Training and test sets The chosen inhibitors underwent Molecular Docking calculations using 11 different anti-TB receptors from the Protein Data Bank (PDB). Every inhibitor had thirty docked poses created for it. Successful docking was observed for all 1000 inhibitors across 11 receptors (1DF7, 1G3U, 1MDB, 1UZN, 1ZID, 2AQK, 2CIB, 2DFT, 2FUM, 3ORM, and 4U0J). However, the docking calculations using 1UZN and 2FUM failed for several ligands. The highest-scored docked poses from receptors 2CIB, 1ZID, and 4U0J exhibited the best correlation between experimental and predicted activity values. Consequently, receptors 2CIB and 1ZID were chosen for further analysis. Evaluation of the first and highest-scored docked poses was based on ligand interaction diagrams. Table displays ligand interaction diagrams for selected inhibitors, summarizing their interactions with the receptors in terms of the highest-scored docked poses. Notably, GLY14 residue displayed the highest number of interactions, followed by GLY96, ALA98, ILE122, and MET147, indicating their primary involvement in interactions with the 2CIB and 1ZID receptors. Receptors 2DFT, 2FUM, and 1G3U were generally associated with hydrogen bonding, while stacking interactions involved ARG153 and LYS13. Additionally, other types of ligand interactions were found to be crucial for binding, with ALA22, ASP148, ASP94, MET61, and ILE201 generally participating in these interactions. MLR analysis MLR analysis was conducted for all potential combinations of descriptors on the chosen anti-TB inhibitors. Traditional descriptors, including constitutional, geometrical, topological, electrostatic, and quantum chemical features, were computed using the CODESSA program . Conceptual DFT-based descriptors such as hardness, chemical potential, and electrophilicity index were manually calculated. Additionally, docking-based descriptors, namely docking fitness score, H-bond, and van der Waals interactions, were obtained from the docking results of both AutoDock and GOLD programs . Different numbers of descriptors were used in regression analyses to ensure mathematical significance and robustness. Next, the optimal quantity of descriptors for the finished model was determined. A summary of the relapse investigation for different combinations of descriptors is provided in Table . The models deemed generally reasonable for the Set-1 through Set-12 datasets are listed in Table , along with the important measurable boundaries associated with each model.