Inflammation Cancer Angiogenesis Biology and Therapeutics | Impact 0.1 (CiteScore) | Online ISSN  2207-872X
REVIEWS   (Open Access)

Advances in Computational Methods for Drug Design: A Revolution in Pharmaceutical Development

Oluwafemi Shittu Bakare 1*, Okoye Oluwabukola 1, Olajuyigbe Olakunle Julius 1

+ Author Affiliations

Journal of Angiotherapy 8(7) 1-7 https://doi.org/10.25163/angiotherapy.879775

Submitted: 16 May 2024  Revised: 08 July 2024  Published: 10 July 2024 

Abstract

Computational methods of drug design involve the use of various software and algorithms to predict the properties of drug molecules, screen for potential targets, and optimize drug candidates for therapeutic efficacy, it lowering the cost of drug research and development time. The discovery and development of a novel medicine is a lengthy, complex, expensive, and high-risk process that has no commercial counterpart. CADD has previously been used to uncover drugs that have gone through clinical trials and become innovative medicines for many ailments. CADD approaches are widely divided into two categories: structure-based drug design (SBDD) and ligand-based drug design (LBDD).  SBDD is used when the three-dimensional structures of target proteins are available, while LBDD design is used in the absence of receptor 3D information and relies on knowledge of molecules that bind to the biological target of interest. Some applications in CADD are Lead Optimization, Virtual screening (VS), ADMET prediction, and toxicity prediction. Medicinal chemists use a variety of computational approaches to modify the chemical structure of a compound to maximize its in vitro activity, drug discovery is driven by the idea that a ligand with higher binding affinity to a target should be more efficacious than that with lower binding affinity to the same target. Target flexibility is one of the key issues that still need to be resolved in drug discovery. The majority of molecular docking tools give the ligand high flexibility, but they fix or give the protein's residues close to or inside the active site only limited flexibility. It is very difficult to provide complete molecular flexibility to the protein as this increases the space and time complexity of the computation. In conclusion, computational methods have revolutionized the field of drug design by enabling faster, more cost-effective, and more efficient drug discovery

Keywords: Computer-Aided Drug Design (CADD), Structure-Based Drug Design (SBDD), Ligand-Based Drug Design (LBDD), Virtual Screening, Molecular Docking

References

Bohnert, T., Prakash, C., (2012). ADME profiling in drug discovery and development: an overview. Encyclopedia of Drug Metabolism and Interactions 1?35. Available from: https://doi.org/10.1002/9780470921920.edm021.

Crasto AM. All About Drugs. (2020) Mumbai, India:[Publisher unknown]; Available from: http://www.allfordrugs.com/drug-design

De Clercq, E. (2009).The design of drugs for HIV and HCV. Nature Reviews Drug Discovery, 8(3), 1-22. https://doi.org/10.1038/nrd2853

de Ruyck J., Brysbaert G., Blossey R., Lensink M.F. (2016) Molecular docking as a popular tool in drug design, an in silico travel. Adv. Appl. Bioinf. Chem. AABC. 9:1–11. doi: 10.2147/AABC.S105289

Doman TN, McGovern SL, Witherbee BJ, Kasten TP, Kurumbail R, Stallings WC, Connolly DT, Shoichet BK. (2002) Molecular docking and high-throughput screening for novel inhibitors of protein tyrosine phosphatase-1B. J Med Chem 45:2213–2221.

Erve J.C., Gauby S., Maynard M.J., Jr., Svensson M.A., Tonn G., Quinn K.P. (2013). Bioactivation of sitaxentan in liver microsomes, hepatocytes, and expressed human P450s with the characterization of the glutathione conjugate by liquid chromatography-tandem mass spectrometry. Chem. Res. Toxicol. 26:926–936.

          doi: 10.1021/tx4001144.

Feher, M., & Schmidt, J. M. (2003). Property distributions: differences between drugs, natural products, and molecules from combinatorial chemistry. Journal of Chemical Information and Computer Sciences, 43(1), 218-227.

 Gao Q, Yang L, Zhu Y. (2010). Pharmacophore Based Drug Design Approach as a Practical Process in Drug Discovery. Curr Comput Aided-Drug Des. 6(1):37–49. DOI: https://doi.org/10.2174/157340910790980151.

Galiè N., Hoeper M.M., Simon J., Gibbs R., Simonneau G. (2011). Liver toxicity of sitaxentan in pulmonary arterial hypertension. Eur. Heart J. 32:386–387.

         doi: 10.1183/09031936.00194810. 

Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., ... & Aspuru-Guzik, A. (2018). Automatic chemical design using a data-driven continuous representation of molecules. ACS Central Science, 4(2), 268-276.

Grant M.A. (2009) Protein structure prediction in structure-based ligand design and virtual screening. Comb. Chem. High Throughput Screen. 12:940–960. doi: 10.2174/138620709789824718.

