential anti-tuberculosis activity. In this study, Pretomanid derivatives as potent anti-TB agents were collected from the literature to generate a 3D-QSAR model and conduct molecular docking. The 3D-QSAR model was successfully generated with a high regression coefficient R²= 0.98 and an excellent cross-validated determination coefficient Q2cv= 0.51 for the training set. Furthermore, the model developed showed good predictive ability, with a high predictive value Q2= 0.75 for the test set. The generated 3D contour cubes were applied to find the structural properties necessary to inhibit Deazaflavin-dependent nitroreductase. Then, the results were used to discover novel molecules with a potential anti-tuberculosis activity using the structure-based virtual screening. Based on successful results obtained by virtual screening, twelve compounds were selected as potential inhibitors of the Ddn with highly predicted activities, binding interactions, and acceptable ADME properties.