Generic placeholder image

Current Pharmaceutical Design

Editor-in-Chief

ISSN (Print): 1381-6128
ISSN (Online): 1873-4286

Review Article

Computational Approaches to Designing Antiviral Drugs against COVID-19: A Comprehensive Review

Author(s): Mohan P. Singh*, Nidhi Singh, Divya Mishra, Saba Ehsan, Vivek K. Chaturvedi, Anupriya Chaudhary, Veer Singh and Emanuel Vamanu*

Volume 29, Issue 33, 2023

Published on: 01 November, 2023

Page: [2601 - 2617] Pages: 17

DOI: 10.2174/0113816128259795231023193419

Price: $65

conference banner
Abstract

The global impact of the COVID-19 pandemic caused by SARS-CoV-2 necessitates innovative strategies for the rapid development of effective treatments. Computational methodologies, such as molecular modelling, molecular dynamics simulations, and artificial intelligence, have emerged as indispensable tools in the drug discovery process. This review aimed to provide a comprehensive overview of these computational approaches and their application in the design of antiviral agents for COVID-19. Starting with an examination of ligand-based and structure-based drug discovery, the review has delved into the intricate ways through which molecular modelling can accelerate the identification of potential therapies. Additionally, the investigation extends to phytochemicals sourced from nature, which have shown promise as potential antiviral agents. Noteworthy compounds, including gallic acid, naringin, hesperidin, Tinospora cordifolia, curcumin, nimbin, azadironic acid, nimbionone, nimbionol, and nimocinol, have exhibited high affinity for COVID-19 Mpro and favourable binding energy profiles compared to current drugs. Although these compounds hold potential, their further validation through in vitro and in vivo experimentation is imperative. Throughout this exploration, the review has emphasized the pivotal role of computational biologists, bioinformaticians, and biotechnologists in driving rapid advancements in clinical research and therapeutic development. By combining state-of-the-art computational techniques with insights from structural and molecular biology, the search for potent antiviral agents has been accelerated. The collaboration between these disciplines holds immense promise in addressing the transmissibility and virulence of SARS-CoV-2.

Keywords: SARS-CoV-2, molecular modelling, molecular docking, antiviral agents, natural resources, COVID-19.

