• Jun M Kalita Department of pharmaceutical Sciences, Dibrugarh University.
  • Surajit K Ghosh
  • Supriya Sahu
  • Mayurakhi Dutta


Objective: The objective of the study was to determine a suitable and reliable docking protocol based on a statistical study.
Methods: Statistical analysis was done to find out the co-relation between in-silico and in-vitro results.
Results: All the docking method shown acceptable root mean square deviation (RMSD) value those were found to be less than that of 2 Ã…. Coefficient
of co-relation value was also quite satisfactory with highest of r = 0.6574 and rs=0.8322 for LigandFit.
Conclusion: Among the three docking method used for the study, LigandFit was found to be more appropriate as it shown low RMSD and high
co‑relation coefficient.

Keywords: Dihydrofolate reductase, 1J3I, LigandFit, CDOCKER, Gold, Karl Pearson's coefficient of correlation, Spearman's rank correlation (rs).

Author Biography

Jun M Kalita, Department of pharmaceutical Sciences, Dibrugarh University.

reserch scholar, Department of pharm sciences, dibrugarh university, assam


1. Kitchen DB, Decornez H, Furr JR, Bajorath J. Docking and scoring in virtual screening for drug discovery: Methods and applications. Nat Rev Drug Discov 2004;3(11):935-49.
2. Kumar GP, Sharmila JS, Murugan S. Docking of ctx-m-9 group of enzymes with drugs and inhibitors and their evolutionary relationship. Asian J Pharm Clin Res 2014;7(1):237-42.
3. Kirchmair J, Markt P, Distinto S, Wolber G, Langer T. Evaluation of the performance of 3D virtual screening protocols: RMSD comparisons, enrichment assessments, and decoy selection – What can we learn from earlier mistakes? J Comput Aided Mol Des 2008;22(3‑4):213‑28.
4. Daisy P, Niveda RP, Bakiya RH. In silico drug designing approach for biotin protein ligase of Mycobacterium tuberculosis. Asian J Pharm Clin Res 2013;6(1):103-7.
5. McGaughey GB, Sheridan RP, Bayly CI, Culberson JC, Kreatsoulas C,Lindsley S, et al. Comparison of topological, shape, and docking methods in virtual screening. J Chem Inf Model 2007;47(4):1504-19.
6. Sutherland JJ, Nandigam RK, Erickson JA, Vieth M. Lessons in molecular recognition 2. Assessing and improving cross-docking accuracy. J Chem Inf Model 2007;47(6):2293-302.
7. Yuriev E, Agostino M, Ramsland PA. Challenges and advances in computational docking: 2009 in review. J Mol Recognit 2011;24(2):149‑64.
8. Venkatachalam CM, Jiang X, Oldfield T, Waldman M. LigandFit: A novel method for the shape-directed rapid docking of ligands to protein active sites. J Mol Graph Model 2003;21(4):289-307.
9. Vidhya VG, Bhaskar A, Vijayasri S. Molecular docking analysis of sorbitol dehydrogenase using Ligandfit algorithm. Indian J Bioinform Biotechnol 2013;2(5):95-9.
10. Neelakantan S. Structure based drug designing of p38 map kinase inhibitors for the treatment of osteoarthritis. Rasayan J Chem 2011;4(1):210-6.
11. Thangapandian S, Krishnamoorthy N, John S, Sakkiah S, Lazar P, Lee Y, et al. Pharmacophore modeling, virtual screening and molecular docking studies for identification of new inverse agonists of human histamine H1 receptor. Bull Korean Chem Soc 2010;31(1):52-8.
12. Taufer M, Crowley M, Price D, Chien AA, Brooks CL. Study of a highly accurate and fast protein-ligand docking algorithm based on molecular dynamics. Concurr Comput 2005;17(14):1627-41.
13. Akdo˘gan ED, Erman B, Yelekc K. In silico design of novel and highly selective lysine-specific histone demethylase inhibitors. Turk J Chem 2011;35:523-42.
14. Jones G, Willett P, Glen RC, Leach AR, Taylor R. Development and validation of a genetic algorithm for flexible docking. J Mol Biol 1997;267(3):727-48.
15. Verdonk ML, Cole JC, Hartshorn MJ, Murray CW, Taylor RD. Improved protein-ligand docking using GOLD. Proteins 2003;52(4):609-23.
16. Hauke J, Kossowski T. Comparison of values of Pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geogr 2011;30(2):87-93.
17. Swann SL, Brown SP, Muchmore SW, Patel H, Merta P, Locklear J, et al. A unified, probabilistic framework for structure- and ligand-based virtual screening. J Med Chem 2011;54(5):1223-32.
18. Rastelli G, Pacchioni S, Sirawaraporn W, Sirawaraporn R, Parenti MD, Ferrari AM. Docking and database screening reveal new classes of Plasmodium falciparum dihydrofolate reductase inhibitors. J Med Chem 2003;46(14):2834-45.
266 Views | 640 Downloads
How to Cite
Kalita, J. M., S. K. Ghosh, S. Sahu, and M. Dutta. “A STATISTICAL ANALYSIS TO FIND OUT AN APPROPRIATE DOCKING METHOD”. Asian Journal of Pharmaceutical and Clinical Research, Vol. 8, no. 1, Jan. 2015, pp. 158-60, https://innovareacademics.in/journals/index.php/ajpcr/article/view/2750.
Original Article(s)