Skip to main content

Drug Databases for Development of Therapeutics Against Coronaviruses

  • Protocol
  • First Online:
In Silico Modeling of Drugs Against Coronaviruses

Part of the book series: Methods in Pharmacology and Toxicology ((MIPT))

Abstract

Within a span of 11 months starting from December 2019, around 47.6 million people have been infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), including the number of deaths touching 1,215,601 on November 3, 2020. The number increases at an alarming rate with a possible second wave of Coronavirus Disease 2019 (COVID-19) throughout the world. A clear threat of another lockdown is looming over the social life and economy. Thus, scientists worldwide are running against the time to find small drug molecules as therapeutics and possible vaccines to relieve the world. Over the past months, computational chemistry and computer-aided drug design (CADD) have shown encouraging promises in generating multiple lead/hit compounds by employing powerful virtual screening techniques (VS) and drug repurposing of various approved and experimental drugs. The present chapter has enlisted and discussed the top 25 small molecule databases, including both synthetic as well as natural compounds. Most of the databases are freely available for research purposes, which can be strategically screened employing multiple computational techniques to discover therapeutics for COVID-19.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 279.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 379.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Fung TS, Liu DX (2019) Human coronavirus: host–pathogen interaction. Annu Rev Microbiol 73:529–557

    Article  CAS  Google Scholar 

  2. Nutho B, Mahalapbutr P, Hengphasatporn K, Pattaranggoon NC, Simanon N, Shigeta Y, Hannongbua S, Rungrotmongkol T (2020) Why are lopinavir and ritonavir effective against the newly emerged coronavirus 2019? Atomistic insights into the inhibitory mechanisms. Biochemistry 59:1769–1779

    Article  CAS  Google Scholar 

  3. Qamar MT, Alqahtani SM, Alamri MA, Chen L-L (2020) Structural basis of SARS-CoV-2 3CLpro and anti-COVID-19 drug discovery from medicinal plants. J Pharm Anal 10:313–319

    Article  Google Scholar 

  4. Wang J (2020) Fast identification of possible drug treatment of coronavirus disease-19 (COVID-19) through computational drug repurposing study. J Chem Inf Model 60:3277–3286

    Article  CAS  Google Scholar 

  5. Huynh T, Wang H, Luan B (2020) J Phys Chem Lett 11:4413–4420

    Article  CAS  Google Scholar 

  6. Ngo ST, Pham NQA, Le LT, Pham D-H, Vu VV (2020) Computational determination of potential inhibitors of SARS-CoV-2 main protease. J Chem Inf Model. https://doi.org/10.1021/acs.jcim.0c00491

  7. Sang P, Tian S-H, Meng Z-H, Yang L-Q (2020) Anti-HIV drug repurposing against SARS-CoV-2. RSC Adv 2020(10):15775–15783

    Article  Google Scholar 

  8. Havranek B, Islam SM (2020) An in silico approach for identification of novel inhibitors as potential therapeutics targeting COVID-19 main protease. J Biomol Struct Dyn:1–12. https://doi.org/10.1080/07391102.2020.1776158

  9. Fantini J, Di Scala C, Chahinian H, Yahi N (2020) Structural and molecular modeling studies reveal a new mechanism of action of chloroquine and hydroxychloroquine against SARS-CoV-2 infection. Int J Antimicrob Agents 55:105960

    Article  CAS  Google Scholar 

  10. Ojha PK, Kar S, Krishna JG (2020) Therapeutics for COVID-19: from computation to practices—where we are, where we are heading to. Mol Divers:1–35. https://doi.org/10.1007/s11030-020-10134-x

  11. Qureshi A, Thakur N, Himani T, Kumar M (2013) AVPdb: a database of experimentally validated antiviral peptides targeting medically important viruses. Nucleic Acids Res 42(D1):D1147–D1153

