Rakhi Mishra, Prem Shankar Mishra, Rupa Mazumder, Avijit Mazumder, Anurag Chaudhary
Rakhi Mishra1*, Prem Shankar Mishra2, Rupa Mazumder1, Avijit Mazumder1, Anurag Chaudhary3
1Noida Institute of Engineering and Technology (Pharmacy Institute), Greater Noida-201306, Uttar Pradesh, India.
2Department of Pharmacy, Galgotias University, Greater Noida-201306, Uttar Pradesh, India.
3Department of Pharmaceutical Technology, Meerut Institute of Engineering and Technology, Meerut-250005, Uttar Pradesh, India.
Volume - 14,
Issue - 10,
Year - 2021
Computational and experimental techniques are two complimentary approaches that have important roles in drug discovery and development. Earlier time and cost of bringing a new drug in market bears a question as it takes seven to twelve years and $ 1.2 billion are often cited. Furthermore, five out of forty thousand compounds tested in animals reach human testing and only one of five compounds reaching clinical studies is approved. This accounts for a large input in terms of time, money and human and other resources. Therefore, new approaches are needed to facilitate, expedite and streamline drug discovery and development, save time, money and resources. Among many computational tools, molecular docking is one of the important means that can be used in drug discovery. It provides the information regarding the binding affinities between small molecules (ligands) and macromolecular receptor targets (proteins). Various approaches, methodology are cited in various literatures for describing the cost, time effect with success of drug discovery task. In this review, introduction of the available molecular docking methods, with simple methodology of docking and examples of drug design and discovery through computational docking methods is discussed and emphasis is made on various examples of sampling algorithms, scoring functions with their relevant characterstics with summary on type of ligand binding with receptors.
Cite this article:
Rakhi Mishra, Prem Shankar Mishra, Rupa Mazumder, Avijit Mazumder, Anurag Chaudhary. Computational Docking Technique for Drug Discovery: A Review. Research Journal of Pharmacy and Technology 2021; 14(10):5558-2. doi: 10.52711/0974-360X.2021.00968
Rakhi Mishra, Prem Shankar Mishra, Rupa Mazumder, Avijit Mazumder, Anurag Chaudhary. Computational Docking Technique for Drug Discovery: A Review. Research Journal of Pharmacy and Technology 2021; 14(10):5558-2. doi: 10.52711/0974-360X.2021.00968 Available on: https://rjptonline.org/AbstractView.aspx?PID=2021-14-10-86
1. Jorgensen, W.L. The many roles of computation in drug discovery. Science. 2004, 303(5665), 1813–1818.[PubMed]
2. Bajorath, J. Integration of virtual and high-throughput screening. Nat Rev Drug Discov. 2002, 1(11), 882–894. [PubMed]
3. Walters, W.P.; Stahl, M.T.; Murcko, M.A. Virtual screening - an overview. Drug Discov. Today. 1998, 3, 160–178.
4. Langer, T.; Hoffmann, R.D. Virtual screening: an effective tool for lead structure discovery. Curr Pharm Des. 2001, 7(7), 509–527. [PubMed]
5. Kitchen, D.B.; Decornez, H.; Furr, J.R.; Bajorath, J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov. 2004, 3(11), 935–949. [PubMed]
6. McConkey, B.J.; Sobolev, V.; Edelma, M. The performance of current methods in ligand-protein docking. Current Science. 2002,83, 845–855.
7. Sandeep Reddy CH.; Sree Kumar Reddy G.; Manoj Kumar Mahto.; Pavan Kunala.; Chaitanya Kanth R. Insilico Design and Discovery of Some Novel Ache Inhibitors for Treatment of Alzheimer’s Disorder. Research J. Pharm. and Tech. 2012, 5(3), 424-427.
8. Koshland, D.E. Correlation of Structure and Function in Enzyme Action. Science. 1963, 142, 1533–1541. [PubMed]
9. M. Sravani, P.; Sai Lakshmi, K.; Amuktha Reddy.; Naga Deepthi N.; Uday Sasi Kiran Kantheti. Insilico Analysis and Docking of Tacrine and Donepezil Derivatives Targeting Histamine-N-Methyltransferase and Acetyl Cholinesterase Protein Respectively for Alzheimer's Disease. Research J. Pharm. and Tech. 2013, 6(1), 86-89
10. Fischer, E. Einfluss der configuration auf die wirkung derenzyme. Ber. Dt. Chem. Ges. 1894, 27, 2985–2993.
11. Anuradha V.; Praveena A.; Habeeb S. K. M.; Madan. Identification of Drug Targets through Mutational Analysis of Drug Resistance Genes in Candida albicans. Research J. Pharm. and Tech. 2013, 6(3), 267-277.
12. Kuntz, I.D.; Leach, A.R.; Conformational analysis of flexible ligands in macromolecular receptor sites. J. Comput. Chem. 1992, 13, 730–748.
