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image of A Mini Review on Metal Complexes as Potential Anti-SARS-CoV-2 Agents: Insights from Molecular Docking Studies

Abstract

There is an urgent need to develop effective antiviral treatments against SARS-CoV-2. Despite the availability of vaccines, drug discovery remains critical for combating emerging variants. Molecular docking studies have become a vital computational tool for identifying antiviral drugs capable of inhibiting different SARS-CoV-2 proteins. This review explores the role of metal complexes as promising viral inhibitors through molecular docking approaches. The binding abilities of several coordination complexes derived from iron, copper, palladium, and zinc ions have been evaluated against major viral proteins such as the spike glycoprotein, RNA-dependent RNA polymerase (RdRp), and the main protease (Mpro), which are responsible for viral infection. Comparative docking studies of specific metal-based compounds with conventional antiviral drugs highlight their superior binding affinities and inhibitory potential. Furthermore, ADME (Absorption, Distribution, Metabolism, and Excretion) analyses, molecular dynamics simulations, and drug-delivery strategies are discussed to assess pharmacokinetics and therapeutic viability. Overall, this review emphasizes the importance of molecular docking in the rational design of metal complexes as antiviral agents and its relevance for developing effective therapeutic strategies to combat COVID-19.

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2026-01-22
2026-01-29
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References

  1. Organization W.H. WHO director-general opening remarks at the member state briefing on the COVID-19 pandemic evaluation. 2020 Available from: https://www.who.int/dg/speeches/detail/who-director-general-opening-remarks-at-the-member-state-briefing-on-the-covid-19-pandemic-evaluation-09-july-
    [Google Scholar]
  2. Nicola M. Alsafi Z. Sohrabi C. Kerwan A. Al-Jabir A. Iosifidis C. Agha M. Agha R. The socio-economic implications of the coronavirus pandemic (COVID-19): A review. Int. J. Surg. 2020 78 185 193 10.1016/j.ijsu.2020.04.018 32305533
    [Google Scholar]
  3. Organization W.H. Weekly operational update on COVID-19. Available from: https://www.who.int/docs/default-source/coronaviruse/situation-reports/wou-28-august-approved.pdf?sfvrsn=d9e49c20_2
    [Google Scholar]
  4. Tabata S. Imai K. Kawano S. Ikeda M. Kodama T. Miyoshi K. Obinata H. Mimura S. Kodera T. Kitagaki M. Sato M. Suzuki S. Ito T. Uwabe Y. Tamura K. Clinical characteristics of COVID-19 in 104 people with SARS-CoV-2 infection on the Diamond Princess cruise ship: A retrospective analysis. Lancet Infect. Dis. 2020 20 9 1043 1050 10.1016/S1473‑3099(20)30482‑5 32539988
    [Google Scholar]
  5. Long Q.X. Tang X.J. Shi Q.L. Li Q. Deng H.J. Yuan J. Hu J.L. Xu W. Zhang Y. Lv F.J. Su K. Zhang F. Gong J. Wu B. Liu X.M. Li J.J. Qiu J.F. Chen J. Huang A.L. Clinical and immunological assessment of asymptomatic SARS-CoV-2 infections. Nat. Med. 2020 26 8 1200 1204 10.1038/s41591‑020‑0965‑6 32555424
    [Google Scholar]
  6. Wu D. Wu T. Liu Q. Yang Z. The SARS-CoV-2 outbreak: What we know. Int. J. Infect. Dis. 2020 94 44 48 10.1016/j.ijid.2020.03.004 32171952
    [Google Scholar]
