Volume : 11, Issue : 02, February – 2024

Title:

A BRIEF REVIEW ON COMPUTATIONAL TECHNIQUES USED IN MODERN DRUG DISCOVERY

Authors :

Swathi Naraparaju, Soujanya Chaganti, Barla Karuna Devi*, Regu Sandhya Rani , Guduru Madhukar Reddy , Guram Gadda Srikanth , Firaz Muzzafer Ali Mohammad

Abstract :

Use of computational techniques has accelerated the discovery process. Traditional drug discovery process was laborious, time consuming and was expensive. The success rate was also very low. In the modern drug discovery process the use of computational techniques has made the drug discovery fast and cost effective with higher success rates. The present review highlights the various computational techniques used in the drug discovery, their advantages and limitations.

Cite This Article:

Please cite this article in press Barla Karuna Devi et al., A Brief Review On Computational Techniques Used In Modern Drug Discovery, Indo Am. J. P. Sci, 2024; 11 (02).

Number of Downloads : 10

References:

1. Science, C., & Engineering, S. (2013). Insilico Methods in Drug Discovery – A Review, 3(5), 680–683.
2. Mandal, S., & Mandal, S. K. (2009). European Journal of Pharmacology, 625(1-3), 90–100.
3. Bharath, E. N., Manjula, S. N., & Vijaychand, A. (2011). In silico drug design – tool for overcoming the innovation deficit in the drug discovery process, 3(2), 6–10.
4. www.in-silico-methods.eu
5. Pugazhendhi et al. Insilico Methods in Drug Discovery – A Review. Int J Adv Res Comp Sci Software Engg. 2013;3(5):680-683.
6. Science, C., & Engineering, S. (2013). Insilico Methods in Drug Discovery – A Review, 3(5), 680–683.
7. Introduction to QSAR”, Introduction to the Principles of Drug Design, By H. J. Smith, Hywel Williams, page no. 213-241.
8. Suh M., Park S., Jee H. (2002). Comparison of QSAR Methods (CoMFA, CoMSIA, HQSAR) of Anticancer 1-N-Substituted Imidazoquinoline-4,9-dione Derivatives. Bull. Korean Chem. Soc. 23, 417-422.
9. Wold S., Ruhe A., Wold H., Dunn WJ., (1984). The co-linearity problem in linear regression. The partial least squares approach to generalized inverse. SIAM. J. Sci. Stat. Comput. 5, 735-743.
10. Malinowski ER., Howery DG. Factor Analysis in Chemistry. (1988).
11. Science, C., & Engineering, S. (2013). Insilico Methods in Drug Discovery – A Review, 3(5), 680–683.
12. Discovery, D. (2002). Chemoinformatics and Drug Discovery, 566–600
13. G. Wolber, “Structure-Based 3D Pharmacophores : An Alternative to Docking ? Abstract & Outline.”
14. Morris GM, Lim-Wilby M. Molecular docking. InMolecular modeling of proteins Humana Press, 2008; 365-382.
15. Fan J, Fu A, Zhang L. Progress in molecular docking. Quantitative Biology, 2019;7(2): 83-9.
16. Sanschagrin, P. (2010). An Introduction to Molecular Docking What is Docking ?
17. Journal of Bioinformatics and Sequence Analysis Vol. 2(5), pp. 89-94, June 2011 Available online at http://www.academicjournals.org/JBSA ISSN 2141-2464 ©2011 Academic Journals
18. Oprea TI (Ed.). Chemoinformatics in drug discovery (Vol. 23). Weinheim: Wiley-VCH. 2005.
19. Kaushik M. A review of Innovative Chemical Drawing and Spectra Prediction Computer Software. Mediterranean Journal of Chemistry. 2014;3(1):759-766.
20. Begam BF, Kumar JS. A study on cheminformatics and its applications on modern drug discovery. Procedia Engineering. 2012;38: 1264- 1275.
21. Park J, Rosania GR, Shedden KA, Nguyen M, Lyu N, Saitou K. Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal. 2009;3(1):4.
22. Tetko IV, Gasteiger J, Todeschini R, Mauri A, Livingstone D, Ertl P, et al. Virtual computational chemistry laboratory–design and description. Journal of computer-aided molecular design. 2005;19(6):453-463.
23. Zhu Q, Lajiness MS, Ding Y, Wild D J. WENDI: a tool for finding non-obvious relationships between compounds and biological properties, genes, diseases and scholarly publications. Journal of cheminformatics. 2010;2(1):6.
24. Girke T, Cheng LC, Raikhel N. ChemMine. A compound mining database for chemical genomics. Plant physiology. 2005;138(2):573-577.
25. Phadungsukanan W, Kraft M, Townsend JA, Murray-Rust P. The semantics of Chemical Markup Language (CML) for computational chemistry: Comp Chem. Journal of cheminformatics. 2012;4(1):15.
26. Xie XQS. Exploiting PubChem for virtual screening. Expert opinion on drug discovery. 2010;5(12):1205-1220.
27. Fjell CD, Jenssen H, Hilpert K, Cheung WA, Pante N, Hancock RE, et al. Identification of novel antibacterial peptides by chemoinformatics and machine learning. Journal of medicinal chemistry. 2009;52(7):2006- 2015.
28. O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. Open Babel: An open chemical toolbox. Journal of cheminformatics. 2011; 3(1):33.
29. Yusuf M. Insights into the in-silico research: Current scenario, advantages, limits, and future perspectives. Life in Silico. 2023 Jul 10;1(1):13-25.