Next »
[1]
Hwang W, Lei W, Katritsis NM, MacMahon M, Chapman K, Han N. Current and prospective computational approaches and challenges for developing COVID-19 vaccines. Adv Drug Deliv Rev 2021; 172: 249-74.
[http://dx.doi.org/10.1016/j.addr.2021.02.004] [PMID: 33561453]
[2]
Gurung AB, Ali MA, Lee J, Farah MA, Al-Anazi KM. An updated review of computer-aided drug design and its application to COVID-19. BioMed Res Int 2021; 2021: 1-18.
[http://dx.doi.org/10.1155/2021/8853056] [PMID: 34258282]
[3]
Basu S, Ramaiah S, Anbarasu A. In-silico strategies to combat COVID-19: A comprehensive review. Biotechnol Genet Eng Rev 2021; 37(1): 64-81.
[http://dx.doi.org/10.1080/02648725.2021.1966920] [PMID: 34470564]
[4]
Shah B, Modi P, Sagar SR. In silico studies on therapeutic agents for COVID-19: Drug repurposing approach. Life Sci 2020; 252: 117652.
[http://dx.doi.org/10.1016/j.lfs.2020.117652] [PMID: 32278693]
[5]
Meganck RM, Baric RS. Developing therapeutic approaches for twenty-first-century emerging infectious viral diseases. Nat Med 2021; 27(3): 401-10.
[http://dx.doi.org/10.1038/s41591-021-01282-0] [PMID: 33723456]
[6]
Wang J, Zhang Y, Nie W, Luo Y, Deng L. Computational anti- COVID-19 drug design: Progress and challenges. Brief Bioinform 2022; 23(1): bbab484.
[http://dx.doi.org/10.1093/bib/bbab484] [PMID: 34850817]
[7]
Layan M, Gilboa M, Gonen T. Impact of bnt162b2 vaccination and isolation on SARS-CoV-2 transmission in Israeli households: An observational study. medRxiv 2021.
[http://dx.doi.org/10.1101/2021.07.12.21260377]
[8]
Prunas O, Warren JL, Crawford FW. Vaccination with bnt162b2 reduces transmission of SARS-CoV-2 to household contacts in Israel. medRxiv 2021.
[http://dx.doi.org/10.1101/2021.07.13.21260393]
[9]
Lynch ML, Snell EH, Bowman SEJ. Structural biology in the time of COVID-19: Perspectives on methods and milestones. IUCrJ 2021; 8(3): 335-41.
[http://dx.doi.org/10.1107/S2052252521003948] [PMID: 33953920]
[10]
Brown N, Ertl P, Lewis R. Artificial intelligence in chemistry and drug design. J Comput Aided Mol Des 2020; 34: 709-15.
[http://dx.doi.org/10.1007/s10822-020-00317-x]
[11]
Meisburger SP, Thomas WC, Watkins MB, Ando N. X-ray scattering studies of protein structural dynamics. Chem Rev 2017; 117(12): 7615-72.
[http://dx.doi.org/10.1021/acs.chemrev.6b00790] [PMID: 28558231]
[12]
Shanmugaraj B, Siriwattananon K, Wangkanont K, Phoolcharoen W. Perspectives on monoclonal antibody therapy as potential therapeutic intervention for Coronavirus disease-19 (COVID-19). Asian Pac J Allergy Immunol 2020; 38(1): 10-8.
[PMID: 32134278]
[13]
Andricopulo A, Salum L, Abraham D. Structure-based drug design strategies in medicinal chemistry. Curr Top Med Chem 2009; 9(9): 771-90.
[http://dx.doi.org/10.2174/156802609789207127] [PMID: 19754394]
[14]
Dhanaraj P, Muthiah I, Rozbu MR, Nuzhat S, Paulraj MS. Computational studies on T2Rs agonist-based anti–COVID-19 drug design. Front Mol Biosci 2021; 8: 637124.
[http://dx.doi.org/10.3389/fmolb.2021.637124] [PMID: 34485378]
[15]
Alaqeel SI, Arumugam N, Almansour AI, et al. Highly functionalized dispiropyrrolidine embedded indandione hybrids as potent cholinesterase inhibitors. J King Saud Univ Sci 2023; 35(5): 102706.
[http://dx.doi.org/10.1016/j.jksus.2023.102706]
[16]
Zhou P, Yang XL, Wang XG, et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 2020; 579(7798): 270-3.
[http://dx.doi.org/10.1038/s41586-020-2012-7] [PMID: 32015507]
[17]
Gorbalenya AE, Baker SC, Baric RS, et al. The species severe acute respiratory syndrome-related coronavirus: Classifying 2019-nCoV and naming it SARS-CoV-2. Nat Microbiol 2020; 5(4): 536-44.
[http://dx.doi.org/10.1038/s41564-020-0695-z]
[18]
Barghash RF, Fawzy IM, Chandrasekar V, Singh AV, Katha U, Mandour AA. In silico modeling as a perspective in developing potential vaccine candidates and therapeutics for COVID-19. Coatings 2021; 11(11): 1273.
[http://dx.doi.org/10.3390/coatings11111273]
[19]
Kuba K, Imai Y, Rao S, et al. A crucial role of angiotensin converting enzyme 2 (ACE2) in SARS coronavirus–induced lung injury. Nat Med 2005; 11(8): 875-9.
[http://dx.doi.org/10.1038/nm1267] [PMID: 16007097]
[20]
Nguyen HL, Lan PD, Thai NQ, Nissley DA, O’Brien EP, Li MS. Does SARS-CoV-2 bind to human ACE2 more strongly than does SARS-CoV? J Phys Chem B 2020; 124(34): 7336-47.
[http://dx.doi.org/10.1021/acs.jpcb.0c04511] [PMID: 32790406]
[21]
Zumla A, Chan JFW, Azhar EI, Hui DSC, Yuen KY. Coronaviruses - drug discovery and therapeutic options. Nat Rev Drug Discov 2016; 15(5): 327-47.
[http://dx.doi.org/10.1038/nrd.2015.37] [PMID: 26868298]
[22]
Yuan Y, Cao D, Zhang Y, et al. Cryo-EM structures of MERS- CoV and SARS-CoV spike glycoproteins reveal the dynamic receptor binding domains. Nat Commun 2017; 8(1): 15092.
[http://dx.doi.org/10.1038/ncomms15092] [PMID: 28393837]
[23]
Kishk SM, Kishk RM, Yassen ASA, et al. Molecular insights into human transmembrane protease serine-2 (TMPS2) inhibitors against SARS-CoV2: Homology modelling, molecular dynamics, and docking studies. Molecules 2020; 25(21): 5007.
[http://dx.doi.org/10.3390/molecules25215007] [PMID: 33137894]
[24]