  7. Cao Y. Liu X. Xiong L. Cai K. Imaging and clinical features of patients with 2019 novel coronavirus SARS‐CoV‐2: A systematic review and meta‐analysis. J. Med. Virol. 2020 92 9 1449 1459 10.1002/jmv.25822 32242947
    [Google Scholar]
  8. a Ahsan W. Javed S. Bratty M.A. Alhazmi H.A. Najmi A. Treatment of SARS-CoV-2: How far have we reached? Drug Discov. Ther. 2020 14 2 67 72 10.5582/ddt.2020.03008 32336723
    [Google Scholar]
  9. b PMID: 32336723 Richardson P. Griffin I. Tucker C. Smith D. Oechsle O. Phelan A. Rawling M. Savory E. Stebbing J. Baricitinib as potential treatment for 2019-nCoV acute respiratory disease. Lancet 2020 395 10223 e30 e31 10.1016/S0140‑6736(20)30304‑4 32032529
    [Google Scholar]
  10. Kouznetsov V.V. COVID-19 treatment: Much research and testing, but far, few magic bullets against SARS-CoV-2 coronavirus. Eur. J. Med. Chem. 2020 203 112647 10.1016/j.ejmech.2020.112647 32693298
    [Google Scholar]
  11. Paudel K.R. Singh M. De Rubis G. Kumbhar P. Mehndiratta S. Kokkinis S. El-Sherkawi T. Gupta G. Singh S.K. Malik M.Z. Mohammed Y. Oliver B.G. Disouza J. Patravale V. Hansbro P.M. Dua K. Computational and biological approaches in repurposing ribavirin for lung cancer treatment: Unveiling antitumorigenic strategies. Life Sci. 2024 352 122859 10.1016/j.lfs.2024.122859 38925223
    [Google Scholar]
  12. Chen L. Gui C. Luo X. Yang Q. Günther S. Scandella E. Drosten C. Bai D. He X. Ludewig B. Chen J. Luo H. Yang Y. Yang Y. Zou J. Thiel V. Chen K. Shen J. Shen X. Jiang H. Cinanserin is an inhibitor of the 3C-like proteinase of severe acute respiratory syndrome coronavirus and strongly reduces virus replication in vitro. J. Virol. 2005 79 11 7095 7103 10.1128/JVI.79.11.7095‑7103.2005 15890949
    [Google Scholar]
  13. Tremblay T. Bergeron C. Gagnon D. Bérubé C. Voyer N. Richard D. Giguère D. Squaramide Tethered Clindamycin, Chloroquine, and Mortiamide Hybrids: Design, synthesis, and antimalarial activity. ACS Med. Chem. Lett. 2023 14 2 217 222 10.1021/acsmedchemlett.2c00531 36793432
    [Google Scholar]
  14. Van Praet S. Preegel G. Rammal F. Sels B.F. One-pot consecutive reductive amination synthesis of pharmaceuticals: From biobased glycolaldehyde to hydroxychloroquine. ACS Sustain. Chem.& Eng. 2022 10 20 6503 6508 10.1021/acssuschemeng.2c00570
    [Google Scholar]
  15. Sparrow K.J. Shrestha R. Wood J.M. Clinch K. Hurst B.L. Wang H. Gowen B.B. Julander J.G. Tarbet E.B. McSweeney A.M. Ward V.K. Evans G.B. Harris L.D. An isomer of Galidesivir that potently inhibits influenza viruses and members of the Bunyavirales order. ACS Med. Chem. Lett. 2023 14 4 506 513 10.1021/acsmedchemlett.3c00048 37077387
    [Google Scholar]
  16. Zdun B. Reiter T. Kroutil W. Borowiecki P. Chemoenzymatic synthesis of Tenofovir. J. Org. Chem. 2023 88 15 11045 11055 10.1021/acs.joc.3c01005 37467462
    [Google Scholar]
  17. Chen Z. Jochmans D. Ku T. Paeshuyse J. Neyts J. Seley-Radtke K.L. Bicyclic and tricyclic “Expanded” nucleobase analogues of Sofosbuvir: New scaffolds for hepatitis C therapies. ACS Infect. Dis. 2015 1 8 357 366 10.1021/acsinfecdis.5b00029 27624884
    [Google Scholar]
  18. Namchuk M.N. Early Returns on small molecule therapeutics for SARS-CoV-2. ACS Infect. Dis. 2021 7 6 1298 1302 10.1021/acsinfecdis.0c00874 33417425
    [Google Scholar]
