Main Article Content
Abstract
Computer-Aided Drug Design (CADD), which bridges the fields of biology and technology, is a revolutionary force in the ever-changing field of drug development. The historical development of CADD, its classification into structure-based and ligand-based techniques, and its critical function in streamlining and accelerating drug discovery are all covered in this study. The process of designing and developing new drugs is expensive and time-consuming. These days, computer-aided drug design techniques are typically employed to increase the effectiveness of medication discovery and advancement. Computer-Aided Drug Design (CADD) is a broad field that incorporates both basic and applied analysis in many forms. It is being used in a number of disciplines, such as nanotechnology, molecular biology, and biochemistry. The pharmaceutical business and research frequently use computational tools to increase the efficacy of medication discovery and development.
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References
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References
1. Andronis, C., Sharma, A., Virvilis, V., Deftereos, S., Persidis, A.: Literature mining, ontologies and information visualization for drug repurposing. Briefings in bioinformatics 12(4), 357–368 (2011)
2. Joseph TL, Namasivayam V, Poongavanam V, et al. In silico approaches for drug discovery and development. Front Comput Chem. 3, 74.
3. Murray CW, Rees DC, 2009. The rise of fragment-based drug discovery. Nature Chem. 1(3), 187-92.
4. Fu, C.; Xiang, M.A.; Chen, S.; Dong, G.; Liu, Z.; Chen, C.; Liang, J.; Cao, Y.; Zhang, M.; Liu, Q. Molecular drug simulation and experimental validation of the CD36 receptor competitively binding to Long-Chain fatty acids by 7-Ketocholesteryl-9-carboxynonanoate. ACS Omega 2023, 8, 28277–28289. [Google Scholar] [CrossRef]
5. Nguengang Wakap S. et al., "Estimating cumulative point prevalence of rare diseases," Orphanet Journal of Rare Diseases, 2020.
6. Hotez PJ et al., "The neglected tropical diseases and their devastating health and economic impact on the poorest populations," PLoS Neglected Tropical Diseases, 2007.
7. DiMasi JA et al., "Innovation in the pharmaceutical industry: New estimates of R&D costs," Journal of Health Economics, 2016.
8. Schneider G. "Automating drug discovery," Nature Reviews Drug Discovery, 2018.
9. Moher D et al. “Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement.” PLoS Med. 2009. https://doi.org/10.1371/journal.pmed.1000097
10. Liberati A et al. "The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions." BMJ, 2009.
11. Kitchen DB et al. “Docking and scoring in virtual screening for drug discovery: methods and applications.” Nature Reviews Drug Discovery, 2004.
12. Wallace BC et al. "OpenMEE: A Free Software for Meta-analysis in Ecology and Evolution." Methods in Ecology and Evolution, 2012.
13. Lionta E et al., “Molecular Docking: Principles and Applications,” Current Topics in Medicinal Chemistry, 2014.
14. Lavecchia A, Di Giovanni C. “Virtual screening strategies in drug discovery: a critical review,” Current Medicinal Chemistry, 2013.
15. Morris GM et al., “AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility,” J Comput Chem, 2009
16. Schuster D et al., “Pharmacophore modeling and virtual screening: concepts, software tools, and applications,” Molecular Informatics, 2010.
17. Cherkasov A et al., “QSAR modeling: where have you been? Where are you going to?” Journal of Medicinal Chemistry, 2014.
18. Hollingsworth SA, Dror RO. “Molecular Dynamics Simulation for All,” Neuron, 2018
19. Vamathevan J et al., “Applications of machine learning in drug discovery and development,” Nature Reviews Drug Discovery, 2019.
20. Daina, A., et al. (2019). "SwissDock, a protein-small molecule docking web service based on EADock DSS." Nucleic Acids Res, 40(W1): W270–W277.
21. Ferreira, R. S., et al. (2010). "Molecular docking and virtual screening for cruzain inhibitors." J Chem Inf Model, 50(10): 1857–1869
22. Vamathevan, J., et al. (2019). "Applications of machine learning in drug discovery and development." Nature Reviews Drug Discovery, 18: 463–477.
23. Mah, J. K., et al. (2014). "A systematic review and meta-analysis on the epidemiology of Duchenne and Becker muscular dystrophy." Neuromuscular Disorders, 24(6): 482–491.
24. Fourches D, Muratov E, Tropsha A. "Trust, but verify: On the importance of chemical structure curation in cheminformatics and QSAR modeling research." J Chem Inf Model, 2010.
25. Wirth M et al. "The neglected diseases drug discovery initiative at ChEMBL." Nucleic Acids Res, 2013.
26. Jumper J et al. "Highly accurate protein structure prediction with AlphaFold." Nature, 2021.
27. Moran M et al. "Neglected disease research and development: How much are we really spending?" Policy Cures, 2020.
28. Ekins S et al. "Exploiting machine learning for end-to-end drug discovery and development." Nat Mater, 2019.
29. Lanza G et al. "The road to precision medicine in rare diseases: Interdisciplinary challenges." Trends Mol Med, 2020.
30. Pedrique B et al., “The drug and vaccine landscape for neglected diseases (2000–11): a systematic assessment,” Lancet Global Health, 2013.
31. Hastings J et al., “The ChEMBL bioactivity database: an update,” Nucleic Acids Research, 2015.
32. Zeggini E et al., “Translational genomics and precision medicine: Moving from the lab to the clinic,” Science, 2019.
33. Vamathevan J et al., “Applications of machine learning in drug discovery and development,” Nat Rev Drug Discov, 2019.
34. Tiffin N et al., “Developing bioinformatics capacity in Africa: lessons learned,” Briefings in Bioinformatics, 2014.
35. Moon S et al., “Innovation for neglected diseases: Will a treaty improve R&D?” PLoS Medicine, 2011.