High-Throughput Computational Discovery of Anti-Coronavirus Agents in the COVID-19 Era: Crucial Insights for Combating Emerging Biogenic Threats

Authors

DOI:

https://doi.org/10.14529/jsfi260107

Keywords:

COVID-19, JEDI COVID-19 challenge, anti-coronavirus agents, in silico screening, future biogenic threats, effective response

Abstract

In May 2020, the Joint European Disruptive Initiative (JEDI) launched the “Billion Molecules against COVID 19” challenge – an extensive open science effort aimed at identifying small molecule inhibitors of SARS-CoV-2 and related human receptors. Our research group joined this initiative among 130 international teams, focusing on the in silico screening for potential anti coronavirus agents that target three viral proteins and one human receptor. The screening campaign covered more than one billion synthetically accessible structures, including approved pharmaceuticals. By July 17, 2020, our team submitted a subset of 10000 prioritized compounds to the organizers for expert evaluation. The results from our selection, together with those from 19 other participating teams, contributed to a pool of approximately 1000 molecules selected for chemical synthesis and bioactivity testing. In total, 878 compounds were successfully synthesized and evaluated for inhibitory activity against various SARS-CoV-2 targets as well as the human serine protease TMPRSS2. Ultimately, 27 compounds – including one proposed by our group – demonstrated measurable anti coronavirus activity. The collective outcomes of these collaborative efforts were reported in the “Molecular Informatics” journal in 2024. In the present study, we summarize our participation in the JEDI challenge and discuss broader methodological and organizational considerations critical for improving the efficiency of rapid scientific responses to future emerging biological threats.

References

Aldrich Market Select (AMS). https://www.sigmaaldrich.com/chemistry/chemistry-services/aldrich-market-select.html, accessed: 2020-07-15

Antiviral CAS dataset. https://www.cas.org/covid-19-antiviral-compounds-dataset, accessed: 2020-07-15

Anusevicius, K., Mickevicius, V., Stasevych, M., et al.: Design, synthesis, in vitro antimicrobial activity evaluation and computational studies of new N-(4-iodophenyl)-β-alanine derivatives. Res. Chem. Intermed. 41(10), 7517–7540 (2014). https://doi.org/10.1007/s11164-014-1841-0

AutoDock Vina. http://vina.scripps.edu/, accessed: 2020-07-15

Baker, J.D., Uhrich, R.L., Kraemer, G.C., et al.: A drug repurposing screen identifies hepatitis C antivirals as inhibitors of the SARS-CoV2 main protease. PLoS One 16(2), e0245962 (2021). https://doi.org/10.1371/journal.pone.0245962

Bender, A.: How similar are those molecules after all? Use two descriptors and you will have three different answers. Expert Opin. Drug Discov. 5(12), 1141–1151 (2010). https://doi.org/10.1517/17460441.2010.517832

Betow, J.Y., Turon, G., Metuge, C.S., et al.: The chemical space spanned by manually curated datasets of natural and synthetic compounds with activities against SARS-CoV-2. Mol. Inform. 44(1), e202400293 (2025). https://doi.org/10.1002/minf.202400293

Bobrowski, T., Melo-Filho, C.C., Korn, D., et al.: Learning from history: do not flatten the curve of antiviral research! Drug Discov. Today 25(9), 1604–1613 (2020). https://doi.org/10.1016/j.drudis.2020.07.008

Bojkova, D., McGreig, J.E., McLaughlin, K.M., et al.: SARS-CoV-2 and SARS-CoV differ in their cell tropism and drug sensitivity profiles. bioRxiv preprint (2020). https://doi.org/10.1101/2020.04.03.024257

Burov, Yu.V., Poroikov, V.V., Korolchenko, L.V.: National system for registration and biological testing of chemical compounds: facilities for new drugs search. Bulletin of the National Center for Biologically Active Compounds 1, 4–25 (1990).