13. A. Ranganadha Reddy.; T.C. Venkateswarulu, D.; John Babu, N.; Shyamala Devi. Homology Modeling, Simulation and Docking Studies of Tau-Protein Kinase. Research J. Pharm. and Tech. 2014, 7(3), 376-388.
14. Clark, K.P. Flexible ligand docking without parameter adjustment across four ligand- receptor complexes. J Comput Chem. 1995, 16, 1210–1226.
15. Taylor, J.S.; Burnet, R.M. DARWIN: a program for docking flexible molecules. Proteins. 2000, 41(2), 173–191. [PubMed]
16. A. Anto Arockia Raj.; Vinnarasi, J.; Venkataraman, R. Synthesis, Characterization and Biological Activity of Mixed Ligand Complexes of Isatin. Research J. Pharm. and Tech. 2013, 6(10), 1121-1123.
17. Susmi M S.; Revathy S Kumar.; Sreelakshmi V.; Sruthy V Menon.; Surya Mohan.; Saranya Tulasidharan Suja.; Sathianarayanan, Asha Asokan Manakadan. A Computational approach for identification of Phytochemicals for targeting and optimizing the inhibitors of Heat shock proteins. Research J. Pharm. and Tech. 2015, 8(9), 1199-1204.
18. Carlson, H.A.; Jorgensen, W.L. An extended linear response method for determining free energies of hydration. J Phys Chem. 1995, 99, 10667–10673.
19. Girija, K.; Jamuna, B. Design and Synthesis of Some Novel Schiff’s Base Aryl Imidazole Derivatives, Characterization, Docking and Study of their Anti-Microbial Activity. Research J. Pharm. and Tech. 2015, 8(4), 407-415.
20. Shoichet, B.K.; Stroud, R.M.; Santi, D.V.; Kuntz, I.D.; Perry, K.M. Structure-based discovery of inhibitors of thymidylate synthase. Science. 1993, 259(5100), 1445–1450. [PubMed]
21. Head, R.D.; Smythe, M.L.; Oprea, T.I.; Waller, C.L.; Green, S.M.; Marshall, G.R. VALIDATE: A New Method for the Receptor-Based Prediction of Binding Affinities of Novel Ligands. J. Am. Chem. Soc. 1996, 118, 3959–3969.
22. Muegge, I.; Martin, Y.C. A general and fast scoring function for protein-ligand interactions: a simplified potential approach. J Med Chem. 1999, 42(5), 791–804. [PubMed]
23. Srinivasan, N.; Lakshmi, C. Stock Price Prediction using Rule Based Genetic Algorithm Approach . Research J. Pharm. and Tech. 2017, 10(1), 87-90.
24. Kaur N.; Monika.; Singh, K. 3D-QSAR and Molecular Docking Studies of N-(2-Aminophenyl)-Benzamide Derivatives as Inhibitors of HDAC2. Research J. Pharm. and Tech. 2014, 7(7), 760-770.
25. Feher, M. Consensus scoring for protein-ligand interactions. Drug Discov Today. 2006, 11(9-10), 421–428. [PubMed]
26. Bron, C.; Kerbosch, J. Algorithm 457: Finding All Cliques of an Undirected Graph. Communications of the ACM. 1973, 16(9), 575–576.
27. Meng, E.C.; Shoichet, B.K.; Kuntz, I.D. Automated docking with grid-based energy evaluation. J. Comput. Chem. 1992, 13, 505–524.
28. Totrov, M.; Abagyan, R. Protein-ligand docking as an energy optimization problem. In: Raffa RB, editor. Drug-receptor thermodynamics: Introduction and experimental applications. John Wiley and Sons; New York, 2001. pp. 603–624.
29. Go, N.; Scheraga, H.A. Ring Closure and Local Conformational DeformationsofChain Molecules.Macromolecules.1970,3(2), 178–187.
30. Freymann, D.M.; Wenck, M.A.; Engel, J.C.; Feng, J.; Focia, P.J.; Eakin, A.E.; Craig, S.P. Efficient identification of inhibitors targeting the closed active site conformation of the HPRT from Trypanosoma cruzi. Chem Biol. 2000, 7(12), 957–968. [PubMed]
31. Venkatesh Kamath, Aravinda Pai. Application of Molecular Descriptors in Modern Computational Drug Design –An Overview. Research J. Pharm. and Tech. 2017, 10(9), 3237-3241.
32. Su, A.I.; Lorber, D.M.; Weston, G.S.; Baase, W.A.; Matthews, B.W.; Shoichet, B.K. Docking molecules by families to increase the diversity of hits in database screens: computational strategy and experimental evaluation. Proteins. 2001, 42(2), 279–293. [PubMed]
33. Gschwend, D.A.; Kuntz, I.D. Orientational sampling and rigid-body minimization in molecular docking revisited: on-the-fly optimization and degeneracy removal. J Comput Aided Mol Des. 1996, 10(2), 123–132.[PubMed]