Hall DC Jr, Ji HF. A search for medications to treat COVID-19 via in silico molecular docking models of the SARS-CoV-2 spike glycoprotein and 3CL protease. Travel Med Infect Dis 2020; 35: 101646.
[http://dx.doi.org/10.1016/j.tmaid.2020.101646] [PMID: 32294562]
[25]
Palanisamy K, Rubavathy SME, Prakash M, Thilagavathi R, Hosseini-Zare MS, Selvam C. Antiviral activities of natural compounds and ionic liquids to inhibit the Mpro of SARS-CoV-2: A computational approach. RSC Adv 2022; 12(6): 3687-95.
[http://dx.doi.org/10.1039/D1RA08604A] [PMID: 35425367]
[26]
Kumar S, Kovalenko S, Bhardwaj S, et al. Drug repurposing against SARS-CoV-2 using computational approaches. Drug Discov Today 2022; 27(7): 2015-27.
[http://dx.doi.org/10.1016/j.drudis.2022.02.004] [PMID: 35151891]
[27]
Aftab SO, Ghouri MZ, Masood MU, et al. Analysis of SARS- CoV-2 RNA-dependent RNA polymerase as a potential therapeutic drug target using a computational approach. J Transl Med 2020; 18(1): 275.
[http://dx.doi.org/10.1186/s12967-020-02439-0]
[28]
Rakib A, Nain Z, Sami SA, et al. A molecular modelling approach for identifying antiviral selenium-containing heterocyclic compounds that inhibit the main protease of SARS-CoV-2: An in silico investigation. Brief Bioinform 2021; 22(2): 1476-98.
[http://dx.doi.org/10.1093/bib/bbab045] [PMID: 33623995]
[29]
De Felice F, Polimeni A. Coronavirus disease (COVID-19): A machine learning bibliometric analysis. In Vivo 2020; 34(S3): 1613-7.
[http://dx.doi.org/10.21873/invivo.11951] [PMID: 32503819]
[30]
Hassantabar S, Ahmadi M, Sharifi A. Diagnosis and detection of infected tissue of COVID-19 patients based on lung x-ray image using convolutional neural network approaches. Chaos Solitons Fractals 2020; 140: 110170.
[http://dx.doi.org/10.1016/j.chaos.2020.110170] [PMID: 32834651]
[31]
Heidari A, Jafari Navimipour N, Unal M, Toumaj S. Machine learning applications for COVID-19 outbreak management. Neural Comput Appl 2022; 34(18): 15313-48.
[http://dx.doi.org/10.1007/s00521-022-07424-w] [PMID: 35702664]
[32]
Kushwaha S, Bahl S, Bagha AK, et al. Significant applications of machine learning for COVID-19 pandemic. J Ind Integr 2020; 5(4): 453-79.
[http://dx.doi.org/10.1142/S2424862220500268]
[33]
Mukherjee H, Ghosh S, Dhar A, Obaidullah SM, Santosh KC, Roy K. Deep neural network to detect COVID-19: One architecture for both CT scans and chest X-rays. Appl Intell 2021; 51(5): 2777-89.
[http://dx.doi.org/10.1007/s10489-020-01943-6] [PMID: 34764562]
[34]
Proia E, Ragno A, Antonini L, et al. Ligand-based and structure-based studies to develop predictive models for SARS-CoV-2 main protease inhibitors through the 3d-qsar.com portal. J Comput Aided Mol Des 2022; 36(7): 483-505.
[http://dx.doi.org/10.1007/s10822-022-00460-7] [PMID: 35716228]
[35]
Yang Y, Zhu Z, Wang X, et al. Ligand-based approach for predicting drug targets and for virtual screening against COVID-19. Brief Bioinform 2021; 22(2): 1053-64.
[http://dx.doi.org/10.1093/bib/bbaa422] [PMID: 33461215]
[36]
Fayed MAA, El-Behairy MF, Abdallah IA, et al. Structure- and ligand-based in silico studies towards the repurposing of marine bioactive compounds to target SARS-CoV-2. Arab J Chem 2021; 14(4): 103092.
[http://dx.doi.org/10.1016/j.arabjc.2021.103092] [PMID: 34909063]
[37]
Dhanalakshmi M, Das K, Pandya M, et al. Artificial neural network-based study predicts gs-441524 as a potential inhibitor of SARS-CoV-2 activator protein furin: A polypharmacology approach. Appl Biochem Biotechnol 2022; 194(10): 4511-29.
[http://dx.doi.org/10.1007/s12010-022-03928-2] [PMID: 35507249]
[38]
Schneider P, Tanrikulu Y, Schneider G. Self-organizing maps in drug discovery: Compound library design, scaffold-hopping, repurposing. Curr Med Chem 2008; 46: 2319-23.
[PMID: 19149576]
[39]
Hristozov DP, Oprea TI, Gasteiger J. Virtual screening applications: A study of ligand-based methods and different structure representations in four different scenarios. J Comput Aided Mol Des 2007; 21(10-11): 617-40.
[http://dx.doi.org/10.1007/s10822-007-9145-8] [PMID: 18008169]
[40]
Amin SA, Ghosh K, Gayen S, Jha T, Zaidi N, Rahman SU. Chemical-informatics approach to COVID-19 drug discovery: Monte Carlo based QSAR, virtual screening and molecular docking study of some in-house molecules as papain-like protease (PLpro) inhibitors. J Biomol Struct Dyn 2021; 39(13): 4764-73.
[http://dx.doi.org/10.1080/07391102.2020.1780946] [PMID: 32568618]
[41]
Saeed M, Saeed A, Alam MJ, Alreshidi M. Receptor-based pharmacophore modelling in the search for natural products for COVID-19 Mpro. Molecules 2021; 26(6): 1549.
[http://dx.doi.org/10.3390/molecules26061549] [PMID: 33799871]
[42]
Kurogi Y, Güner O. Pharmacophore modeling and three-dimensional database searching for drug design using catalyst. Curr Med Chem 2001; 8(9): 1035-55.
[http://dx.doi.org/10.2174/0929867013372481] [PMID: 11472240]
[43]
Dixon SL, Smondyrev AM, Rao SN. PHASE: A novel approach to pharmacophore modeling and 3D database searching. Chem Biol Drug Des 2006; 67(5): 370-2.
[http://dx.doi.org/10.1111/j.1747-0285.2006.00384.x] [PMID: 16784462]
[44]