ChEBI. https://www.ebi.ac.uk/chebi/, accessed: 2020-07-15

ChEMBL database. https://www.ebi.ac.uk/chembl, accessed: 2026-02-15

Chou, C.Y., Chien, C.H., Han, Y.S., et al.: Thiopurine analogues inhibit papain-like protease of severe acute respiratory syndrome coronavirus. Biochem. Pharmacol. 75(8), 1601–1609 (2008). https://doi.org/10.1016/j.bcp.2008.01.005

Collins, F., Adam, S., Colvis, C., et al.: The NIH-led research response to COVID-19. Science 379(6631), 441–444 (2023). https://doi.org/10.1126/science.adf5167

Cortellis Drug Discovery Intelligence. https://www.cortellis.com/drugdiscovery/, accessed: 2020-07-15

Dai, W., Zhang, B., Jiang, X.M., et al.: Structure-based design of antiviral drug candidates targeting the SARS-CoV-2 main protease. Science 368(6497), 1331–1335 (2020). https://doi.org/10.1126/science.abb4489

De Meyer, S., Bojkova, D., Cinatl, J., et al.: Lack of antiviral activity of darunavir against SARS-CoV-2. Int. J. Infect. Dis. 97, 7–10 (2020). https://doi.org/10.1016/j.ijid.2020.05.085

Dimova, D., Bajorath, J.: Advances in activity cliff research. Mol. Inform. 35(5), 181–191 (2016). https://doi.org/10.1002/minf.201600023

Do, T.N.D., Abdelnabi, R., Boda, B., et al.: Remdesivir: The triple combination of REmdesivir (GS-441524), molnupiravir and ribavirin is highly efficient in inhibiting coronavirus replication in human nasal airway epithelial cell cultures and in a hamster infection model. Antiviral Res. 231, 105994 (2024). https://doi.org/10.1016/j.antiviral.2024

Druzhilovskiy, D.S., Stolbov, L.A., Savosina, P.I., et al.: Computational approaches to identify a hidden pharmacological potential in large chemical libraries. Supercomputing Frontiers and Innovations 7(3), 57–76 (2020). https://doi.org/10.14529/jsfi200306

Eastman, R.T., Roth, J.S., Brimacombe, K.R., et al.: Remdesivir: A review of its discovery and development leading to emergency use authorization for treatment of COVID-19. ACS Cent. Sci. 6(5), 672–683 (2020). https://doi.org/10.1021/acscentsci.0c00489

Ellinger, B., Bojkova, D., Zaliani, A., et al.: A SARS-CoV-2 cytopathicity dataset generated by high-content screening of a large drug repurposing collection. Sci. Data 8(1), 70 (2021). https://doi.org/10.1038/s41597-021-00848-4

ENAMINE in-stock compounds. https://enamine.net/, accessed: 2026-02-15

Filimonov, D., Poroikov, V., Borodina, Yu., Gloriozova, T.: Chemical similarity assessment through multilevel neighborhoods of atoms: definition and comparison with the other descriptors. J. Chem. Inf. Comput. Sci. 39(4), 666–670 (1999). https://doi.org/10.1021/ci980335o

Filimonov, D.A., Zakharov, A.V., Lagunin, A.A., Poroikov, V.V.: QNA-based ‘Star Track’ QSAR approach. SAR QSAR Environ. Res. 20(7–8), 679–709 (2009). https://doi.org/10.1080/10629360903438370

Filimonov, D.A., Lagunin, A.A., Gloriozova, T.A., et al.: Prediction of the biological activity spectra of organic compounds using the PASS online web resource. Chem. Heterocycl. Comp. 50(3), 444–457 (2016). https://doi.org/10.1007/s10593-014-1496-1

Filimonov, D.A., Druzhilovskiy, D.S., Lagunin, A.A., et al.: Computer-aided prediction of biological activity spectra for chemical compounds: opportunities and limitations. Biomedical Chemistry: Research and Methods 1(1), e00004 (2018). https://doi.org/10.18097/bmcrm00004

Filimonov, D.A., Akimov, D.V., Poroikov, V.V.: Method of self-consistent regression in analysis of quantitative structure-property relationships of chemical compounds. Pharm. Chem. J. 38(1), 21–24 (2004). https://doi.org/10.1023/B:PHAC.0000027639.17115.5d