  19. Zhang H. Penninger J.M. Li Y. Zhong N. Slutsky A.S. Angiotensin-converting enzyme 2 (ACE2) as a SARS-CoV-2 receptor: molecular mechanisms and potential therapeutic target. Intensive Care Med. 2020 46 4 586 590 10.1007/s00134‑020‑05985‑9 32125455
    [Google Scholar]
  20. Gheblawi M. Wang K. Viveiros A. Nguyen Q. Zhong J.C. Turner A.J. Raizada M.K. Grant M.B. Oudit G.Y. Angiotensin-converting enzyme 2: SARS-CoV-2 receptor and regulator of the renin-angiotensin system. Circ. Res. 2020 126 10 1456 1474 10.1161/CIRCRESAHA.120.317015 32264791
    [Google Scholar]
  21. Jia H.P. Look D.C. Shi L. Hickey M. Pewe L. Netland J. Farzan M. Wohlford-Lenane C. Perlman S. McCray P.B. ACE2 receptor expression and severe acute respiratory syndrome coronavirus infection depend on differentiation of human airway epithelia. J. Virol. 2005 79 23 14614 14621 10.1128/JVI.79.23.14614‑14621.2005 16282461
    [Google Scholar]
  22. Hamming I. Timens W. Bulthuis M.L.C. Lely A.T. Navis G.J. van Goor H. Tissue distribution of ACE2 protein, the functional receptor for SARS coronavirus. A first step in understanding SARS pathogenesis. J. Pathol. 2004 203 2 631 637 10.1002/path.1570 15141377
    [Google Scholar]
  23. Gu J. Korteweg C. Pathology and pathogenesis of severe acute respiratory syndrome. Am. J. Pathol. 2007 170 4 1136 1147 10.2353/ajpath.2007.061088 17392154
    [Google Scholar]
  24. Amirfakhryan H. safari, F. Outbreak of SARS-CoV2: Pathogenesis of infection and cardiovascular involvement. Hellenic J. Cardiol. 2021 62 1 13 23 10.1016/j.hjc.2020.05.007 32522617
    [Google Scholar]
  25. Totura A.L. Baric R.S. SARS coronavirus pathogenesis: Host innate immune responses and viral antagonism of interferon. Curr. Opin. Virol. 2012 2 3 264 275 10.1016/j.coviro.2012.04.004 22572391
    [Google Scholar]
  26. Mason R.J. Pathogenesis of COVID-19 from a cell biology perspective. Eur. Respir. J. 2020 55 4 2000607 10.1183/13993003.00607‑2020 32269085
    [Google Scholar]
  27. Jain A.N. Virtual screening in lead discovery and optimization. Curr. Opin. Drug Discov. Devel. 2004 7 4 396 403 15338948
    [Google Scholar]
  28. Baildya N. Ghosh N.N. Chattopadhyay A.P. Inhibitory activity of hydroxychloroquine on COVID-19 main protease: An insight from MD-simulation studies. J. Mol. Struct. 2020 1219 128595 10.1016/j.molstruc.2020.128595 32834108
    [Google Scholar]
  29. Felsenstein S. Herbert J.A. McNamara P.S. Hedrich C.M. COVID-19: Immunology and treatment options. Clin. Immunol. 2020 215 108448 10.1016/j.clim.2020.108448 32353634
    [Google Scholar]
  30. Abd El-Lateef H.M.A. El-Dabea T. Khalaf M.M. Abu-Dief A.M. Development of metal complexes for treatment of coronaviruses. Int. J. Mol. Sci. 2022 23 12 6418 10.3390/ijms23126418 35742870
    [Google Scholar]
  31. Pal M. Musib D. Roy M. Transition metal complexes as potential tools against SARS-CoV-2: an in silico approach. New J. Chem. 2021 45 4 1924 1933 10.1039/D0NJ04578K
    [Google Scholar]
  32. Pal M. Musib D. Zade A.J. Chowdhury N. Roy M. Computational studies of selected transition metal complexes as potential drug candidates against the SARS‐CoV‐2 virus. ChemistrySelect 2021 6 29 7429 7435 10.1002/slct.202101852 34541296
    [Google Scholar]