Seidel T, Bryant SD, Ibis G, Poli G, Langer T. 3D pharmacophore modelling techniques in computer-aided molecular design using ligandscout. Tutorials in Chemoinformatics 2017; pp. 279-309.
[45]
Zhao X, Yuan M, Huang B, Ji H, Zhu L. Ligand-based pharmacophore model of N-Aryl and N-Heteroaryl piperazine α1A-adrenoceptors antagonists using GALAHAD. J Mol Graph Model 2010; 29(2): 126-36.
[http://dx.doi.org/10.1016/j.jmgm.2010.05.002] [PMID: 20538497]
[46]
Liu X, Ouyang S, Yu B, et al. PharmMapper server: A web server for potential drug target identification using pharmacophore mapping approach. Nucleic Acids Res 2010; 38(S2): W609-14.
[http://dx.doi.org/10.1093/nar/gkq300] [PMID: 20430828]
[47]
Culletta G, Gulotta MR, Perricone U, et al. Exploring the SARS- CoV-2 proteome in the search of potential inhibitors via structure-based pharmacophore modelling/docking approach. Computation 2020; 8(3): 77.
[http://dx.doi.org/10.3390/computation8030077]
[48]
Jin Z, Du X, Xu Y, et al. Structure of Mpro from SARS-CoV-2 and discovery of its inhibitors. Nature 2020; 582(7811): 289-93.
[http://dx.doi.org/10.1038/s41586-020-2223-y] [PMID: 32272481]
[49]
Blundell TL. Structure-based drug design. Nature 1996; 384(S6604): 23-6.
[PMID: 8895597]
[50]
Song CM, Lim SJ, Tong JC. Recent advances in computer-aided drug design. Brief Bioinform 2009; 10(5): 579-91.
[http://dx.doi.org/10.1093/bib/bbp023] [PMID: 19433475]
[51]
Lavecchia A, Giovanni C. Virtual screening strategies in drug discovery: A critical review. Curr Med Chem 2013; 20(23): 2839-60.
[http://dx.doi.org/10.2174/09298673113209990001] [PMID: 23651302]
[52]
Meng XY, Zhang HX, Mezei M, Cui M. Molecular docking: A powerful approach for structure-based drug discovery. Curr Computeraided Drug Des 2011; 7(2): 146-57.
[http://dx.doi.org/10.2174/157340911795677602] [PMID: 21534921]
[53]
Sanders MPA, McGuire R, Roumen L, et al. From the protein’s perspective: The benefits and challenges of protein structure-based pharmacophore modeling. MedChemComm 2012; 3(1): 28-38.
[http://dx.doi.org/10.1039/C1MD00210D]
[54]
Ewing TJA, Makino S, Skillman AG, Kuntz ID. DOCK 4.0: Search strategies for automated molecular docking of flexible molecule databases. J Comput Aided Mol Des 2001; 15(5): 411-28.
[http://dx.doi.org/10.1023/A:1011115820450] [PMID: 11394736]
[55]
Jones G, Willett P, Glen RC, Leach AR, Taylor R. Development and validation of a genetic algorithm for flexible docking. J Mol Biol 1997; 267(3): 727-48.
[http://dx.doi.org/10.1006/jmbi.1996.0897] [PMID: 9126849]
[56]
Tietze S, Apostolakis J. GlamDock: Development and validation of a new docking tool on several thousand protein-ligand complexes. J Chem Inf Model 2007; 47(4): 1657-72.
[http://dx.doi.org/10.1021/ci7001236] [PMID: 17585857]
[57]
Morris GM, Goodsell DS, Huey R, Olson AJ. Distributed automated docking of flexible ligands to proteins: Parallel applications of AutoDock 2.4. J Comput Aided Mol Des 1996; 10(4): 293-304.
[http://dx.doi.org/10.1007/BF00124499] [PMID: 8877701]
[58]
Trott O, Olson AJ. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 2010; 31(2): 455-61.
[PMID: 19499576]
[59]
Forli S, Huey R, Pique ME, Sanner MF, Goodsell DS, Olson AJ. Computational protein–ligand docking and virtual drug screening with the AutoDock suite. Nat Protoc 2016; 11(5): 905-19.
[http://dx.doi.org/10.1038/nprot.2016.051] [PMID: 27077332]
[60]
Sinha SK, Prasad SK, Islam MA, et al. Identification of bioactive compounds from Glycyrrhiza glabra as possible inhibitor of SARS-CoV-2 spike glycoprotein and non-structural protein-15: A pharmacoinformatics study. J Biomol Struct Dyn 2021; 39(13): 4686-700.
[http://dx.doi.org/10.1080/07391102.2020.1779132] [PMID: 32552462]
[61]
Souza PFN, Lopes FES, Amaral JL, Freitas CDT, Oliveira JTA. A molecular docking study revealed that synthetic peptides induced conformational changes in the structure of SARS-CoV-2 spike glycoprotein, disrupting the interaction with human ACE2 receptor. Int J Biol Macromol 2020; 164: 66-76.
[http://dx.doi.org/10.1016/j.ijbiomac.2020.07.174] [PMID: 32693122]
[62]
Dhameliya TM, Nagar PR, Gajjar ND. Systematic virtual screening in search of SARS-CoV-2 inhibitors against spike glycoprotein: Pharmacophore screening, molecular docking, ADMET analysis and MD simulations. Mol Divers 2022; 26(5): 2775-92.
[http://dx.doi.org/10.1007/s11030-022-10394-9] [PMID: 35132518]
[63]
Gyebi GA, Adegunloye AP, Ibrahim IM, Ogunyemi OM, Afolabi SO, Ogunro OB. Prevention of SARS-CoV-2 cell entry: Insight from in silico interaction of drug-like alkaloids with spike glycoprotein, human ACE2, and TMPRSS2. J Biomol Struct Dyn 2022; 40(5): 2121-45.
[http://dx.doi.org/10.1080/07391102.2020.1835726] [PMID: 33089728]
[64]
Shekhar N, Sarma P, Prajapat M, et al. In silico structure-based repositioning of approved drugs for spike glycoprotein S2 domain fusion peptide of SARS-CoV-2: Rationale from molecular dynamics and binding free energy calculations. mSystems 2020; 5(5): e00382-20.
[http://dx.doi.org/10.1128/mSystems.00382-20] [PMID: 32963099]
[65]
Dubey K, Dubey R. Computation screening of narcissoside a glycosyloxyflavone for potential novel coronavirus 2019 (COVID-19) inhibitor. Biomed J 2020; 43(4): 363-7.