Guner O. (2005). History and Evolution of the Pharmacophore Concept in Computer-Aided Drug Design. Curr Top Med Chem;2(12):1321–32. DOI:

          https://doi.org/10.2174/1568026023392940.

Hayden, F. G. (2009). Developing new antiviral agents for influenza treatment: what does the future hold? Clinical Infectious Diseases, 48(Supplement_1), S3-S13. https://doi.org/10.1086/591952

Hop, C.E., 2012b. Role of ADME studies in selecting drug candidates: dependence of ADME parameters on physicochemical properties. Encyclopedia of Drug Metabolism and Interactions 6, 1?43. Available from: https://doi.org/10.1002/9780470921920.edm049.

Hwang T.J., Carpenter D., Lauffenburger J.C., Wang B., Franklin J.M., Kesselheim A.S. (2016). Failure of Investigational Drugs in Late-Stage Clinical Development and Publication of Trial Results. JAMA Intern. Med. 176:1826–

         33. doi: 10.1001/jamainternmed. 6008. 

 Janodia M.D., Sreedhar D., Virendra L., Ajay P., Udupa N. (2007). Drug Development Process: A review. Pharm. Rev. 5:2214–2221. [Google Scholar]

Kalyaanamoorthy S., Chen Y.P. (2011) Structure-based drug design to augment hit discovery. Drug Discov. Today. 16:831–839. doi: 10.1016/j.drudis.2011.07.006.

Kitchen D B, Decornez H, Furr J R, Bajorath J. (2004) Nat Rev Drug Discovery, 3:935–949. doi: 10.1038/nrd1549. [PubMed] [CrossRef] [Google Scholar] [Ref list]

Klontz, E. H., Kenney, I. M., & Kirschner, D. E. (2018). Multiscale modeling in the clinic: diseases of the kidney. Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 10(2), e1406.

 Kumar A., Zhang K.Y.J. (2015). Hierarchical virtual screening approaches in small molecule drug discovery. Methods. 71:26–37. doi 10.1016/j.ymeth.2014.07.007. [PMC free article] [PubMed] [CrossRef] [Google Scholar] [Ref list]

 Kumar A., Rathi E., Kini S.G. (2019). Identification of potential tumor-associated carbonic anhydrase isozyme IX inhibitors: Atom-based 3D-QSAR modeling, pharmacophore-based virtual screening, and molecular docking studies. J. Biomol. Struct. Dyn.  doi: 10.1080/07391102.2019.1626285. 

Laurie A.T., Jackson R.M. Q-sitefinder. (2005) An energy-based method for the prediction of protein-ligand binding sites. Bioinformatics. 21:1908–1916.

Langer T, Hoffmann RD. (2006). Pharmacophore Modelling: Applications in Drug Discovery. Expert Opin Drug Discov. 1(3):261–7. DOI:

          https://doi.org/10.1517/17460441.1.3.261.

Leelananda SP, Lindert S. (2016). Computational methods in drug discovery. Beilstein J Org Chem. 12:2694–718. DOI: https://doi.org/10.3762/bjoc.12.267.

Lin, X., Li, X., & Lin, X. (2020). A Review on Applications of Computational Methods in Drug Screening and Design. Molecules (Basel, Switzerland), 25(6), 1375. https://doi.org/10.3390/molecules25061375

Lin, Shu-Kun Sutter, J.M. Hoffman R. HypoGen. (2000) An automated system for generating predictive 3D pharmacophore models. In: Güner O, editor. Pharmacophore Perception, Development and Use in Drug Design. International University Line; p. 171–89.

 Lionta E., Spyrou G., Vassilatis D.K., Cournia Z. (2014) Structure-based virtual screening for drug discovery: Principles, applications, and recent advances. Curr. Top. Med. Chem. 14:1923–1938. doi: 10.2174/1568026614666140929124445.                       

Maria Antony Dhivyan JE and Anoop MN. (2012). School of Health and Life Sciences, Edinburgh Napier University, Edinburgh, United Kingdom – EH10 5DT.

McArdle, S., Endo, S., Aspuru-Guzik, A., Benjamin, S. C., & Yuan, X. (2020). Quantum computational chemistry. Reviews of Modern Physics, 92(1), 015003.

Md. Mofizur Rahman1*, Md. Rezaul Karim2, Md. Qamrul Ahsan3, Abul Bashar Ripon Khalipha1, Mohammed Raihan Chowdhury4 and Md Saifuzzaman. (2012).

Ms. Priti B. Savant,Ms. Ashwini R. Pawar, Ms. Kaufiya D. Sayyed, Ms. Pooja R. Yelmar. (2022) 1,2,3,4 Sahyadri College of Pharmacy Methwade Tal, Sangola, Distsolapur  Maharashtra 413307.

Noha SM, Schuster D. (2013). Pharmacophore modeling. In: Lill MA, editor. In Silico Drug Discovery and Design. p. 80–93. ISBN: 9781909453029.

 Pau L.; Gardner, C.L.; Pugliai, F.A. (2017) Gonzalez, teleonomic acid binding pocket in prb from liberibacterasiaticus. Front microbial 8,1591.

Prachayasittikul V, Worachartcheewan A, Shoombuatong W, Songtawee N, Simeon S, Prachayasittikul V, et al. (2015). Computer-Aided Drug Design of Bioactive Natural Products. Curr Top Med Chem. 15(18):1780–800. URL:

          https://www.ingentaconnect.com/content/ben/ctmc /2015/00000015/00000018/art00004.

Sanders MPA, McGuire R, Roumen L, De Esch IJP, De Vlieg J, Klomp JPG, et al. (2012). From the protein’s perspective: The benefits and challenges of protein structure-based pharmacophore modeling. Medchemcomm

         3(1):28–38. DOI: https://doi.org/10.1039/C1MD00210D

Scior T., Bender A., Tresadern G., Medina-Franco J.L., Martínez-Mayorga K., Langer T., Cuanalo-Contreras K., Agrafiotis D.K. (2012). Recognizing pitfalls in virtual screening: A critical review. J. Chem. Inf. Model.52:867–881.

          doi: 10.1021/ci200528d. [PubMed] [CrossRef] [Google Scholar] [Ref list]

Schuster D. (2010). 3D pharmacophores as tools for activity profiling. Drug Discov Today Technol. 7(4):e205–11. DOI: https://doi.org/10.1016/j.ddtec.2010.11.006

 Sheridan RP, Rusinko A, Nilakantan R, Venkataraghavan R. (1989). Searching for pharmacophores in large coordinate databases and their use in drug design. Proc Natl Acad Sci U S A. 86(20):8165–9. DOI: https://doi.org/10.1073/pnas.86.20.8165.

Sharma, A., & Almasi, Z. (2019). Kinase inhibitors in cancer treatment: an overview. Mini-Reviews in Medicinal Chemistry, 19(12), 986-1002.

          https://doi.org/10.2174/1389557519666190911153827

Sliwoski, G., Kothiwale, S., Meiler, J., & Lowe, E. W., Jr (2013). Computational methods in drug discovery. Pharmacological reviews, 66(1), 334–395.

          https://doi.org/10.1124/pr.112.007336

 Talele T T, Khedkar S A, Rigby A C. (2010) Curr Top Med Chem. 10:127–141. doi 10.2174/156802610790232251. [PubMed] [CrossRef] [Google Scholar] [Ref list]

Ting N., editor. (2006). Introduction and New Drug Development Process. Dose Finding in Drug Development. Springer; New York, NY, USA; pp. 1–17. [Google Scholar]

Van de Waterbeemd, H.,Gifford, E., (2000). ADMET in silico modeling: toward prediction paradise? Nat. Rev. Drug Discov. 2(3), 192-204.

Vel EP, Guti PA. (2012). Generation of pharmacophores and classification of drugs using protein-ligand complexes Generación de farmacóforos y clasificación de drogas utilizando complejos proteína-ligando Geração de farmacóforos e classificação de fármacos usando-se complexo prote. Rev Colomb Química. 41(3):337–48. URL: http://www.scielo.org.co/scielo.php?pid=S012028042012000300001&script=sci_arttext&tlng=en.

Vijayakrishnan R. (2009) Structure-based drug design and modern medicine. J Postgrad Med 55:301–304 [PubMed] [Google Scholar] [Ref list]

 Vucicevic J., Nikolic K., Mitchell J.B. (2019) Rational drug design of antineoplastic agents using 3D-QSAR, cheminformatic, and virtual screening approaches. Curr. Med. Chem. 26:3874–3889. doi 10.2174/0929867324666170712115411. [PubMed] [CrossRef] [Google Scholar] [Ref list]

Zhang Y.; hand.; Tian H.; Jiao Y.; Shi Z.; Ran T.; Liu H.; Lu S.; Xu A.; Qiao X.; Pau J.; Yin L.; Zhou W.; Lu T.; Chen Y. (2016) Identification of covalent binding sites targeting cytokines based on computational approaches Mol. Pharma, 13(9) 3106-3118.

Zhang, D., Luo, G., Ding, X., Lu, C., (2012). Preclinical experimental models of drug metabolism and disposition in drug discovery and development. Acta Pharm. Sin. B 2 (6), 549? 561.

Zhuang, X., Lu, C., (2016). PBPK modeling and simulation in drug research and development. Acta Pharm. Sin. B 6 (5), 430?440.

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