Open Access
Subscription Access
K Nearest Neighbor and 3D QSAR Analysis of Thiazolidinone Derivatives as Antitubercular Agents
Purpose: The present research communication describes development of kNN and 3D QSAR models for identification of structural features which are responsible for antimycobacterial activity of Thiazolidinone.
Methodology/Approach : In the present work, two predictiveof kNN and 3D QSAR models were developed via utilization of multiple linear regression analysis. MLR analysis was carried out on reported dataset of thiazolidinone as Antimycobacterial. Vlife MDS 4.4 is utilized for development of kNN and 3D QSAR models which were validated via internal test set.
Findings : Two different kNN and 3D QSAR models developed for dataset of thiazolidinone molecules as antimycobacterial. The Model A and Model B describes the best selected 3D QSAR model predicting antimycobacterial activity of the thiazolidinone derivatives. 3D QSAR model A is best selected model which indicates steric interaction fields needs to be minimized while electrostatic interaction field needs to be improved for potential increase in antimycobacterial activity. The Model C and D are two selected kNN models for anti-mycobacterial activity of the thiazolidinone derivatives. Model D is better fitted kNN model describing negative contribution of the electrostatic interaction fields and positive contribution of the steric interaction field.
Original Value : The review of literature revealed QSAR analysis plays vital role in the development of the novel drug like candidates. Thiazolidinone derivatives were reported for their antimycobacterial potential but their quantitative measures were not reported. These facts prompted us to for development of QSAR models which will be utilized for development of potent and selective antimycobacterial agents.
Conclusion : The study revealed that 3DQSAR model A and kNN model D better describes the antimycobacterial potential of the thiazolidinone derivatives. Substitution of the smaller groups on the aromatic ring bearing thiazolidinone nucleus will increase the antimycobacterial potential of the thiazolidinone derivatives.
Keywords
- Lou Z, Zhang X. Protein targets for structure-based antiMycobacterium tuberculosis drug discovery. Protein Cell.2010;1(5):435–42.
- Chopra P, Meena LS, Singh Y. New drug targets for Mycobacterium tuberculosis. Indian J Med Res. 2003; 117:1-9.
- Kamal Ahmed, Azeeza A, Malik MS, Shaik AA, Rao MV. Efforts Towards the Development of New Antitubercular Agents: Potential for Thiolactomycin Based Compounds J Pharm Pharmaceut Sci.2008;11 (2):56-80.
- Mdluli K, Spigelman M. Novel targets for tuberculosis drug discovery CurrOpi-Pharmaco.2006;6:459–467.
- Sharma MC, Sharma S, Sahu NK, Kohli DV. 3D QSAR kNN-MFA studies on 6-substituted benzimidazoles derivatives as Nonpeptide Angiotensin II Receptor Antagonists: A rational approach to antihypertensive agents.J SauChemSoc.2013;17:167–76.
- Sharma MC. Structural requirements of N-aryloxazolidinone5-carboxamide derivatives for anti-HIV protease activity using molecular modelling techniques.J Tai Uni Sci.2014;8:111–23.
- Choudhari PB, Bhatia MS.3D QSAR, pharmacophore identification studies on series of 1-(2-ethoxyethyl)-1hpyrazolo [4,3-d]pyrimidines as phosphodiesterase V inhibitors J SauChemSoc.2015;19(3):265–73.
- Desai SA, Kumbhar SS, Katti VS, Choudhari PB, Bhatia M. S. 3D QSAR and Pharmacophore Modelling on Chalcones as Antileishmanial Agents potential Trypanothionereductase Inhibitors. JAppPharma Sci. 2013;3: 99-02.
- Choudhari PB, Bhatia MS, Bhatia NM. Application of pocket modeling and k nearest neighbour molecular field analysis (kNN-MFA) for designing of some anticoagulants: potential factor IXa inhibitors. Med Chem Res.2013;22:976-85.
- Malipeddi H, Karigar AA, Malipeddi VR, Sikarwar MS. Synthesis and Antitubercular Activity of Some Novel Thiazolidinone Derivatives. TroJ of Pharm Res. 2012; 11 (4):611-20.
Abstract Views: 235
PDF Views: 119