[http://dx.doi.org/10.1016/j.bj.2020.05.002] [PMID: 32426388]
[66]
Trezza A, Iovinelli D, Santucci A, Prischi F, Spiga O. An integrated drug repurposing strategy for the rapid identification of potential SARS-CoV-2 viral inhibitors. Sci Rep 2020; 10(1): 13866.
[http://dx.doi.org/10.1038/s41598-020-70863-9] [PMID: 32807895]
[67]
Miroshnychenko KV, Shestopalova AV. Combined use of the hepatitis C drugs and amentoflavone could interfere with binding of the spike glycoprotein of SARS-CoV-2 to ACE2: The results of a molecular simulation study. J Biomol Struct Dyn 2022; 40(19): 8672-86.
[http://dx.doi.org/10.1080/07391102.2021.1914168] [PMID: 33896392]
[68]
Vardhan S, Sahoo SK. Virtual screening by targeting proteolytic sites of furin and TMPRSS2 to propose potential compounds obstructing the entry of SARS-CoV-2 virus into human host cells. J Tradit Complement Med 2022; 12(1): 6-15.
[http://dx.doi.org/10.1016/j.jtcme.2021.04.001] [PMID: 33868970]
[69]
Hosseini M, Chen W, Xiao D, Wang C. Computational molecular docking and virtual screening revealed promising SARS-CoV-2 drugs. Precis Clin Med 2021; 4(1): 1-16.
[http://dx.doi.org/10.1093/pcmedi/pbab001] [PMID: 33842834]
[70]
Singh R, Bhardwaj VK, Das P, Purohit R. A computational approach for rational discovery of inhibitors for non-structural protein 1 of SARS-CoV-2. Comput Biol Med 2021; 135: 104555.
[http://dx.doi.org/10.1016/j.compbiomed.2021.104555] [PMID: 34144270]
[71]
Goodsell DS, Morris GM, Olson AJ. Automated docking of flexible ligands: Applications of autodock. J Mol Recognit 1996; 9(1): 1-5.
[http://dx.doi.org/10.1002/(SICI)1099-1352(199601)9:1<1::AID-JMR241>3.0.CO;2-6] [PMID: 8723313]
[72]
Annamala MK, Inampudi KK, Guruprasad L. Docking of phosphonate and trehalose analog inhibitors into M. tuberculosis mycolyltransferase Ag85C: Comparison of the two scoring fitness functions GoldScore and ChemScore, in the GOLD software. Bioinformation 2007; 1(9): 339-50.
[http://dx.doi.org/10.6026/97320630001339] [PMID: 17597917]
[73]
Repasky MP, Shelley M, Friesner RA. Flexible ligand docking with glide. Curr Protoc Bioinf 2007; (1): 12.
[PMID: 18428795]
[74]
Schulz-Gasch T, Stahl M. Binding site characteristics in structure-based virtual screening: Evaluation of current docking tools. J Mol Model 2003; 9(1): 47-57.
[http://dx.doi.org/10.1007/s00894-002-0112-y] [PMID: 12638011]
[75]
Yang JM, Chen CC. GEMDOCK: A generic evolutionary method for molecular docking. Proteins 2004; 55(2): 288-304.
[http://dx.doi.org/10.1002/prot.20035] [PMID: 15048822]
[76]
Marialke J, Tietze S, Apostolakis J. Similarity based docking. J Chem Inf Model 2008; 48(1): 186-96.
[http://dx.doi.org/10.1021/ci700124r] [PMID: 18044949]
[77]
Li Y, Frenz CM, Li Z, et al. Virtual and in vitro bioassay screening of phytochemical inhibitors from flavonoids and isoflavones against Xanthine oxidase and Cyclooxygenase-2 for gout treatment. Chem Biol Drug Des 2011; 81(4): 537-44.
[78]
Abagyan R, Totrov M, Kuznetsov D. ICM? A new method for protein modeling and design: Applications to docking and structure prediction from the distorted native conformation. J Comput Chem 1994; 15(5): 488-506.
[http://dx.doi.org/10.1002/jcc.540150503]
[79]
Chen YC. Beware of docking! Trends Pharmacol Sci 2015; 36(2): 78-95.
[http://dx.doi.org/10.1016/j.tips.2014.12.001] [PMID: 25543280]
[80]
Jawla S, Kumar Y. Molecular docking interaction of Pinitol (ligand) with dipeptidyl peptidase 4 receptor (PDB 3C45). World Appl Sci J 2013; 24(12): 1629-34.
[81]
Corbeil CR, Englebienne P, Moitessier N. Docking ligands into flexible and solvated macromolecules. 1. Development and validation of FITTED 1.0. J Chem Inf Model 2007; 47(2): 435-49.
[http://dx.doi.org/10.1021/ci6002637] [PMID: 17305329]
[82]
Li H, Leung KS, Wong MH. idock: A multithreaded virtual screening tool for flexible ligand docking. 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. 09-12 May 2012; San Diego, CA, USA. 2012; p. 77.
[http://dx.doi.org/10.1109/CIBCB.2012.6217214]
[83]
Davis IW, Baker D. RosettaLigand docking with full ligand and receptor flexibility. J Mol Biol 2009; 385(2): 381-92.
[http://dx.doi.org/10.1016/j.jmb.2008.11.010] [PMID: 19041878]
[84]
Yang SY. Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Drug Discov Today 2010; 15(11-12): 444-50.
[http://dx.doi.org/10.1016/j.drudis.2010.03.013] [PMID: 20362693]
[85]
Li L, Chen R, Weng Z. RDOCK: Refinement of rigid-body protein docking predictions. Proteins 2003; 53(3): 693-707.
[http://dx.doi.org/10.1002/prot.10460] [PMID: 14579360]
[86]
Güner OF, Bowen JP. Setting the record straight: The origin of the pharmacophore concept. J Chem Inf Model 2014; 54(5): 1269-83.
[http://dx.doi.org/10.1021/ci5000533] [PMID: 24745881]
[87]
Wermuth CG, Ganellin CR, Lindberg P, Mitscher LA. Glossary of terms used in medicinal chemistry (IUPAC Recommendations 1998). Pure Appl Chem 1998; 70(5): 1129-43.
[http://dx.doi.org/10.1351/pac199870051129]
[88]
Mitra K, Ghanta P, Acharya S, Chakrapani G, Ramaiah B, Doble M. Dual inhibitors of SARS-CoV-2 proteases: Pharmacophore and molecular dynamics based drug repositioning and phytochemical leads. J Biomol Struct Dyn 2021; 39(16): 6324-37.