Fourches, D., Muratov, E., Tropsha, A.: Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research. J. Chem. Inf. Model 50(7), 1189–1204 (2010). https://doi.org/10.1021/ci100176x

Fourches, D., Muratov, E., Tropsha, A.: Curation of chemogenomics data. Nat. Chem. Biol. 11(8), 535 (2015). https://doi.org/10.1038/nchembio.1881

Fourches, D., Muratov, E., Tropsha, A.: Trust, but Verify II: A Practical Guide to Chemogenomics Data Curation. J. Chem. Inf. Model 56(7), 1243–1252 (2016). https://doi.org/10.1021/acs.jcim.6b00129

Geronikaki, A., Druzhilovskiy, D., Zakharov, A., Poroikov, V.: Computer-aided predictions for medicinal chemistry via Internet. SAR QSAR Environ. Res. 19(1-2), 27–38 (2008). https://doi.org/10.1080/10629360701843649

GIAID. https://gisaid.org/, accessed: 2026-02-15

Ghosh, A.K., Takayama, J., Aubin, Y., et al.: Structure-based design, synthesis, and biological evaluation of a series of novel and reversible inhibitors for the severe acute respiratory syndrome-coronavirus papain-like protease. J. Med. Chem. 52(16), 5228–5340 (2009). https://doi.org/10.1021/jm900611t

Halford, B.: The path to Paxlovid. ACS Cent. Sci. 8(4), 405–407 (2022). https://doi.org/10.1021/acscentsci.2c00369

Hodgson, C.L., Broadley, T.: Long COVID – unravelling a complex condition. Lancet Respir. Med. 11(8), 667–668 (2023). https://doi.org/10.1016/S2213-2600(23)00232-1

Huang, L., Chen, Y., Xiao, J., et al.: Progress in the research and development of anti-COVID-19 drugs. Front. Public Health 8, 365 (2020). https://doi.org/10.3389/fpubh.2020.00365

IBS Natural Compounds Set. https://www.ibscreen.com/, accessed: 2020-07-15

Jeon, S., Ko, M., Lee, J., et al.: Identification of Antiviral Drug Candidates against SARS-CoV-2 from FDA-Approved Drugs. Antimicrob. Agents Chemother. 64(7), e00819–20 (2020). https://doi.org/10.1128/AAC.00819-20

JEDI billion molecules against COVID-19 grand challenge. https://www.jedi.foundation/covid19challenge, accessed: 2026-02-15

Jin, Z., Du, X., Xu, Y., et al.: Structure of Mpro from SARS-CoV-2 and discovery of its inhibitors. Nature 582(7811), 289–293 (2020). https://doi.org/10.1038/s41586-020-2223-y

Kakhar Umar, A., Zothantluanga, J.H., Luckanagul, J.A., et al.: Structure-based computational screening of 470 natural quercetin derivatives for identification of SARS-CoV-2 Mpro inhibitor. PeerJ. 11, e14915 (2023). https://doi.org/10.7717/peerj

Klimenko, K., Marcou, G., Horvath, D., Varnek, A.: Chemical space mapping and structure-activity analysis of the ChEMBL antiviral compound set. J. Chem. Inf. Model 56(8), 1438–1454 (2016). https://doi.org/10.1021/acs.jcim.6b00192

Kneller, D.W., Galanie, S., Phillips, G., et al.: Malleability of the SARS-CoV-2 3CL Mpro active-site cavity facilitates binding of clinical antivirals. Structure 28(12), 1313–1320 (2020). https://doi.org/10.1016/j.str.2020.10.007

Kubinyi, H.: Chemical similarity and biological activities. J. Braz. Chem. Soc. 13(6), 717–726 (2002). https://doi.org/10.1590/S0103-50532002000600002

Kuzikov, M., Costanzi, E., Reinshagen, J., et al.: Identification of inhibitors of SARS-CoV-2 3CL-pro enzymatic activity using a small molecule in vitro repurposing screen. ACS Pharmacol. Transl. Sci. 4(3), 1096–1110 (2021). https://doi.org/10.1021/acsptsci.0c00216

Li, Y., Tang, C., Zhang, Y., et al.: Diversity and independent evolutionary profiling of rodent-borne viruses in Hainan, a tropical island of China. Virol. Sin. 8(5), 651–662 (2023). https://doi.org/10.1016/j.virs.2023.08.003

Listings of WHOs response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, accessed: 2026-02-15

Ma, C., Hurst, B., Hu, Y., et al.: Boceprevir, GC-376, and calpain inhibitors II, XII inhibit SARS-CoV-2 viral replication by targeting the viral main protease. Cell Res. 30(8), 678–692 (2020). https://doi.org/10.1038/s41422-020-0356-z

Mansouri, K., Kleinstreuer, N., Abdelaziz, A.M., et al.: CoMPARA: Collaborative modeling project for androgen receptor activity. Environ Health Perspect. 128(2), 27002 (2020). https://doi.org/10.1289/EHP5580

Martinez, M.A.: Lack of effectiveness of repurposed drugs for COVID-19 treatment. Front. Immunol. 12, 635371 (2021). https://doi.org/10.3389/fimmu.2021

Murtazalieva, K.A., Druzhilovskiy, D.S., Goel, R.K., et al.: How good are publicly available web services that predict bioactivity profiles for drug repurposing? SAR QSAR Environ. Res. 28 (10), 843–862 (2017). https://doi.org/10.1080/1062936X.2017.1399448

Muratov, E.N., Bajorath, J., Sheridan, R.P., et al.: QSAR Without Borders. Chem. Soc. Rev. 49(11), 3525–3564 (2020). https://doi.org/10.1039/d0cs00098a

Natural Product Activity and Species Source (NPASS). https://bidd2.nus.edu.sg/NPASS/, accessed: 2020-07-15

Natural Products from Northern African Sources (NANPDB). https://african-compounds.org/nanpdb/, accessed: 2020-07-15

Nuclei of Bioassays, Ecophysiology and Biosynthesis of Natural Products Database (NuBBE). https://nubbe.iq.unesp.br/portal/nubbedb.html, accessed: 2020-07-15

Poroikov, V.V., Filimonov, D.A., Gloriozova, T.A., et al.: Computer-aided prediction of biological activity spectra for organic compounds: the possibilities and limitations. Russ. Chem. Bull. 68(12), 2143–2154 (2019). https://doi.org/10.1007/s11172-019-2683

Poroikov, V.V., Filimonov, D.A., Borodina, Yu.V., et al.: Robustness of biological activity spectra predicting by computer program PASS for non-congeneric sets of chemical compounds. J. Chem. Inf. Comput. Sci. 40(6), 1349-01355 (2000). https://doi.org/10.1021/ci000383k

PostERA activity data. https://postera.ai/covid/activity_data, accessed: 2020-07-15

Protein Data Bank. https://www.rcsb.org/, accessed: 2026-02-15

Pruijssers, A.J., George, A.S., Schäfer, A., et al.: Remdesivir potently inhibits SARS-CoV-2 in human lung cells and chimeric SARS-CoV expressing the SARS-CoV-2 RNA polymerase in mice. Cell Rep. 32(3), 107940 (2020). https://doi.org/10.1016/j.celrep.2020.107940

Ratia, K., Pegan, S., Takayama, J., et al.: A noncovalent class of papain-like protease/deubiquitinase inhibitors blocks SARS virus replication. PNAS 105(42), 16119–16124 (2008). https://doi.org/10.1073/pnas.0805240105

Riva, L., Yuan, S., Yin, X., et al.: A large scale drug repositioning survey for SARS-CoV-2 antivirals. Nature 586(7827), 113–119 (2020). https://doi.org/10.1038/s41586-020-2577-1

Sachs, J.D., Karim, S.S.A., Aknin, L., et al.: The Lancet Commission on lessons for the future from the COVID-19 pandemic. Lancet 400(10359), 1224–1280 (2022). https://doi.org/10.1016/S0140-6736(22)01585-9

