Perspectives on Computer Aided Drug Discovery

Kevser Kübra Kırboğa; Ecir Küçüksille

doi:10.55007/dufed.1103457

Derleme

Bilgisayar Destekli İlaç Keşfi Üzerine Bakışlar

Yıl 2022, Cilt: 11 Sayı: 2, 405 - 426, 30.12.2022

Kevser Kübra Kırboğa Ecir Küçüksille

https://doi.org/10.55007/dufed.1103457

Öz

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.

Anahtar Kelimeler

Bilgisayar destekli ilaç tasarımı, Yapı-aktivite İlişkisi, Keminformatik, Moleküler modelleme, Sanal tarama

Kaynakça

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Perspectives on Computer Aided Drug Discovery

Yıl 2022, Cilt: 11 Sayı: 2, 405 - 426, 30.12.2022

Kevser Kübra Kırboğa Ecir Küçüksille

https://doi.org/10.55007/dufed.1103457

Öz

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.

Anahtar Kelimeler

Computer-aided drug design, Structure-activity relationship, Cheminformatics, Molecular modeling, Virtual screening

Kaynakça

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.
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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.
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.
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.
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.
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.2017.10.005.
Z. Omran and C. Rauch, "Acid-mediated Lipinski's second rule: application to drug design and targeting in cancer," (in eng), Eur Biophys J., vol. 43, no. 4-5, pp. 199-206, May 2014, doi: 10.1007/s00249-014-0953-1.
C. M. Chagas, S. Moss, and L. Alisaraie, "Drug metabolites and their effects on the development of adverse reactions: Revisiting Lipinski's Rule of Five," International Journal of Pharmaceutics, vol. 549, no. 1-2, pp. 133-149, 2018-10-01, doi: 10.1016/j.ijpharm.2018.07.046.
C. A. Lipinski, "Lead- and drug-like compounds: the rule-of-five revolution," Drug Discovery Today: Technologies, vol. 1, no. 4, pp. 337-341, 2004-12-01, doi: 10.1016/j.ddtec.2004.11.007.
M. Congreve, R. Carr, C. Murray, and H. Jhoti, "A 'rule of three' for fragment-based lead discovery?," (in eng), Drug Discov Today, vol. 8, no. 19, pp. 876-7, Oct-1-2003, doi: 10.1016/s1359-6446(03)02831-9.
H. Jhoti, G. Williams, D. C. Rees, and C. W. Murray, "The 'rule of three' for fragment-based drug discovery: where are we now?," Nature Reviews Drug Discovery, vol. 12, no. 8, pp. 644-644, 2013-08-01, doi: 10.1038/nrd3926-c1.
J. Bajorath, "Computer-aided drug discovery," F1000Research, vol. 4, pp. 630, 2015, doi: 10.12688/f1000research.6653.1.
L. Shi and N. Zhang, "Applications of Solution NMR in Drug Discovery," Molecules, vol. 26, no. 3, pp. 576, 2021-01-22, doi: 10.3390/molecules26030576.
D. R. Knighton, C. C. Kan, E. Howland, C. A. Janson, Z. Hostomska et al., "Structure of and kinetic channelling in bifunctional dihydrofolate reductase–thymidylate synthase," Nature Structural Biology, vol. 1, no. 3, pp. 186-194, 1994/03/01, doi: 10.1038/nsb0394-186.
W. Lu, R. Zhang, H. Jiang, H. Zhang, and C. Luo, "Computer-Aided Drug Design in Epigenetics," (in English), Frontiers in Chemistry, vol. 6, 2018-March-12, doi: 10.3389/fchem.2018.00057.
L. A. Caliguiri, J. J. McSharry, and G. W. Lawrence, "Effect of arildone on modifications of poliovirus in vitro," Virology, vol. 105, no. 1, pp. 86-93, 1980/08/01, doi: https://doi.org/10.1016/0042-6822(80)90158-0.
C. Mattos, B. Rasmussen, X. Ding, G. A. Petsko, and D. Ringe, "Analogous inhibitors of elastase do not always bind analogously," (in eng), Nat Struct Biol, vol. 1, no. 1, pp. 55-8, Jan 1994, doi: 10.1038/nsb0194-55.
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Toplam 64 adet kaynakça vardır.

Ayrıntılar

Birincil Dil	İngilizce
Konular	Yazılım Mühendisliği (Diğer)
Bölüm	Derleme
Yazarlar	Kevser Kübra Kırboğa 0000-0002-2917-8860 Ecir Küçüksille 0000-0002-3293-9878
Erken Görünüm Tarihi	1 Ekim 2022
Yayımlanma Tarihi	30 Aralık 2022
Gönderilme Tarihi	14 Nisan 2022
Yayımlandığı Sayı	Yıl 2022 Cilt: 11 Sayı: 2

Kaynak Göster

IEEE	K. K. Kırboğa ve E. Küçüksille, “Perspectives on Computer Aided Drug Discovery”, DÜFED, c. 11, sy. 2, ss. 405–426, 2022, doi: 10.55007/dufed.1103457.

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