[http://dx.doi.org/10.1080/07391102.2020.1796802] [PMID: 32698693]
[89]
Ozbuyukkaya G, Ozkirimli Olmez E, Ulgen KO. Discovery of YopE inhibitors by pharmacophore-based virtual screening and docking. ISRN Bioinform 2013; 2013: 1-12.
[http://dx.doi.org/10.1155/2013/640518] [PMID: 25937949]
[90]
Yoshino R, Yasuo N, Sekijima M. Identification of key interactions between SARS-CoV-2 main protease and inhibitor drug candidates. Sci Rep 2020; 10(1): 12493.
[http://dx.doi.org/10.1038/s41598-020-69337-9] [PMID: 32719454]
[91]
Shehroz M, Zaheer T, Hussain T. Computer-aided drug design against spike glycoprotein of SARS-CoV-2 to aid COVID-19 treatment. Heliyon 2020; 6(10): e05278.
[http://dx.doi.org/10.1016/j.heliyon.2020.e05278] [PMID: 33083627]
[92]
Battisti V, Wieder O, Garon A, Seidel T, Urban E, Langer T. A computational approach to identify potential novel inhibitors against the coronavirus SARS-CoV-2. Mol Inform 2020; 39(10): 2000090.
[http://dx.doi.org/10.1002/minf.202000090] [PMID: 32721082]
[93]
Rampogu S, Lee KW. Pharmacophore modelling-based drug repurposing approaches for SARS-CoV-2 therapeutics. Front Chem 2021; 9: 636362.
[http://dx.doi.org/10.3389/fchem.2021.636362] [PMID: 34041221]
[94]
Barnum D, Greene J, Smellie A, Sprague P. Identification of common functional configurations among molecules. J Chem Inf Comput Sci 1996; 36(3): 563-71.
[http://dx.doi.org/10.1021/ci950273r] [PMID: 8690757]
[95]
Li H, Sutter J, Hoffmann R. HypoGen: An automated system for generating 3D predictive pharmacophore models. Pharmacophore perception, development, and use in drug design. 2000; 2: p. 171.
[96]
Richmond NJ, Abrams CA, Wolohan PRN, Abrahamian E, Willett P, Clark RD. GALAHAD: 1. Pharmacophore identification by hypermolecular alignment of ligands in 3D. J Comput Aided Mol Des 2006; 20(9): 567-87.
[http://dx.doi.org/10.1007/s10822-006-9082-y] [PMID: 17051338]
[97]
Patel Y, Gillet VJ, Bravi G, Leach AR. A comparison of the pharmacophore identification programs: Catalyst, DISCO and GASP. J Comput Aided Mol Des 2002; 16(8/9): 653-81.
[http://dx.doi.org/10.1023/A:1021954728347] [PMID: 12602956]
[98]
Wolber G, Langer T. LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J Chem Inf Model 2005; 45(1): 160-9.
[http://dx.doi.org/10.1021/ci049885e] [PMID: 15667141]
[99]
Vilar S, Cozza G, Moro S. Medicinal chemistry and the molecular operating environment (MOE): application of QSAR and molecular docking to drug discovery. Curr Top Med Chem 2008; 8(18): 1555-72.
[http://dx.doi.org/10.2174/156802608786786624] [PMID: 19075767]
[100]
Schneidman-Duhovny D, Dror O, Inbar Y, Nussinov R, Wolfson HJ. PharmaGist: A webserver for ligand-based pharmacophore detection. Nucleic Acids Res 2008; 36(S2): W223-8.
[http://dx.doi.org/10.1093/nar/gkn187] [PMID: 18424800]
[101]
Koes DR, Camacho CJ. Pharmer: Efficient and exact pharmacophore search. J Chem Inf Model 2011; 51(6): 1307-14.
[http://dx.doi.org/10.1021/ci200097m] [PMID: 21604800]
[102]
Rath SL, Kumar K. Investigation of the effect of temperature on the structure of SARS-CoV-2 spike protein by molecular dynamics simulations. Front Mol Biosci 2020; 7: 583523.
[http://dx.doi.org/10.3389/fmolb.2020.583523] [PMID: 33195427]
[103]
Van Der Spoel D, Lindahl E, Hess B, Groenhof G, Mark AE, Berendsen HJC. GROMACS: Fast, flexible, and free. J Comput Chem 2005; 26(16): 1701-18.
[http://dx.doi.org/10.1002/jcc.20291] [PMID: 16211538]
[104]
Salomon-Ferrer R, Case DA, Walker RC. An overview of the Amber biomolecular simulation package. Wiley Interdiscip Rev Comput Mol Sci 2013; 3(2): 198-210.
[http://dx.doi.org/10.1002/wcms.1121]
[105]
Brooks BR, Brooks CL III, Mackerell AD Jr, et al. CHARMM: The biomolecular simulation program. J Comput Chem 2009; 30(10): 1545-614.
[http://dx.doi.org/10.1002/jcc.21287] [PMID: 19444816]
[106]
Krieger E, Vriend G. YASARA View-molecular graphics for all devices-from smartphones to workstations. Bioinformatics 2014; 30(20): 2981-2.
[http://dx.doi.org/10.1093/bioinformatics/btu426] [PMID: 24996895]
[107]
Humphrey W, Dalke A, Schulten K. VMD: Visual molecular dynamics. J Mol Graph 1996; 14(1): 33-38, 27-28.
[http://dx.doi.org/10.1016/0263-7855(96)00018-5] [PMID: 8744570]
[108]
Phillips JC, Braun R, Wang W, et al. Scalable molecular dynamics with NAMD. J Comput Chem 2005; 26(16): 1781-802.
[http://dx.doi.org/10.1002/jcc.20289] [PMID: 16222654]
[109]
Ponder JW. TINKER: Software tools for molecular design. Saint Louis, MO: Washington University School of Medicine 2004; p. 3.
[110]
Patel HM, Ahmad I, Pawara R, Shaikh M, Surana S. In silico search of triple mutant T790M/C797S allosteric inhibitors to conquer acquired resistance problem in non-small cell lung cancer (NSCLC): A combined approach of structure-based virtual screening and molecular dynamics simulation. J Biomol Struct Dyn 2021; 39(4): 1491-505.
[http://dx.doi.org/10.1080/07391102.2020.1734092] [PMID: 32102624]
[111]
Remdesivir for the treatment of COVID-19 Cochrane Database of Systematic Reviews. Cochrane Library 2021.
[112]
