Quantitative structure activity relationship (QSAR) modeling study of some novel thiazolidine 4-one derivatives as potent anti-tubercular agents.

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.

Data on molecular docking of naturally occurring flavonoids with biologically important targets.

Flavonoids in nature are known to possess various activities such as anti-inflammatory, antimicrobial, anticancer, antioxidant, neuroprotective, anti-HIV activities etc., The molecular docking was performed by 26 naturally occurring flavonoids with selected targets COX-2, hydroxyacyl-ACP dehydratase, tyrosinase from Agaricus bisporus, isomaltase from Saccharomyces cerevisiae, Human IkB kinase beta, Human ABC transporter, topoisomerase II, topoisomerase IV, N-myristoyltransferase from Candida albicans, Peptide deformylase from Pseudomonas aeruginosa, polypeptide deformylase from Streptococcus pneumoniae. The analysis was based on docking score, glide energy, interactions type (bond type and distance) and interaction with amino acids. The top 5 flavonoids with best docking score was reported. The in-silico results provided for 26 naturally occurring flavonoid shows that they reduce the risk of inflammation, cancer and infectious disease if people have taken in diet continuously. The provided docking data of flavonoids may be useful to synthesis novel drug candidate for the mentioned targets.