  33. Milenković D.A. Dimić D.S. Avdović E.H. Marković Z.S. Several coumarin derivatives and their Pd(ii) complexes as potential inhibitors of the main protease of SARS-CoV-2, an in silico approach. RSC Advances 2020 10 58 35099 35108 10.1039/D0RA07062A 35515669
    [Google Scholar]
  34. Ali A. Sepay N. Afzal M. Sepay N. Alarifi A. Shahid M. Ahmad M. Molecular designing, crystal structure determination and in silico screening of copper(II) complexes bearing 8-hydroxyquinoline derivatives as anti-COVID-19. Bioorg. Chem. 2021 110 104772 10.1016/j.bioorg.2021.104772 33676041
    [Google Scholar]
  35. Almalki S.A. Bawazeer T.M. Asghar B. Alharbi A. Aljohani M.M. Khalifa M.E. El-Metwaly N. Synthesis and characterization of new thiazole-based Co(II) and Cu(II) complexes; therapeutic function of thiazole towards COVID-19 in comparing to current antivirals in treatment protocol. J. Mol. Struct. 2021 1244 130961 10.1016/j.molstruc.2021.130961 34188314
    [Google Scholar]
  36. Alharbi A. Alsoliemy A. Alzahrani S.O. Alkhamis K. Almehmadi S.J. Khalifa M.E. Zaky R. El-Metwaly N.M. Green synthesis approach for new Schiff’s-base complexes; theoretical and spectral based characterization with in-vitro and in-silico screening. J. Mol. Liq. 2022 345 117803 10.1016/j.molliq.2021.117803
    [Google Scholar]
  37. Atasever Arslan B. Kaya B. Şahin O. Baday S. Saylan C.C. Ülküseven B. The iron(III) and nickel(II) complexes with tetradentate thiosemicarbazones. Synthesis, experimental, theoretical characterization, and antiviral effect against SARS-CoV-2. J. Mol. Struct. 2021 1246 131166 10.1016/j.molstruc.2021.131166 34316082
    [Google Scholar]
  38. Hussein R.K. Elkhair H.M. Molecular docking identification for the efficacy of some zinc complexes with chloroquine and hydroxychloroquine against main protease of COVID-19. J. Mol. Struct. 2021 1231 129979 10.1016/j.molstruc.2021.129979 33518801
    [Google Scholar]
  39. Haribabu J. Srividya S. Mahendiran D. Gayathri D. Venkatramu V. Bhuvanesh N. Karvembu R. Synthesis of Palladium(II) complexes via michael addition: Antiproliferative effects through ROS-mediated mitochondrial apoptosis and docking with SARS-CoV-2. Inorg. Chem. 2020 59 23 17109 17122 10.1021/acs.inorgchem.0c02373 33231439
    [Google Scholar]
  40. Elhusseiny A.F. Sherif N.M. Soliman S.M. Ali A.E. Elsayed E.H. A promising perspective through potential antimicrobial, anti‐cancer, and antiviral activity against SARS‐CoV‐2 of silver(I) complexes: Synthesis, characterization, density functional theory calculations, and in‐silico molecular docking studies. Appl. Organomet. Chem. 2024 38 6 e7461 10.1002/aoc.7461
    [Google Scholar]
  41. Kumar S. Choudhary M. Synthesis and characterization of novel copper(ii) complexes as potential drug candidates against SARS-CoV-2 main protease. New J. Chem. 2022 46 10 4911 4926 10.1039/D2NJ00283C
    [Google Scholar]
  42. Hajibabaei F. Sanei Movafagh S. Salehzadeh S. Gable R.W. A tris(2-aminoethyl)amine-based zinc complex as a highly water-soluble drug carrier for the anti-COVID-19 drug favipiravir: a joint experimental and theoretical study. Dalton Trans. 2023 52 21 7031 7047 10.1039/D3DT00256J 36880337
    [Google Scholar]
  43. Elfiky A.A. Ribavirin, Remdesivir, Sofosbuvir, Galidesivir, and Tenofovir against SARS-CoV-2 RNA dependent RNA polymerase (RdRp): A molecular docking study. Life Sci. 2020 253 117592 10.1016/j.lfs.2020.117592 32222463
