ABSTRACT
This study aims to develop a QSAR model for Antitubercular activity. The quantitative structure-activity relationship (QSAR) approach predicted the thiazolidine-4-ones derivative's Antitubercular activity. For the QSAR study, 53 molecules with Antitubercular activity on H37Rv were collected from the literature. Compound structures were drawn by ACD/Labs ChemSketch. The energy minimization of the 2D structure was done using the MM2 force field in Chem3D pro. PaDEL Descriptor software was used to construct the molecular descriptors. QSARINS software was used in this work to develop the 2D QSAR model. A series of thiazolidine 4-one with MIC data were taken from the literature to develop the QSAR model. These compounds were split into a training set (43 compounds) and a test set (10 compounds). The PaDEL software calculated 2300 descriptors for this series of thiazolidine 4-one derivatives. The best predictive Model 4, which has R2 of 0.9092, R2adj of 0.8950 and LOF parameter of 0.0289 identify a preferred fit. The QSAR study resulted in a stable, predictive, and robust model representing the original dataset. In the QSAR equation, the molecular descriptor of MLFER_S, GATSe2, Shal, and EstateVSA 6 positively correlated with Antitubercular activity. While the SpMAD_Dzs 6 is negatively correlated with Antitubercular activity. The high polarizability, Electronegativities, Surface area contributions and number of Halogen atoms in the thiazolidine 4-one derivatives will increase the Antitubercular activity.