Choudhary MI, Shaikh M, tul-Wahab A, et al. In silico identification of potential inhibitors of key SARS-CoV-2 3CL hydrolase (Mpro) via molecular docking, MMGBSA predictive binding energy calculations, and molecular dynamics simulation. PLoS One 2020; 15(7): e0235030.
[http://dx.doi.org/10.1371/journal.pone.0235030] [PMID: 32706783]
[113]
Nagar PR, Gajjar ND, Dhameliya TM. In search of SARS-CoV-2 replication inhibitors: Virtual screening, molecular dynamics simulations and ADMET analysis. J Mol Struct 2021; 1246: 131190.
[http://dx.doi.org/10.1016/j.molstruc.2021.131190] [PMID: 34334813]
[114]
Ghahremanian S, Rashidi MM, Raeisi K, Toghraie D. Molecular dynamics simulation approach for discovering potential inhibitors against SARS-CoV-2: A structural review. J Mol Liq 2022; 354: 118901.
[http://dx.doi.org/10.1016/j.molliq.2022.118901] [PMID: 35309259]
[115]
Steindl TM, Schuster D, Laggner C, Chuang K, Hoffmann RD, Langer T. Parallel screening and activity profiling with HIV protease inhibitor pharmacophore models. J Chem Inf Model 2007; 47(2): 563-71.
[http://dx.doi.org/10.1021/ci600321m] [PMID: 17381173]
[116]
Kirchmair J, Distinto S, Schuster D, Spitzer G, Langer T, Wolber G. Enhancing drug discovery through in silico screening: Strategies to increase true positives retrieval rates. Curr Med Chem 2008; 15(20): 2040-53.
[http://dx.doi.org/10.2174/092986708785132843] [PMID: 18691055]
[117]
Galati S, Di Stefano M, Martinelli E, Poli G, Tuccinardi T. Recent advances in in silico target fishing. Molecules 2021; 26(17): 5124.
[http://dx.doi.org/10.3390/molecules26175124] [PMID: 34500568]
[118]
Alrasheid AA, Babiker MY, Awad TA. Evaluation of certain medicinal plants compounds as new potential inhibitors of novel corona virus (COVID-19) using molecular docking analysis. In Silico Pharmacol 2021; 9(1): 10.
[http://dx.doi.org/10.1007/s40203-020-00073-8] [PMID: 33432283]
[119]
Jain AS, Sushma P, Dharmashekar C, et al. In silico evaluation of flavonoids as effective antiviral agents on the spike glycoprotein of SARS-CoV-2. Saudi J Biol Sci 2021; 28(1): 1040-51.
[http://dx.doi.org/10.1016/j.sjbs.2020.11.049] [PMID: 33424398]
[120]
Attia GH, Moemen YS, Youns M, Ibrahim AM, Abdou R, El Raey MA. Antiviral zinc oxide nanoparticles mediated by hesperidin and in silico comparison study between antiviral phenolics as anti-SARS-CoV-2. Colloids Surf B Biointerfaces 2021; 203: 111724.
[http://dx.doi.org/10.1016/j.colsurfb.2021.111724] [PMID: 33838582]
[121]
Chowdhury P. In silico investigation of phytoconstituents from Indian medicinal herb ‘Tinospora cordifolia (giloy)’ against SARS- CoV-2 (COVID-19) by molecular dynamics approach. J Biomol Struct Dyn 2021; 39(17): 6792-809.
[http://dx.doi.org/10.1080/07391102.2020.1803968] [PMID: 32762511]
[122]
Maurya VK, Kumar S, Prasad AK, Bhatt MLB, Saxena SK. Structure-based drug designing for potential antiviral activity of selected natural products from Ayurveda against SARS-CoV-2 spike glycoprotein and its cellular receptor. Virusdisease 2020; 31(2): 179-93.
[http://dx.doi.org/10.1007/s13337-020-00598-8] [PMID: 32656311]
[123]
Adegbola PI, Semire B, Fadahunsi OS, Adegoke AE. Molecular docking and ADMET studies of Allium cepa, Azadirachta indica and Xylopia aethiopica isolates as potential anti-viral drugs for Covid-19. Virusdisease 2021; 32(1): 85-97.
[http://dx.doi.org/10.1007/s13337-021-00682-7] [PMID: 33869672]
[124]
Ansori ANM, Kharisma VD, Parikesit AA, et al. Bioactive compounds from mangosteen (Garcinia mangostana L.) as an antiviral agent via dual inhibitor mechanism against SARSCoV-2: An in silico approach. Pharmacogn J 2022; 14(1): 85-90.
[http://dx.doi.org/10.5530/pj.2022.14.12]
[125]
Chikhale RV, Sinha SK, Patil RB, et al. In-silico investigation of phytochemicals from Asparagus racemosus as plausible antiviral agent in COVID-19. J Biomol Struct Dyn 2021; 39(14): 5033-47.
[http://dx.doi.org/10.1080/07391102.2020.1784289] [PMID: 32579064]
[126]
Tito A, Colantuono A, Pirone L, et al. Pomegranate peel extract as an inhibitor of SARS-CoV-2 spike binding to human ACE2 receptor (in vitro): A promising source of novel antiviral drugs. Front Chem 2021; 9: 638187.
[http://dx.doi.org/10.3389/fchem.2021.638187] [PMID: 33996744]
[127]
Mori M, Quaglio D, Calcaterra A, et al. Natural flavonoid derivatives have pan-coronavirus antiviral activity. Microorganisms 2023; 11(2): 314.
[http://dx.doi.org/10.3390/microorganisms11020314] [PMID: 36838279]
[128]
Omer EA, Abdelfatah S, Riedl M, Meesters C, Hildebrandt A, Efferth T. Coronavirus inhibitors targeting nsp16. Molecules 2023; 28(3): 988.
[http://dx.doi.org/10.3390/molecules28030988] [PMID: 36770656]
[129]
Abreu Alves P, Dantas Rocha KA, Bezerra LL, Ayala AP, Vieira Monteiro NK, Pessoa ODL. Withanolides of Athenaea velutina with potential inhibitory properties against SARS coronavirus main protease (mpro): Molecular modeling studies. J Biomol Struct Dyn 2023; 1-9.
[http://dx.doi.org/10.1080/07391102.2023.2167863] [PMID: 36690603]
[130]
Das K, Das P, Almuqbil M, et al. Inhibition of SARS-CoV2 viral infection with natural antiviral plants constituents: An in-silico approach. J King Saud Univ Sci 2023; 35(3): 102534.
[http://dx.doi.org/10.1016/j.jksus.2022.102534] [PMID: 36619666]