    Article  Google Scholar 

  12. Antiviral peptides database. http://crdd.osdd.net/servers/avpdb/. Accessed 6 Nov 2020

  13. Asinex database. http://www.asinex.com. Accessed 6 Nov 2020

  14. BindingDB database. https://www.bindingdb.org/bind/index.jsp. Accessed 6 Nov 2020

  15. CAS database. https://www.cas.org/support/documentation/cas-databases. Accessed 6 Nov 2020

  16. Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, Sajed T, Johnson D, Li C, Sayeeda Z, Assempour N, Iynkkaran I, Liu Y, Maciejewski A, Gale N, Wilson A, Chin L, Cummings R, Le D, Pon A, Knox C, Wilson M (2018) DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res 46:D1074–D1082

    Article  CAS  Google Scholar 

  17. DrugBank database. https://go.drugbank.com/covid-19#drugs. Accessed 6 Nov 2020

  18. Dark Chemical Matter database. https://www.chemdiv.com/dark-chemical-matter-library/#close. Accessed 6 Nov 2020

  19. Drugs@FDA database. https://www.accessdata.fda.gov/scripts/cder/daf/. Accessed 6 Nov 2020

  20. Drugs-lib database. http://www.druglib.com/. Accessed 6 Nov 2020

  21. Drug Repurposing Hub. https://clue.io/repurposing. Accessed 6 Nov 2020

  22. Enamine database. https://enamine.net/. Accessed 6 Nov 2020

  23. FooDB database. www.foodb.ca. Accessed 6 Nov 2020

  24. Squires RB, Noronha J, Hunt V, García-Sastre A, Macken C, Baumgarth N, Suarez D, Pickett BE, Zhang Y, Larsen CN, Ramsey A, Zhou L, Zaremba S, Kumar S, Deitrich J, Klem E, Scheuermann RH (2012) Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza Other Respi Viruses 6:404–416

    Article  Google Scholar 

  25. Influenza Research Database. http://www.fludb.org. Accessed 6 Nov 2020

  26. InterBioScreen Ltd database. https://www.ibscreen.com/. Accessed 6 Nov 2020

  27. LOPAC database. https://www.sigmaaldrich.com/life-science/cell-biology/bioactive-small-molecules/lopac1280-navigator.html. Accessed 6 Nov 2020

  28. MCULE database. https://mcule.com/database/. Accessed 6 Nov 2020

  29. MERGED AND UNIFIED DATA. https://sites.google.com/view/mud-data. Accessed 6 Nov 2020

  30. MolPort database. https://www.molport.com/. Accessed 6 Nov 2020

  31. Selleckchem database. https://www.selleckchem.com/. Accessed 6 Nov 2020

  32. COVID-19 database under Selleckchem. https://www.selleckchem.com/covid-19-related-products.html. Accessed 6 Nov 2020

  33. Scotti MT, Herrera-Acevedo C, Oliveira TB, Costa RPO, Santos SYKO, Rodrigues RP, Scotti L, Da-Costa FB (2018) SistematX, an online web-based cheminformatics tool for data management of secondary metabolites. Molecules 23:103

    Article  Google Scholar 

  34. SISTEMAT X database. https://sistematx.ufpb.br/. Accessed 6 Nov 2020

  35. Siramshetty VB, Eckert OA, Gohlke B-O, Goede A, Chen Q, Devarakonda P, Preissner S, Preissner R (2018) SuperDRUG2: a one stop resource for approved/marketed drugs. Nucleic Acids Res 46:D1137–D1143

    Article  CAS  Google Scholar 

  36. SuperDRUG2 database. http://cheminfo.charite.de/superdrug2/downloads.html. Accessed 6 Nov 2020

  37. Banerjee P, Erehman J, Gohlke BO, Wilhelm T, Preissner R (2015) Dunkel M (2015) Super natural II: a database of natural products. Nucleic Acids Res 43:D935–D939

    Article  CAS  Google Scholar 

  38. Super Natural II database. http://bioinf-applied.charite.de/supernatural_new/index.php?site=home. Accessed 6 Nov 2020