    [Google Scholar]
  44. Joshi T. Joshi T. Sharma P. Mathpal S. Pundir H. Bhatt V. Chandra S. In silico screening of natural compounds against COVID-19 by targeting Mpro and ACE2 using molecular docking. Eur. Rev. Med. Pharmacol. Sci. 2020 24 8 4529 4536 10.26355/eurrev_202004_21036 32373991
    [Google Scholar]
  45. Kong R. Yang G. Xue R. Liu M. Wang F. Hu J. Guo X. Chang S. COVID-19 Docking Server: a meta server for docking small molecules, peptides and antibodies against potential targets of COVID-19. Bioinformatics 2020 36 20 5109 5111 10.1093/bioinformatics/btaa645 32692801
    [Google Scholar]
  46. Bucinsky L. Bortňák D. Gall M. Matúška J. Milata V. Pitoňák M. Štekláč M. Végh D. Zajaček D. Machine learning prediction of 3CL SARS-CoV-2 docking scores. Comput. Biol. Chem. 2022 98 107656 10.1016/j.compbiolchem.2022.107656 35288359
    [Google Scholar]
  47. Elmezayen A.D. Al-Obaidi A. Şahin A.T. Yelekçi K. Drug repurposing for coronavirus (COVID-19): in silico screening of known drugs against coronavirus 3CL hydrolase and protease enzymes. J. Biomol. Struct. Dyn. 2021 39 8 2980 2992 10.1080/07391102.2020.1758791 32306862
    [Google Scholar]
  48. Tejera E. Munteanu C.R. López-Cortés A. Cabrera-Andrade A. Pérez-Castillo Y. Drugs repurposing using QSAR, Docking and molecular dynamics for possible inhibitors of the SARS-CoV-2 Mpro Protease. Molecules 2020 25 21 5172 10.3390/molecules25215172 33172092
    [Google Scholar]
  49. Smith M. Smith J. Repurposing therapeutics for COVID-19: Supercomputer-based docking to the SARS-CoV-2 viral spike protein and viral spike protein-human ACE2 interface. Chemrxiv 2020 10.26434/chemrxiv.11871402.v4
    [Google Scholar]
  50. Štekláč M. Zajaček D. Bučinský L. 3CLpro and PLpro affinity, a docking study to fight COVID19 based on 900 compounds from PubChem and literature. Are there new drugs to be found? J. Mol. Struct. 2021 1245 130968 10.1016/j.molstruc.2021.130968 34219808
    [Google Scholar]
  51. Guedes I.A. Costa L.S.C. dos Santos K.B. Karl A.L.M. Rocha G.K. Teixeira I.M. Galheigo M.M. Medeiros V. Krempser E. Custódio F.L. Barbosa H.J.C. Nicolás M.F. Dardenne L.E. Drug design and repurposing with DockThor-VS web server focusing on SARS-CoV-2 therapeutic targets and their non-synonym variants. Sci. Rep. 2021 11 1 5543 10.1038/s41598‑021‑84700‑0 33692377
    [Google Scholar]
  52. Batra R. Chan H. Kamath G. Ramprasad R. Cherukara M.J. Sankaranarayanan S.K.R.S. screening of therapeutic agents for COVID-19 using machine learning and ensemble docking studies. J. Phys. Chem. Lett. 2020 11 17 7058 7065 10.1021/acs.jpclett.0c02278 32787328
    [Google Scholar]
  53. El-Behery H. Attia A.F. El-Fishawy N. Torkey H. Efficient machine learning model for predicting drug-target interactions with case study for COVID-19. Comput. Biol. Chem. 2021 93 107536 10.1016/j.compbiolchem.2021.107536 34271420
    [Google Scholar]
  54. Schütt K.T. Kessel P. Gastegger M. Nicoli K.A. Tkatchenko A. Müller K.R. SchNetPack: A deep learning toolbox for atomistic systems. J. Chem. Theory Comput. 2019 15 1 448 455 10.1021/acs.jctc.8b00908 30481453
    [Google Scholar]
  55. Abadi M. Agarwal A. Barham P. Brevdo E. Chen Z. Citro C. Corrado G.S. Davis A. Dean J. Devin M. Ghemawat S. Goodfellow I. Harp A. Irving G. Isard M. Jia Y. Jozefowicz R. Kaiser L. Kudlur M. Levenberg J. Man’e D. Monga R. Moore S. Murray D. Olah C. Schuster M. Shlens J. Steiner B. Sutskever I. Talwar K. Tucker P. Vanhoucke V. Vasudevan V. Vi’egas F. Vinyals O. Warden P. Wattenberg M. Wicke M. Yu Y. Zheng X. TensorFlow: LargeScale machine learning on heterogeneous systems, software. ArXiv 2016
    [Google Scholar]
  56. Chen T. Guestrin C. XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16 New York, NY, USA 2016 10.1145/2939672.2939785
    [Google Scholar]
  57. Ton A.T. Gentile F. Hsing M. Ban F. Cherkasov A. Rapid identification of potential inhibitors of SARS‐CoV‐2 main protease by deep docking of 1.3 billion compounds. Mol. Inform. 2020 39 8 2000028 10.1002/minf.202000028 32162456
    [Google Scholar]
  58. Joshi T. Joshi T. Pundir H. Sharma P. Mathpal S. Chandra S. Predictive modeling by deep learning, virtual screening and molecular dynamics study of natural compounds against SARS-CoV-2 main protease. J. Biomol. Struct. Dyn. 2021 39 17 6728 6746 10.1080/07391102.2020.1802341 32752947
    [Google Scholar]
  59. Acharya A. Agarwal R. Baker M.B. Baudry J. Bhowmik D. Boehm S. Byler K.G. Chen S.Y. Coates L. Cooper C.J. Demerdash O. Daidone I. Eblen J.D. Ellingson S. Forli S. Glaser J. Gumbart J.C. Gunnels J. Hernandez O. Irle S. Kneller D.W. Kovalevsky A. Larkin J. Lawrence T.J. LeGrand S. Liu S.H. Mitchell J.C. Park G. Parks J.M. Pavlova A. Petridis L. Poole D. Pouchard L. Ramanathan A. Rogers D.M. Santos-Martins D. Scheinberg A. Sedova A. Shen Y. Smith J.C. Smith M.D. Soto C. Tsaris A. Thavappiragasam M. Tillack A.F. Vermaas J.V. Vuong V.Q. Yin J. Yoo S. Zahran M. Zanetti-Polzi L. Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to COVID-19. J. Chem. Inf. Model. 2020 60 12 5832 5852 10.1021/acs.jcim.0c01010 33326239
    [Google Scholar]
  60. Sanchez-Lengeling B. Aspuru-Guzik A. Inverse molecular design using machine learning: Generative models for matter engineering. Science 2018 361 6400 360 365 10.1126/science.aat2663 30049875
    [Google Scholar]
  61. Sinsulpsiri S. Nishii Y. Xu-Xu Q.F. Miura M. Wilasluck P. Salamteh K. Deetanya P. Wangkanont K. Suroengrit A. Boonyasuppayakorn S. Duan L. Harada R. Hengphasatporn K. Shigeta Y. Shi L. Maitarad P. Rungrotmongkol T. Unveiling the antiviral inhibitory activity of ebselen and ebsulfur derivatives on SARS-CoV-2 using machine learning-based QSAR, LB-PaCS-MD, and experimental assay. Sci. Rep. 2025 15 1 6956 10.1038/s41598‑025‑91235‑1 40011571
    [Google Scholar]
  62. Cai J. Li Y. Liu B. Wu Z. Zhu S. Chen Q. Lei Q. Hou H. Guo Z. Jiang H. Guo S. Wang F. Huang S. Zhu S. Fan X. Tao S. Developing deep LSTMs with later temporal attention for predicting COVID-19 severity, clinical outcome, and antibody level by screening serological indicators over time. IEEE J. Biomed. Health Inform. 2024 28 7 4204 4215 10.1109/JBHI.2024.3384333 38564357
    [Google Scholar]
  63. Qi Y. Cai J. Chen R. AO-TransUNet: A multi-attention optimization network for COVID-19 and medical image segmentation. Digit. Signal Process. 2025 164 105264 10.1016/j.dsp.2025.105264
    [Google Scholar]
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  • Article Type:
    Review Article
Keywords: molecular docking studies ; protein ; SARS-CoV-2 ; Binding Energy ; COVID-19 ; metal Complexes
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