The investigation utilized molecular docking scores and quantitative structure-property relationships (QSAR) for an analysis of 1000 molecules. The study compared conceptual DFTbased descriptors with conventional descriptors such as constitutional, topological, docking, geometrical electrostatic, and quantum chemical descriptors. Notably, quantum chemical descriptors emerged as crucial, and the incorporation of conceptual DFT descriptors notably enhanced the statistical robustness of the model across various scenarios. A total of 12 QSAR models were established through multiple linear regression (MLR) and partial least squares, utilizing data gathered from various biological activities of anti-TB compounds. The calculated descriptors were based on conceptual density functional theory, specifically focusing on hardness, chemical potential, and electrophilicity index.