[131]
Ghoneum M, Abdulmalek S, Fadel HH. Biobran/MGN-3, an arabinoxylan rice bran, protects against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2): An in vitro and in silico study. Nutrients 2023; 15(2): 453.
[http://dx.doi.org/10.3390/nu15020453] [PMID: 36678324]
[132]
Magurano F, Micucci M, Nuzzo D, et al. A potential host and virus targeting tool against COVID-19: Chemical characterization, antiviral, cytoprotective, antioxidant, respiratory smooth muscle relaxant effects of Paulownia tomentosa Steud. Biomed Pharmacother 2023; 158: 114083.
[http://dx.doi.org/10.1016/j.biopha.2022.114083] [PMID: 36495668]
[133]
Prasetiya FS, Destiarani W, Nuwarda RF, et al. The nanomolar affinity of C-phycocyanin from virtual screening of microalgal bioactive as potential ACE2 inhibitor for COVID-19 therapy. J King Saud Univ Sci 2023; 35(3): 102533.
[http://dx.doi.org/10.1016/j.jksus.2022.102533] [PMID: 36624782]
[134]
Chen YL, Chen CY, Lai KH, Chang YC, Hwang TL. Anti-inflammatory and antiviral activities of flavone C-glycosides of Lophatherum gracile for COVID-19. J Funct Foods 2023; 101: 105407.
[http://dx.doi.org/10.1016/j.jff.2023.105407] [PMID: 36627926]
[135]
Putra WE, Hidayatullah A, Heikal MF, Widiastuti D, Isnanto H. Analysis of three non-structural proteins, NSP1, NSP2, AND NSP10 of SARS-COV-2 as pivotal target proteins for computational drug screening. J Microbiol Biotechnol Food Sci 2023; 12(5): e9586.
[http://dx.doi.org/10.55251/jmbfs.9586]
[136]
Palanisamy K, Maiyelvaganan KR, Kamalakannan S, Thilagavathi R, Selvam C, Prakash M. In silico screening of potential antiviral inhibitors against SARS-CoV-2 main protease. Mol Simul 2023; 49(2): 175-85.
[http://dx.doi.org/10.1080/08927022.2022.2136392]
[137]
Liu W, Zheng W, Cheng L, et al. Citrus fruits are rich in flavonoids for immunoregulation and potential targeting ACE2. Nat Prod Bioprospect 2022; 12(1): 4.
[http://dx.doi.org/10.1007/s13659-022-00325-4] [PMID: 35157175]
[138]
Jamali N, Soureshjani EH, Mobini GR, Samare-Najaf M, Clark CCT, Saffari-Chaleshtori J. Medicinal plant compounds as promising inhibitors of coronavirus (COVID-19) main protease: An in silico study. J Biomol Struct Dyn 2022; 40(17): 8073-84.
[http://dx.doi.org/10.1080/07391102.2021.1906749] [PMID: 33970805]
[139]
Altyar AE, Youssef FS, Kurdi MM, Bifari RJ, Ashour ML. The role of Cannabis sativa L. as a source of cannabinoids against coronavirus 2 (SARS-CoV-2): An in silico study to evaluate their activities and admet properties. Molecules 2022; 27(9): 2797.
[http://dx.doi.org/10.3390/molecules27092797] [PMID: 35566148]
[140]
Zannella C, Giugliano R, Chianese A, et al. Antiviral activity of vitis vinifera leaf extract against SARS-CoV-2 and HSV-1. Viruses 2021; 13(7): 1263.
[http://dx.doi.org/10.3390/v13071263] [PMID: 34209556]
[141]
El-Ashrey MK, Bakr RO, Fayed MAA, Refaey RH, Nissan YM. Pharmacophore based virtual screening for natural product database revealed possible inhibitors for SARS-COV-2 main protease. Virology 2022; 570: 18-28.
[http://dx.doi.org/10.1016/j.virol.2022.03.003] [PMID: 35339903]
[142]
Zhang Y, Li W, Hu Y, et al. Cotton flower metabolites inhibit SARS-CoV-2 main protease. FEBS Open Bio 2022; 12(10): 1886-95.
[http://dx.doi.org/10.1002/2211-5463.13477] [PMID: 36054247]
[143]
Rabi FA, Al Zoubi MS, Kasasbeh GA, Salameh DM, Al-Nasser AD. SARS-CoV-2 and coronavirus disease 2019: What we know so far. Pathogens 2020; 9(3): 231.
[http://dx.doi.org/10.3390/pathogens9030231] [PMID: 32245083]
[144]
Alanagreh L, Alzoughool F, Atoum M. The human coronavirus disease COVID-19: Its origin, characteristics, and insights into potential drugs and its mechanisms. Pathogens 2020; 9(5): 331.
[http://dx.doi.org/10.3390/pathogens9050331] [PMID: 32365466]
[145]
Fouladirad S, Bach H. Development of coronavirus treatments using neutralizing antibodies. Microorganisms 2021; 9(1): 165.
[http://dx.doi.org/10.3390/microorganisms9010165] [PMID: 33451069]
[146]
Yan Y, Pang Y, Lyu Z, et al. The COVID-19 vaccines: Recent development, challenges and prospects. Vaccines 2021; 9(4): 349.
[http://dx.doi.org/10.3390/vaccines9040349] [PMID: 33916489]
[147]
Martínez-González B, Soria ME, Vázquez-Sirvent L, et al. SARS- CoV-2 mutant spectra at different depth levels reveal an overwhelming abundance of low frequency mutations. Pathogens 2022; 11(6): 662.
[http://dx.doi.org/10.3390/pathogens11060662] [PMID: 35745516]
[148]
Jena AB, Duttaroy AK. A computational approach for molecular characterization of covaxin (bbv152) and its ingredients for assessing its efficacy against COVID-19. Future Pharmacology 2022; 2(3): 306-19.
[http://dx.doi.org/10.3390/futurepharmacol2030021]
[149]
Singh N, Rai SN, Singh V, Singh MP. Molecular characterization, pathogen-host interaction pathway and in silico approaches for vaccine design against COVID-19. J Chem Neuroanat 2020; 110: 101874.2030021.
[150]
Matsuzaka Y, Yashiro R. In silico protein structure analysis for SARS-CoV-2 vaccines using deep learning. BioMedInformatics 2023; 3(1): 54-72.
[http://dx.doi.org/10.3390/biomedinformatics3010004]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy