A Review On Computer-Aided Drug Design And Discovery

Main Article Content

Sushant M. Ahire
Shivraj P. Jadhav
Vijay V. Shewale
Prerana S. Pawar
Aditi M. Kokande
Dhanashree P. Sonawane
Dhananjay M Patil

Abstract

A new drug's discovery and development are typically thought of as an extremely difficult process that requires a lot of time and resources. Therefore, to improve the effectiveness of the drug discovery and development process, computer-aided drug design methodologies are currently used extensively. Structure-based drug design and ligand-based drug design approaches are known as particularly effective and powerful techniques in the field of drug discovery and development, among other Computer aided drug design approaches that are considered promising techniques based on their necessity. These two approaches can be used in conjunction with molecular docking for lead identification and optimization in virtual screening. In recent years, the pharmaceutical industry and several academic fields have increasingly embraced computational methods to increase the efficiency and effectiveness of drug development. By the way of CADD we minimize the risks as well as save time and money and the CADD is more economical than others. This process is most valuable for future prospects.

Article Details

How to Cite
Sushant M. Ahire, Shivraj P. Jadhav, Vijay V. Shewale, Prerana S. Pawar, Aditi M. Kokande, Dhanashree P. Sonawane, & Dhananjay M Patil. (2023). A Review On Computer-Aided Drug Design And Discovery. Journal for ReAttach Therapy and Developmental Diversities, 6(10s(2), 1573–1582. https://doi.org/10.53555/jrtdd.v6i10s(2).2169
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Articles
Author Biographies

Sushant M. Ahire

Divine College of Pharmacy, Satana, Maharashtra, India. 423301

Shivraj P. Jadhav

Divine College of Pharmacy, Satana, Maharashtra, India. 423301

Vijay V. Shewale

Divine College of Pharmacy, Satana, Maharashtra, India. 423301

Prerana S. Pawar

Divine College of Pharmacy, Satana, Maharashtra, India. 423301

Aditi M. Kokande

Divine College of Pharmacy, Satana, Maharashtra, India. 423301

Dhanashree P. Sonawane

Divine College of Pharmacy, Satana, Maharashtra, India. 423301

Dhananjay M Patil

Divine College of Pharmacy, Satana, Maharashtra, India. 423301

References

Whittaker the role of bioinformatics in target validation. Drug Discovery To-Clinical trial registration: a statement from the International Committee of Medical Journal Editors. Medical Journal of Australia, 2004; 181: 293-4.

Lengauer. Bioinformatics. From Genomes to Drugs. Wiley- VCH, Weinheim, Germany, 2002.

Lipinski Lead and drug-like compounds: the rule-of-five revolution. Drug Discovery Today: Technologies, 2004; 1(4): 337-341.

Leeson P D, Davis A M, Steele J. Drug-like properties: Guiding principles for design – or chemical prejudice? Drug Discovery Today: Technologies, 2004; 1(3): 189-195.

Hou T. Xu X. Recent Development and Application of Virtual Screening in Drug Discovery: An Overview. Current Pharmaceutical Design, 2004; 10: 1011-1033.

Klebbe G. Lead Identification in Post-Genomics: Computers as a Complementary Alternative. Drug Discovery Today: Technologies, 2004; 1(3): 225-215.

Gisbert Schneider. Uli Fechner. Computer-based de novo design of drug-like molecules. Nature. Reviews. Drug Discovery, 2005; 4(8): 649-663.

Butte A. The use and analysis of microarray data. Nature Reviews Drug Discovery, 1(12): 951-960.

Richards W. G. Computer-Aided Drug Design. Pure and Applied Chemistry, 1994; 6(68): 1589-1596.

Song C.M., Lim S.J., Tong J.C. Recent advances in computer-aided drug design. Brief. Bioinform. 2009;10(5):579–591.

R.A. Hodo, B.A. Kidd, K. Shameer, B.P. Reudhead, J.T. Dudley in silico method for drug repurposing and pharmacology computational approaches to drug repurposing and pharmacology WIRREs Syst. Biol. Med. 186 (2016)

M.J. Wasko, K.A. Pellegrene, J.D. Madura, D.K. Surratt a role for fragment-based drug design in developing novel lead compounds for central nervous system target. Front. Neurol.;6(2015), pp- 1-11

Ms. Priti B. Savant,Ms. Ashwini R. Pawar, Ms. Kaufiya D. Sayyed , Ms. Pooja R. Yelmar

13.Imam SS, Gilani SJ. Computer Aided Drug Design: A Novel Loom To Drug Discovery. Org. Med. Chem. 2017; 1(4):1-6.

Ferreira LG, dos Santos RN, Oliva G, Andricopulo AD. Molecular docking and structure-based drug design strategies. Molecules. 2015

Anderson AC. The process of structure-based drug design. Chemistry & biology. 2003

Grant M.A. Protein structure prediction in structure-based ligand design and virtual screening. Comb. Chem. High Throughput Screen. 2009;12:940–960.

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.; identification of covalent binding sites targeting cryteines based on computational approaches Mol. Pharma,2016,13(9) 3106-3118.

Pau L.; Gardner, C.L.; Pugliai, F.A.; honzalez, teleonomic acid binding pocket in prb from liberibacter asiaticus. Front microbiol.,2017,8,1591.

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

Wunberg T., Hendrix M., Hillisch A., Lobell M., Meier H., Schmeck C., Wild H., Hinzen B. Improving the hit-to-lead process: Data-driven assessment of drug-like and lead-like screening hits. Drug Discov. Today. 2006;11:175–180.

Prada-Gracia D., Huerta-Yepez S., Moreno-Vargas L.M. Application of computational methods for anticancer drug discovery, design, and optimization. Bol. Med. Hosp. Infan.t Mex. 2016;73:411–423.

Clark D.E. What has computer-aided molecular design ever done for drug discovery? Expert Opin. Drug Discov. 2006;1:103–110.

Huang S.Y., Zou X. Advances and challenges in protein-ligand docking. Int. J. Mol. Sci. 2010;11:3016–3034.

Sousa S.F., Fernandes P.A., Ramos M.J. Protein-ligand docking: Current status and future challenges. Proteins. 2006;65:15–26.

Rarey M., Kramer B., Lengauer T., Klebe G. A fast flexible docking method using an incremental construction algorithm. J. Mol. Biol. 1996;261:470–489.

Taylor R.D., Jewsbury P.J., Essex J.W. A review of protein-small molecule docking methods. J. Comput. Aided Mol. Des. 2002;16:151–166.

Ain Q.U., Aleksandrova A., Roessler F.D., Ballester P.J. Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening. Wiley Interdiscip. Rev. Comput Mol. Sci. 2015;5:405–424.

Moitessier N., Englebienne P., Lee D., Lawandi J., Corbeil C.R. Towards the development of universal, fast and highly accurate docking/scoring methods: A long way to go. Br. J. Pharm. 2008;153:7–26.

Huang S.Y., Grinter S.Z., Zou X. Scoring functions and their evaluation methods for protein-ligand docking: Recent advances and future directions. Phys. Chem. Chem. Phys. 2010;12:12899–12908.

Guedes I.A., Pereira F.S.S., Dardenne L.E. Empirical scoring functions for structure-based virtual screening: Applications, critical aspects, and challenges. Front. Pharm. 2018;9:1089.

Lopez-Vallejo F., Caulfield T., Martinez-Mayorga K., Giulianotti M.A., Nefzi A., Houghten R.A., Medina-Franco J.L. Integrating virtual screening and combinatorial chemistry for accelerated drug discovery. Comb. Chem. High. Throughput Screen. 2011;14:475–487.

Kapetanovic I.M. Computer-aided drug discovery and development (caddd): In silico-chemico-biological approach. Chem. Biol. Interact. 2008;171:165–176.

Akoka J., Comyn-Wattiau I., Laoufi N. Research on big data—A systematic mapping study. Comput. Stand. Interfaces. 2017;54:105–115.

Warren G.L., Andrews C.W., Capelli A.M., Clarke B., LaLonde J., Lambert M.H., Lindvall M., Nevins N., Semus S.F., Senger S., et al. A critical assessment of docking programs and scoring functions. J. Med. Chem. 2006;49:5912–5931.

Bordoli L., Kiefer F., Arnold K., Benkert P., Battey J., Schwede T. Protein structure homology modeling using swiss-model workspace. Nat. Protoc. 2009;4:1–13.

Raub S., Steffen A., Kamper A., Marian C.M. Aiscore chemically diverse empirical scoring function employing quantum chemical binding energies of hydrogen-bonded complexes. J. Chem. Inf. Model. 2008;48:1492–1510.

Secchi P. On the role of statistics in the era of big data: A call for a debate. Stat. Probab. Lett. 2018;136:10–14.

Cox D.R., Kartsonaki C., Keogh R.H. Big data: Some statistical issues. Stat. Probab. Lett. 2018;136:111–115.

Bornmann L. Measuring the societal impact of research. EMBO Rep. 2012;13:673.

Mårtensson P., Fors U., Wallin S.-B., Zander U., Nilsson G.H. Evaluating research: A multidisciplinary approach to assessing research practice and quality. Res. Policy. 2016;45:593–603.

Cabrera M.T., Brewer E.M., Grant L., Tarczy-Hornoch K. Exudative retinal detachment documented by handheld spectral domain optical coherence tomography after retinal laser photocoagulation for retinopathy of prematurity. Retin. Cases Brief. Rep. 2018

Ghosh A.K., Osswald H.L., Prato G. Recent progress in the development of HIV-1 protease inhibitors for the treatment of hiv/aids. J. Med. Chem. 2016;59:5172–5208.

Barmania F., Pepper M.S. C-c chemokine receptor type five (ccr5): An emerging target for the control of hiv infection. Appl. Transl. Genom. 2013;2:3–16.

Ain Q.U., Aleksandrova A., Roessler F.D., Ballester P.J. Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening. Wiley Interdiscip. Rev. Comput Mol. Sci. 2015;5:405–424.

Kuritzkes D., Kar S., Kirkpatrick P. Maraviroc. Nat. Rev. Drug Discov. 2008;7:15.

Lusher S.J., McGuire R., van Schaik R.C., Nicholson C.D., de Vlieg J. Data-driven medicinal chemistry in the era of big data. Drug Discov. Today. 2014;19:859–868.

Ebejer J.P., Fulle S., Morris G.M., Finn P.W. The emerging role of cloud computing in molecular modelling. J. Mol. Graph. Model. 2013;44:177–187

Kissin I. What can big data on academic interest reveal about a drug? Reflections in three major us databases. Trends Pharm. Sci. 2018;39:248–257.

Mak K.K., Pichika M.R. Artificial intelligence in drug development: Present status and future prospects. Drug Discov. Today. 2019;24:773–780.

Bishop C.M. Model-based machine learning. Philos Trans. A Math. Phys. Eng. Sci. 2013;371:20120222.

Hernández-Santoyo A, Tenorio-Barajas AY, Altuzar V, Vivanco-Cid H, Mendoza-Barrera C. Protein-protein and protein-ligand docking. InProtein engineering-technology and application 2013.

Duch W., Swaminathan K., Meller J. Artificial intelligence approaches for rational drug design and discovery. Curr. Pharm. Des. 2007;13:1497–1508. doi: 10.2174/138161207780765954.

Probst C., Schneider S., Loskill P. High-throughput Organ-on-a-chip systems: Current status and remaining challenges. Curr. Opin. Biomed. Eng. 2018;6:33–41. doi: 10.1016/j.cobme.2018.02.004.

IBM Ibm Watson. [(accessed on 1 May 2019)]; Available online: https://www.ibm.com/watson.

Feher M. Consensus scoring for protein-ligand interactions. Drug. Discov. Today, 2006; 11: 421-428.

O‘Boyle NM, Liebeschuetz JW, Cole JC. Testing assumptions and hypotheses for rescoring success in protein-ligand docking. J. Chem. Inf. Model, 2009; 49: 1871-1878.

Akamatsu M. Current State and Perspectives of 3D-QSAR. Curr. Top. Med. Chem. 2002;2:1381–1394.

Verma RP, Hansch C. Camptothecins: A SAR/QSAR Study. Chem. Rev. 2009;109:213–235.

Bernard D, Coop A, MacKerell AD., Jr. Conformationally sampled pharmacophore for peptidic delta opioid ligands. J. Med. Chem. 2005;48(24):7773–80.

Duchowicz PR, Castro EA, Fernandez FM, Gonzalez MP. A new search algorithm for QSPR/QSAR theories: Normal boiling points of some organic molecules. Chem Phys Lett. 2005;412:376–380.

Wade RC, Henrich S, Wang T. Using 3D protein structures to derive 3D-QSARs. Drug Discovery Today: Technologies. 2004;1(3):241–246.

Halloway MK. A priori prediction of ligand affinity by energy minimization. Perspectives in Drug Discovery and Design. 1998;9(11):63–84.

Bohl CE, Chang C, Mohler ML, Chen J, Miller DD, Swaan PW, Dalton JT. A ligand-based approach to identify quantitative structure-activity relationships for the androgen receptor. J. Med. Chem. 2004;47(15):3765–76.

Becker OM, Dhanoa DS, Marantz Y, Chen D, Shacham S, Cheruku S, Heifetz A, Mohanty P, Fichman M, Sharadendu A. An integrated in silico 3D model-driven discovery of a novel, potent, and selective amidosulfonamide 5-HT1A agonist (PRX-00023) for the treatment of anxiety and depression. J. Med. Chem., 2006; 49: 3116-3135.

Johnson MA, Maggiora GM. Concepts and Applications of Molecular Similarity, Wiley, New York, 1990.

Stumpfe D, Bill A, Novak N, Loch G, Blockus H, Geppert H, Becker T, Schmitz A, Hoch M, Kolanus W. Targeting multifunctional proteins by virtual screening: structurally diverse cytohesin inhibitors with differentiated biological functions. Chem. Biol., 2010; 5: 839-849.

Cramer RD, Patterson DE, Bunce JD. Comparative molecular field analysis (CoMFA) Effect of shape on binding of steroids to carrier proteins. J. Am. Chem. Soc., 1988; 110: 5959-5967.

Kiaris H, Spandidos DA. Mutations of Ras Genes in Human Tumors. International Journal of Oncology, 1995; 7(3): 413-421.

Zhang S. Computer-aided drug discovery and development. In Drug Design and Discovery. 2011; 716:23-38.

Kapetanovic IM. Computer-aided drug discovery and development (CADD): insilico-chemico-biological approach Chemico-biological interactions. 2008; 171(2):165-76.

Shivraj Jadhav, Komal Nikam, Anand Gandhi, Kishor Salunkhe, Narendra Shinde. Applications of computer science in Pharmacy: An overview. Natl J Physiol Pharm Pharmacol. 2012; 2(1): 1-9