  39. Novick PA, Ortiz OF, Poelman J, Abdulhay AY, Pande VS (2013) SWEETLEAD: an in silico database of approved drugs, regulated chemicals, and herbal isolates for computer-aided drug discovery. PLoS One 8:e79568

    Article  CAS  Google Scholar 

  40. Sweetlead database. https://simtk.org/frs/?group_id=871. Accessed 6 Nov 2020

  41. Ru J, Li P, Wang J, Zhou W, Li B, Huang C, Li P, Guo Z, Tao W, Yang Y, Xu X, Li Y, Wang Y, Yang L (2014) TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J Cheminform 6:13

    Article  Google Scholar 

  42. Traditional Chinese Medicine Systems Pharmacology Database. https://tcmspw.com/tcmsp.php. Accessed 6 Nov 2020

  43. WuXi GalaXi database. https://www.biosolveit.de/CoLibri/spaces.html. Accessed 6 Nov 2020

  44. ZINC database. http://zinc.docking.org/. Accessed 6 Nov 2020

  45. Sterling T, Irwin JJ (2015) ZINC 15 – ligand discovery for everyone. J Chem Inf Model 55:2324–2337

    Article  CAS  Google Scholar 

  46. Smith M, Smith JC (2020) Repurposing therapeutics for covid-19: supercomputer-based docking to the sars-cov-2 viral spike protein and viral spike protein-human ace2 interface. Preprint. https://doi.org/10.26434/chemrxiv.11871402.v3

  47. Talluri S (2020) Virtual screening based prediction of potential drugs for COVID-19. Preprints. https://doi.org/10.20944/preprints202002.0418.v2

  48. Ton AT, Gentile F, Hsing M, Ban F, Cherkasov A (2020) Rapid identification of potential inhibitors of SARS-CoV-2 main protease by deep docking of 1.3 billion compounds. Mol Inform 39:2000028

    Article  CAS  Google Scholar 

  49. Contini A (2020) Virtual screening of an FDA approved drugs database on two COVID-19 coronavirus proteins. Preprint. https://doi.org/10.26434/chemrxiv.11847381

  50. Hosseini FS, Amanlou M (2020) Simeprevir, potential candidate to repurpose for coronavirus infection: virtual screening and molecular docking study. Preprints. https://doi.org/10.20944/preprints202002.0438.v1

  51. Wang J (2020) Fast identification of possible drug treatment of coronavirus disease -19 (COVID-19) through computational drug repurposing study. Preprint. https://doi.org/10.26434/chemrxiv.11875446.v1

  52. Chakraborti S, Sneha B, Srinivasan N (2020) Repurposing drugs against main protease of SARS-CoV-2: mechanism based insights supported by available laboratory and clinical data. ChemRxiv. https://doi.org/10.26434/chemrxiv.12057846.v2

  53. Sharma A, Tiwari V, Sowdhamini R (2020) Computational search for potential COVID-19 drugs from FDA-approved drugs and small molecules of natural origin identifies several antivirals and plant products. Preprint. https://doi.org/10.26434/chemrxiv.12091356.v1

  54. Mendoza-Martinez C, Rodriguez-Lezama A (2020) Identification of potential inhibitors of SARS-CoV2 main protease via a rapid in-silico drug repurposing approach. Preprint. https://doi.org/10.26434/chemrxiv.12085083.v1

  55. Wu C, Liu Y, Yang Y et al (2020) Analysis of therapeutic targets for SARS-CoV2 and discovery of potential drugs by computational methods. Acta Pharm Sin B 10:766–788

    Article  CAS  Google Scholar 

  56. Zhang DH, Wu KL, Zhang X, Deng SQ, Peng B (2020) In silico screening of Chinese herbal medicines with the potential to directly inhibit 2019 novel coronavirus. J Integr Med 18:152–158

    Article  Google Scholar 

  57. Beck BR, Shin B, Choi Y, Park S, Kang K (2020) Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV2) through a drug-target interaction deep learning model. Comput Struct Biotecnol 18:784–790

