Assessing the Prediction Quality of the Anti-SARS-CoV-2 Activity Using the D3Targets-2019-nCoV Web Service

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N.S. Ionov
P.V. Pogodin
V.V. Poroikov


The D3Targets-2019-nCoV web service predicting the interaction of chemical compounds with SARS-CoV-2 virus proteins and human proteins involved in the pathogenesis of COVID-19 by structural similarity and molecular docking was evaluated. The quality of the prediction was assessed as a balanced accuracy, which was calculated based on the results of the prediction for the structures of chemical compounds from the test set we compiled. The test set consisted of 35 active and 59 inactive molecules, including compounds with the experimetnaly confirmed absence of activity against the selected targets and compounds active against SARS-CoV-2 targets, not presented in the CoViLigands database. The authors of the analyzed web service did not indicate the thresholds for the values of the similarity score and the docking scoring function, using which it would be possible to reliably divide the compounds into active and inactive with respect to target proteins. Therefore, we assessed the balanced accuracy of the predictive methods D3Targets-2019-nCoV at various thresholds for cutting off active substances from inactive ones. Using our test set it was found that the highest value of balanced accuracy (0.59) was achieved when choosing active molecules based on the results of 2D similarity assessment (cutoff threshold was 46%). Assessment of 3D similarity did not allow achieving balanced accuracy values exceeding 0.5. It is shown that using the 2Dх3D integral similarity assessment recommended by the authors, the maximum value of the balanced accuracy 0.57 was achieved at a threshold of 31%. The calculated balanced accuracy for molecular docking results does not exceed 0.51. On the case study for the tideglusib, it was shown that the values of the scoring function for two target proteins, the activity against which was confirmed in the experiment (3CLpro and GSK3B), do not differ significantly from the values of the scoring function for the remaining 44 targets were not confirmed.

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Ionov, N., Pogodin, P., & Poroikov, V. (2020). Assessing the Prediction Quality of the Anti-SARS-CoV-2 Activity Using the D3Targets-2019-nCoV Web Service. Biomedical Chemistry: Research and Methods, 3(4), e00140.


  1. Sohrabi, C., Alsafi, Z., O’Neill, N., Khan, M., Kerwan, A., Al-Jabir, A., Iosifidis, C., & Agha, R. (2020) World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19). International Journal of Surgery. DOI
  2. Coronavirus disease 2019 (COVID-19). Situation report – 134. Retrieved October 25, 2020, from
  3. Koronavirus – simptomy, priznaki, obshchaya informaciya, otvety na voprosy — Minzdrav Rossii. Retrieved October 25, 2020, from
  4. Covid19db | ReDO Project. Retrieved June 9, 2020, from
  5. Poroikov, V., Druzhilovskiy, D. Drug Repositioning: New Opportunities for Older Drugs. In: In Silico Drug Design, 1st Edition. Repurposing Techniques and Methodologies. Chapter 1. Editors: Kunal Roy. Elsevier, Academic Press, 2019, p.3-17.
  6. Liu, C., Zhou, Q., Li, Y., Garner, L. V., Watkins, S. P., Carter, L. J., ... & Albaiu, D. (2020). Research and development on therapeutic agents and vaccines for COVID-19 and related human coronavirus diseases. DOI
  7. Vremennye metodicheskie rekomendacii. Retrieved June 3, 2020, from
  8. Beigel, J. H., Tomashek, K. M., Dodd, L. E., Mehta, A. K., Zingman, B. S., Kalil, A. C., ... & de Castilla, D. L. (2020). Remdesivir for the treatment of Covid-19. New England Journal of Medicine. DOI
  9. Pan, H., Peto, R., Karim, Q. A., Alejandria, M., Restrepo, A. M. H., Garcia, C. H., ... & Reddy, S. (2020). Repurposed antiviral drugs for COVID-19; interim WHO SOLIDARITY trial results. medRxiv. DOI
  10. Shi, Y., Zhang, X., Mu, K., Peng, C., Zhu, Z., Wang, X., Yang, Y., Xu, Z., & others. (2020). D3Targets-2019-nCoV: A Web Server to Identify Potential Targets for Antivirals Against 2019-nCoV. DOI:10.1016/j.apsb.2020.04.006
  11. Open Babel. Retrieved June 12, 2020, from
  12. Getting Started with the RDKit in Python — The RDKit 2020.03.1 documentation. Retrieved June 12, 2020, from
  13. Halgren, T. A. (1996). Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. Journal of Computational Chemistry, 17(5–6), 490–519. DOI:10.1002/(SICI)1096-987X(199604)17:5/6<490::AID-JCC1>3.0.CO;2-P
  14. D3Targets-2019-nCoV (CoViLigands database). Retrieved June 26, 2020, from
  15. Zhu, Z., Wang, X., Yang, Y., Zhang, X., Mu, K., Shi, Y., Peng, C., Xu, Z., & others. (2020). D3Similarity: A ligand-based approach for predicting drug targets and for virtual screening of active compounds against COVID-19. DOI
  16. Trott, O., & Olson, A. J. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of computational chemistry, 31(2), 455-461. DOI
  17. Wang, J., Peng, C., Yu, Y., Chen, Z., Xu, Z., Cai, T., ... & Zhu, W. (2020). Exploring conformational change of adenylate kinase by replica exchange molecular dynamic simulation. Biophysical Journal, 118(5), 1009-1018. DOI
  18. Gaulton, A., Hersey, A., Nowotka Michałand Bento, A. P., Chambers, J., Mendez, D., Mutowo, P., Atkinson, F., Bellis, L. J., Cibrián-Uhalte, E., & others. (2016). The ChEMBL database in 2017. Nucleic Acids Research, 45(D1), D945--D954. DOI
  19. Stanford Coronavirus Antiviral Research Database. Retrieved July 03, 2020, from
  20. Wermuth, C.G. Similarity in drugs: reflections on analogue design. Drug Discov. Today, 2006, 11 (7-8), 348-354. DOI
  21. Filimonov, D. A., Druzhilovskiy, D. S., Lagunin, A. A., Gloriozova, T. A., Rudik, A. V., Dmitriev, A. V., ... & Poroikov, V. V. (2018). Computer-aided prediction of biological activity spectra for chemical compounds: opportunities and limitations. Biomedical Chemistry: Research and Methods, 1(1), e00004-e00004. DOI
  22. Lagunin, A., Zakharov, A., Filimonov, D., & Poroikov, V. (2011). QSAR modelling of rat acute toxicity on the basis of PASS prediction. Molecular informatics, 30(23), 241-250. DOI
  23. Druzhilovskiy, D.S., Stolbov, L.A., Savosina, P.I., Pogodin, P.V., Filimonov, D.A., Veselovsky, A.V., Tarasova, N.I., Nicklaus, M.C., Poroikov, V.V. (2020). Computational approaches to identify a hidden pharmacological potential in large chemical libraries. Supercomputing Frontiers and Innovations, 2020, Accepted.
  24. Xie, J., Liang, R., Wang, Y., Huang, J., Cao, X., Niu, B. (2020). Progress in Target Drug Molecules for Alzheimer’s Disease. Current Topics in Medicinal Chemistry, 20(1), 4–36. DOI
  25. Jin, Z., Du, X., Xu, Y., Deng, Y., Liu, M., Zhao, Y., Zhang, B., Li, X., Zhang, L., Duan, Y., & others. (2020). Structure of Mpro from COVID-19 virus and discovery of its inhibitors. bioRxiv. DOI