SAVI. https://cactus.nci.nih.gov/download/savi_download/, accessed: 2020-07-15

Sheahan, T.P., Sims, A.C., Zhou, S., et al.: An orally bioavailable broad-spectrum antiviral inhibits SARS-CoV-2 and multiple endemic, epidemic and bat coronavirus. bioRxiv preprint (2020). https://doi.org/10.1101/2020.03.19.997890

Schimunek, J., Seidl, P., Elez, K., et al.: A community effort in SARS-CoV-2 drug discovery. Mol. Inform. 43, e202300262 (2024). https://doi.org/10.1002/minf.202300262

Sielaff, F., Böttcher-Friebertshäuser, E., Meyer, D., et al.: Development of substrate analogue inhibitors for the human airway trypsin-like protease HAT. Bioorg. Med. Chem. Lett. 21(16), 4860–4864 (2011). https://doi.org/10.1016/j.bmcl.2011.06.03

Smith, C.I.E., Bergman, P., Hagey, D.W.: Estimating the number of diseases – the concept of rare, ultra-rare, and hyper-rare. iScience 25(8), 104698 (2022). https://doi.org/10.1016/j.isci.2022.104698

Stanford Coronavirus Antiviral Research Database. https://covdb.stanford.edu/, accessed: 2020-07-15

Stolbov, L.A., Druzhilovskiy, D.S., Filimonov, D.A., et al.: (Q)SAR models of HIV-1 proteins inhibition by drug-like compounds. Molecules 25(1), 87 (2019). https://doi.org/10.3390/molecules25010087

SWEETLEAD. https://simtk.org/projects/sweetlead, accessed: 2020-07-15

SYBYL-X Suite. https://www.g6g-softwaredirectory.com/bio/proteomics/structure-modeling/20710-Tripos-SYBYL-X-Suite.php, accessed: 2020-07-15

Touret, F., Gilles, M., Barral, K., et al.: In vitro screening of a FDA approved chemical library reveals potential inhibitors of SARS-CoV-2 replication. Sci. Rep. 10(1), 13093 (2020). https://doi.org/10.1038/s41598-020-70143-6

Universal Natural Products Database (UNPD). http://pkuxxj.pku.edu.cn, accessed: 2020-07-15

UCSF Dock. http://dock.compbio.ucsf.edu/, accessed: 2020-07-15

Wang, M., Cao, R., Zhang, L., et al.: Remdesivir and chloroquine effectively inhibit the recently emerged novel coronavirus (2019-nCoV) in vitro. Cell Res. 30(3), 269–271 (2020). https://doi.org/10.1038/s41422-020-0282-0

Wermuth, C.G.: Similarity in drugs: reflections on analogue design. Drug Discov. Today 11(7-8), 348–354 (2006). https://doi.org/10.1016/j.drudis.2006.02.006

World Wide Approved Drugs (WWAD) database. https://way2drug.com/wwad/, accessed: 2026-02-15

Yuan, C., Goonetilleke, E.C., Unarta, I.C., Huang, X.: Incorporation efficiency and inhibition mechanism of 2’-substituted nucleotide analogs against SARS-CoV-2 RNA-dependent RNA polymerase. Phys. Chem. Chem. Phys. 23(36), 20117–20128 (2021). https://doi.org/10.1039/d1cp03049c

Zakharov, A.V., Peach, M.L., Sitzmann, M., Nicklaus, M.C.: A new approach to radial basis function approximation and its application to QSAR. J. Chem. Inf. Model 54(3), 713-719 (2014). https://doi.org/10.1021/ci400704f

ZINC. https://zinc.docking.org/, accessed: 2020-07-15

Downloads

Published

2026-04-27

How to Cite

Druzhilovskiy, D. S., Filimonov, D. A., Pogodin, P. V., Rudik, A. V., Stolbov, L. A., Tarasova, O. A., Veselovsky, A. V., & Poroikov, V. V. (2026). High-Throughput Computational Discovery of Anti-Coronavirus Agents in the COVID-19 Era: Crucial Insights for Combating Emerging Biogenic Threats. Supercomputing Frontiers and Innovations, 13(1), 86–105. https://doi.org/10.14529/jsfi260107

Most read articles by the same author(s)