    Article  CAS  Google Scholar 

  58. Choudhary S, Malik YS, Tomar S (2020) Identification of SARS-CoV2 cell entry inhibitors by drug repurposing using in silico structure-based virtual screening approach. Preprint. https://doi.org/10.26434/chemrxiv.12005988.v1

  59. Chen YW, Yiu CPB, Wong KY (2020) Prediction of the SARS-CoV2 (2019-nCoV) 3C-like protease (3CLpro) structure: virtual screening reveals velpatasvir, ledipasvir, and other drug repurposing candidates. F1000Research 9:129

    Article  CAS  Google Scholar 

  60. Oliveira LD, Davi M, Oliveira TD, Mota K (2020) Comparative computational study of SARS-CoV2 receptors antagonists from already approved drugs. Preprint. https://doi.org/10.26434/chemrxiv.12044538.v2

  61. Mittal L, Kumari A, Srivastava M, Singh M, Asthana S (2020) Identification of potential molecules against COVID-19 main protease through structure-guided virtual screening approach. Preprint. https://doi.org/10.26434/chemrxiv.12086565.v2

  62. Kumar V, Roy K (2020) Development of a simple, interpretable and easily transferable QSAR model for quick screening antiviral databases in search of novel 3Clike protease (3CLpro) enzyme inhibitors against SARS-CoV diseases. SAR QSAR Environ Res 31:511–526

    Article  CAS  Google Scholar 

  63. Andrade BS, Ghosh P, Barh D, Tiwari S, Silva RJS, Soares WRDA, Melo TS, Freitas AS, González-Grande P, Palmeira LS, Alcantara LCJ, Giovanetti M, Góes-Neto A, Azevedo VADC (2020) Computational screening for potential drug candidates against the SARS-CoV-2 main protease [version 1; awaiting peer review]. F1000Research 9:514. https://doi.org/10.12688/f1000research.23829.1

    Article  CAS  Google Scholar 

  64. Onawole AT, Sulaiman KO, Kolapo TU, Akinde FO, Adegoke RO (2020) COVID-19: CADD to the rescue. Virus Res 285:198022

    Article  CAS  Google Scholar 

  65. Santibáñez-Morán MG, Edgar L-L, Prieto-Martínez FD, Norberto S-C, Medina-Franco JL (2020) Consensus virtual screening of dark chemical matter and food chemicals uncover potential inhibitors of SARS-CoV-2 main protease. Preprint. https://doi.org/10.26434/chemrxiv.12420860.v1

  66. Kapusta K, Kar S, Collins JT, Franklin LM, Kolodziejczyk W, Leszczynski J, Hill GA (2020) Protein reliability analysis and virtual screening of natural inhibitors for SARS-CoV-2 Main Protease (Mpro) through docking, molecular mechanic & dynamic, and ADMET profiling. J Biomol Struct Dyn:1–18. https://doi.org/10.1080/07391102.2020.1806930

  67. De P, Bhayye S, Kumar V, Roy K (2020) In silico modeling for quick prediction of inhibitory activity against 3CLpro enzyme in SARS CoV diseases. J Biomol Struct Dyn:1–27. https://doi.org/10.1080/07391102.2020.1821779

Download references

Acknowledgments

SK and JL are thankful to the National Science Foundation (NSF/CREST HRD-1547754 and NSF/RISE HRD-1547836) for financial support.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jerzy Leszczynski .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Science+Business Media, LLC

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Kar, S., Leszczynski, J. (2021). Drug Databases for Development of Therapeutics Against Coronaviruses. In: Roy, K. (eds) In Silico Modeling of Drugs Against Coronaviruses. Methods in Pharmacology and Toxicology. Humana, New York, NY. https://doi.org/10.1007/7653_2020_66

Download citation

  • DOI: https://doi.org/10.1007/7653_2020_66

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1365-8

  • Online ISBN: 978-1-0716-1366-5

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics