Journals / Dicle Üniversitesi Fen Bilimleri Enstitüsü Dergisi / 2022 / Cilt: 11 - Sayı: 2

Perspectives on Computer Aided Drug Discovery

Bilgisayar Destekli İlaç Keşfi Üzerine Bakışlar

Pages
405–426
Publication Date
2022-12-30
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Finally, this review article provides an overview of the details, purposes, uses in developing drugs, general workflows, tools used, limitations, and future of CADD methods, including the SBDD and LBDD processes that have become an integral part of pharmaceutical companies and academic research.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The drug development and discovery process are challenging, take 15 to 20 years, and require approximately 1.5-2 billion dollars, from the critical selection of the target molecule to post-clinical market application.
📊
Key Findings & Data:Several computational drug design methods identify and optimize target biologically lead compounds.
💡
Conclusion & Impact:Finally, this review article provides an overview of the details, purposes, uses in developing drugs, general workflows, tools used, limitations, and future of CADD methods, including the SBDD and LBDD processes that have become an integral part of pharmaceutical companies and academic research.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Bilgisayar destekli ilaç tasarımı · Yapı-aktivite İlişkisi · Keminformatik

Abstract

The drug development and discovery process are challenging, take 15 to 20 years, and require approximately 1.5-2 billion dollars, from the critical selection of the target molecule to post-clinical market application. Several computational drug design methods identify and optimize target biologically lead compounds. Given the complexity and cost of the drug discovery process in recent years, computer-assisted drug discovery (CADD) has spread over a broad spectrum. CADD methods support the discovery of target molecules, optimization of small target molecules, analysis, and development processes faster and less costly. These methods can be classified into structure-based (SBDD) and ligand-based (LBDD). SBDD begins the development process by focusing on the knowledge of the three-dimensional structure of the biological target. Finally, this review article provides an overview of the details, purposes, uses in developing drugs, general workflows, tools used, limitations, and future of CADD methods, including the SBDD and LBDD processes that have become an integral part of pharmaceutical companies and academic research.

Özet

İlaç geliştirme ve keşif süreci, hedef molekülün kritik seçiminden klinik sonrası pazar uygulamasına kadar 15 ila 20 yıl süren ve yaklaşık 1,5-2 milyar dolar gerektiren zorlu bir süreçtir. Bu süreçte, biyolojik aktiviteye sahip hedef öncü bileşikleri belirlemek ve optimize etmek için bir dizi hesaplamalı ilaç tasarım yöntemi kullanılır. Son yıllarda ilaç keşif sürecinin karmaşıklığı ve maliyeti göz önüne alındığında, bilgisayar destekli ilaç keşfi (CADD) geniş bir yelpazeye yayılmıştır. Bu gözden geçirme makalesi, ilaç şirketlerinin ve akademik araştırmaların ayrılmaz bir parçası haline gelen SBDD ve LBDD süreçleri de dahil olmak üzere CADD yöntemlerinin ayrıntılarına, amaçlarına, ilaç keşfindeki kullanımlarına, genel iş akışlarına, kullanılan araçlara, sınırlamalara ve geleceğine ilişkin bir genel bakış sunmaktadır.

🔖

Cite This Article

KIRBOĞA, K. K., KÜÇÜKSİLLE, E. (2022). Perspectives on Computer Aided Drug Discovery. Dicle Üniversitesi Fen Bilimleri Enstitüsü Dergisi, Cilt: 11 - Sayı: 2, 405–426. https://ajindex.com/articles/858698

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (6)

  1. A. S. Rifaioglu, H. Atas, M. J. Martin, R. Cetin-Atalay, V. Atalay, and T. Doğan, "Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases," Briefings in Bioinformatics, vol. 20, no. 5, pp. 1878-1912, 2018, doi: 10.1093/bib/bby061. B. S. Robinson, K. A. Riccardi, Y. F. Gong, Q. Guo, D. A. Stock et al., "BMS-232632, a highly potent human immunodeficiency virus protease inhibitor that can be used in combination with other available antiretroviral agents," Antimicrobial agents and chemotherapy, vol. 44, no. 8, pp. 2093-2099, 2000.
  2. Z. Chen, Y. Li, E. Chen, D. L. Hall, P. L. Darke, C. Culberson, J. A. Shafer, L. C. Kuo, "Crystal structure at 1.9-A resolution of human immunodeficiency virus (HIV) II protease complexed with L-735,524, an orally bioavailable inhibitor of the HIV proteases," Journal of Biological Chemistry, vol. 269, no. 42, pp. 26344-26348, 1994.
  3. D. J. Kempf, K. C. Marsh, J. F. Denissen, E. McDonald, S. Vasavanonda, C. A. Flentge, B. E. Green, L. Fino, C. H. Park, X. P. Kong "ABT-538 is a potent inhibitor of human immunodeficiency virus protease and has high oral bioavailability in humans," Proceedings of the National Academy of Sciences, vol. 92, no. 7, pp. 2484-2488, 1995.
  4. A. Krohn, S. Redshaw, J. C. Ritchie, B. J. Graves, and M. H. Hatada, "Novel binding mode of highly potent HIV-proteinase inhibitors incorporating the (R)-hydroxyethylamine isostere," Journal of medicinal chemistry, vol. 34, no. 11, pp. 3340-3342, 1991. A. C. Anderson, "The process of structure-based drug design," Chemistry & biology, vol. 10, no. 9, pp. 787-797, 2003. E. E. Rutenber and R. M. Stroud, "Binding of the anticancer drug ZD1694 to E. coli thymidylate synthase: assessing specificity and affinity," Structure, vol. 4, no. 11, pp. 1317-1324, 1996.
  5. J. Vamathevan, D. Clark, P. Czodrowski, I. Dunham, E. Ferran et al., "Applications of machine learning in drug discovery and development," Nature reviews Drug discovery, vol. 18, no. 6, pp. 463-477, 2019.
  6. O. J. Wouters, M. Mckee, and J. Luyten, "Estimated Research and Development Investment Needed to Bring a New Medicine to Market, 2009-2018," JAMA, vol. 323, no. 9, pp. 844, 2020-03-03, doi: 10.1001/jama.2020.1166. R. C. Mohs and N. H. Greig, "Drug discovery and development: Role of basic biological research," Alzheimer's & Dementia: Translational Research & Clinical Interventions, vol. 3, no. 4, pp. 651-657, 2017/11/01, doi: https://doi.org/10.1016/j.trci.2017.10