Integrated QSAR, Docking, and Molecular Dynamics‐Based Discovery of 1,4‐Naphthoquinone Derivatives as Potential Anti‐Tuberculosis Agents

ABSTRACT This study examines seventy‐one 1,4‐naphthoquinone scaffold‐bearing compounds with known half‐maximal inhibitory concentration (IC 50 ) against Mycobacterium tuberculosis (Mtb) using QSAR, docking, and molecular dynamics (MD) simulations. The molecular descriptors were computed using PaDEL and ChemDes to create multiple linear regression (MLR) based predictive 2D QSAR models through QSARINS v2.2.4. The statistically suitable five‐descriptor QSAR model demonstrated a correlation coefficient ( R 2 0.7136) and a cross‐validated R 2 ( Q 2 LOO 0.6599). The model exhibited lower values for root mean squared error (RMSE tr 0.2298) and mean absolute error (MAE tr 0.1738), along with a higher concordance correlation coefficient (CCC 0.8329), indicating strong fitness and predictive accuracy. In silico screening of all compounds for physicochemical and medicinal chemistry parameters, followed by docking against five key Tb pathogenesis proteins using Cresset Flare 10.0.1, identified 24 leading candidates. A 200 ns MD simulation revealed good protein‐ligand complex stability of two compounds, 52 and 70 , which was further supported by MM/GBSA calculation. SAR analysis demonstrates that introducing chlorine into the quinone scaffold, in combination with highly lipophilic aryl substituents such as trifluoromethyl, significantly enhances binding affinity. Considering suitable druggability parameters, we suggest compound 70 for further research to confirm its potential as an effective anti‐TB drug.