Skip to main content
SpringerLink
Log in

Generative Autoencoders for Designing Novel Small-Molecule Compounds as Potential SARS-CoV-2 Main Protease Inhibitors

  • Conference paper
  • First Online:
Pattern Recognition and Information Processing (PRIP 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1562))

  • 261 Accesses

Abstract

Two generative autoencoder models for designing novel drug-like compounds able to block the catalytic site of the SARS-CoV-2 main protease (MPro) critical for mediating viral replication and transcription were developed using deep learning methods. To do this, the following steps were performed: (i) architectures of two neural networks were constructed; (ii) a virtual compound library of potential anti-SARS-CoV-2 MPro agents for training two neural networks was formed; (iii) molecular docking of all compounds from this library with MPro was made and calculations of the values of binding free energy were carried out; (iv) two neural networks were trained followed by estimation of the learning outcomes and work of two autoencoders involving several generation modes. Validation of autoencoders and their comparison revealed the best combination of the neural network architecture with the generation mode, which allows one to generate good chemical scaffold for the design of novel antiviral drugs with suitable pharmaceutical properties.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Similar content being viewed by others

References

  1. Iqbal, T., Qureshi, S.: The survey: text generation models in deep learning. J. King Saud Univ. Comput. Inf. Sci. (2020). https://doi.org/10.1016/j.jksuci.2020.04.001

  2. Sorin, V., Barash, Y., Konen, E., Klang, E.: Creating artificial images for radiology applications using generative adversarial networks (GANs) – A systematic review. Acad. Radiol. 27(8), 1175–1185 (2020). https://doi.org/10.1016/j.acra.2019.12.024

    Article  Google Scholar 

  3. Kim, S., et al.: PubChem in 2021: new data content and improved web interfaces. Nucleic Acids Res. 49(D1), D1388–D1395 (2021). https://doi.org/10.1093/nar/gkaa971

    Article  Google Scholar 

  4. PubChem Homepage. https://pubchem.ncbi.nlm.nih.gov/. Accessed 10 Dec 2021

  5. Chen, Y., Liu, Q., Guo, D.: Coronaviruses: genome structure, replication, and pathogenesis. J. Med. Virol. 92(4), 418–423 (2020). https://doi.org/10.1002/jmv.25681

    Article  Google Scholar 

  6. Anand, K., Palm, G.J., Mesters, J.R., Siddell, S.G., Ziebuhr, J., Hilgenfeld, R.: Structure of coronavirus main proteinase reveals combination of a chymotrypsin fold with an extra α-helical domain. EMBO J. 21(13), 3213–3224 (2002). https://doi.org/10.1093/emboj/cdf327

    Article  Google Scholar 

  7. Yang, H., et al.: The crystal structures of severe acute respiratory syndrome virus main protease and its complex with an inhibitor. PNAS USA 100(23), 13190–13195 (2003). https://doi.org/10.1073/pnas.1835675100

    Article  Google Scholar 

  8. Hegyi, A., Ziebuhr, J.: Conservation of substrate specificities among coronavirus main proteases. J. Gen. Virol. 83(3), 595–599 (2002). https://doi.org/10.1099/0022-1317-83-3-595

    Article  Google Scholar 

  9. Pillaiyar, T., Manickam, M., Namasivayam, V., Hayashi, Y., Jung, S.H.: An overview of severe acute respiratory syndrome-coronavirus (SARS-CoV) 3CL protease inhibitors: peptidomimetics and small molecule chemotherapy. J. Med. Chem. 59(14), 6595–6628 (2016). https://doi.org/10.1021/acs.jmedchem.5b01461

    Article  Google Scholar 

  10. Yan, F., Gao, F.: An overview of potential inhibitors targeting non-structural proteins 3 (PLpro and Mac1) and 5 (3CLpro/Mpro) of SARS-CoV-2. Comp. Struct. Biotechnol. J. 19, 4868–4883 (2021). https://doi.org/10.1016/j.csbj.2021.08.036

    Article  Google Scholar 

  11. Ullrich, S., Nitsche, C.: The SARS-CoV-2 main protease as drug target. Bioorganic Med. Chem. Lett. 30(17), 127377 (2020). https://doi.org/10.1016/j.bmcl.2020.127377

    Article  Google Scholar 

  12. Forster, P., Forster, L., Renfrew, C., Forster, M.: Phylogenetic network analysis of SARS-CoV-2 genomes. PNAS USA 117(17), 9241–9243 (2020). https://doi.org/10.1073/pnas.2004999117

    Article  Google Scholar 

  13. Pachetti, M., et al.: Emerging SARS-CoV-2 mutation hot spots include a novel RNA-dependent-RNA polymerase variant. J. Transl. Med. 18, 179 (2020). https://doi.org/10.1186/s12967-020-02344-6

    Article  Google Scholar 

  14. Yao, H., et al.: Patient-derived SARS-CoV-2 mutations impact viral replication dynamics and infectivity in vitro and with clinical implications in vivo. Cell Discov. 6, 76 (2020). https://doi.org/10.1038/s41421-020-00226-1

    Article  Google Scholar 

  15. Khailany, R.A., Safdar, M., Ozaslan, M.: Genomic characterization of a novel SARS-CoV-2. Gene Rep. 19, 100682 (2020). https://doi.org/10.1016/j.genrep.2020.100682

    Article  Google Scholar 

  16. Weininger, D.: SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Model. 28(1), 31–36 (1988). https://doi.org/10.1021/ci00057a005

    Article  Google Scholar 

  17. Andrianov, A.M., Nikolaev, G.I., Shuldov, N.A., Bosko, I.P., Anischenko, A.I., Tuzikov, A.V.: Application of deep learning and molecular modeling to identify small drug-like compounds as potential HIV-1 entry inhibitors. J. Biomol. Struct. Dyn. 1–19 (2021). https://doi.org/10.1080/07391102.2021.1905559

  18. Pharmit Homepage. http://pharmit.csb.pitt.edu. Accessed 10 Dec 2021

  19. Sunseri, J., Koes, D.R.: Pharmit: interactive exploration of chemical space. Nucleic Acids Res. 44(W1), W442–W448 (2016). https://doi.org/10.1093/nar/gkw287

    Article  Google Scholar 

  20. Schneidman-Duhovny, D., Dror, O., Inbar, Y., Nussinov, R., Wolfson, H.J.: Deterministic pharmacophore detection via multiple flexible alignment of drug-like molecules. J. Comput. Biol. 15(7), 737–754 (2008). https://doi.org/10.1089/cmb.2007.0130

    Article  MathSciNet  Google Scholar 

  21. PubChemPy Homepage. https://pubchempy.readthedocs.io/. Accessed 10 Dec 2021

  22. Python Homepage. https://www.python.org/. Accessed 10 Dec 2021

  23. RCSB PDB Homepage. https://www.rcsb.org/pdb/. Accessed 10 Dec 2021

  24. RDKit Homepage. http://www.rdkit.org/. Accessed 10 Dec 2021

  25. Halgren, T.A.: Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. J. Comput. Chem. 17(5–6), 490–519 (1996). https://doi.org/10.1002/(SICI)1096-987X(199604)17:5/6<490::AID-JCC1>3.0.CO;2-P

  26. Tosco, P., Stiefl, N., Landrum, G.: Bringing the MMFF force field to the RDKit: implementation and validation. J. Cheminformatics 6(1), 1–4 (2014). https://doi.org/10.1186/s13321-014-0037-3

    Article  Google Scholar 

  27. Wang, S., Witek, J., Landrum, G.A., Riniker, S.: Improving conformer generation for small rings and macrocycles based on distance geometry and experimental torsional-angle preferences. J. Chem. Inf. Model. 60(4), 2044–2058 (2020). https://doi.org/10.1021/acs.jcim.0c00025

    Article  Google Scholar 

  28. Gasteiger, J., Marsili, M.: A new model for calculating atomic charges in molecules. Tetrahedron Lett. 19(34), 3181–3184 (1978). https://doi.org/10.1016/S0040-4039(01)94977-9

    Article  Google Scholar 

  29. Rappe, A.K., Casewit, C.J., Colwell, K.S., Goddard, W.A., Skiff, W.M.: UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations. J. Am. Chem. Soc. 114(25), 10024–10035 (1992). https://doi.org/10.1021/ja00051a040

    Article  Google Scholar 

  30. O’Boyle, N.M., Banck, M., James, C.A., Morley, C., Vandermeersch, T., Hutchison, G.R.: Open babel: an open chemical toolbox. J. Cheminformatics 3(1), 33 (2011). https://doi.org/10.1186/1758-2946-3-33

    Article  Google Scholar 

  31. MGLTools Homepage. http://mgltools.scripps.edu/. Accessed 10 Dec 2021

  32. Alhossary, A., Handoko, S.D., Mu, Y., Kwoh, C.-K.: Fast, accurate, and reliable molecular docking with QuickVina 2. Bioinformatics 31(13), 2214–2216 (2015). https://doi.org/10.1093/bioinformatics/btv082

    Article  Google Scholar 

  33. Trott, O., Olson, A.J.: AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 31(2), 455–461 (2010). https://doi.org/10.1002/jcc.21334

    Article  Google Scholar 

  34. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735

    Article  Google Scholar 

  35. TensorFlow Homepage. https://www.tensorflow.org/. Accessed 10 Dec 2021

  36. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations (ICLR), San Diego (2015)

    Google Scholar 

Download references

Acknowledgments

This study was financed by grants of the Belarusian Republican Foundation for Fundamental Research (projects F21COVID-002 and F21ARMG-001) with the support of the Alliance of International Organizations (ANSO-CR-PP-2021-04). The authors are also grateful to the PRIP2021 Conference team for the selection of this study to be supported for publication.

Author information

Authors and Affiliations

  1. United Institute of Informatics Problems, National Academy of Sciences of Belarus, Minsk, Republic of Belarus

    Mikita A. Shuldau, Artsemi M. Yushkevich, Ivan P. Bosko & Alexander V. Tuzikov

  2. EPAM Systems, Minsk Office, Minsk, Republic of Belarus

    Mikita A. Shuldau

  3. Institute of Bioorganic Chemistry, National Academy of Sciences of Belarus, Minsk, Republic of Belarus

    Alexander M. Andrianov

Authors
  1. Mikita A. Shuldau
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Artsemi M. Yushkevich
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ivan P. Bosko
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alexander V. Tuzikov
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alexander M. Andrianov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alexander M. Andrianov .

Editor information

Editors and Affiliations

  1. United Institute of Informatics Problems of The National Academy of Sciences of Belarus, Minsk, Belarus

    Alexander V. Tuzikov

  2. United Institute of Informatics Problems of The National Academy of Sciences of Belarus, Minsk, Belarus

    Alexei M. Belotserkovsky

  3. Belarusian State University of Informatics and Radioelectronics, Minsk, Belarus

    Marina M. Lukashevich

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Shuldau, M.A., Yushkevich, A.M., Bosko, I.P., Tuzikov, A.V., Andrianov, A.M. (2022). Generative Autoencoders for Designing Novel Small-Molecule Compounds as Potential SARS-CoV-2 Main Protease Inhibitors. In: Tuzikov, A.V., Belotserkovsky, A.M., Lukashevich, M.M. (eds) Pattern Recognition and Information Processing. PRIP 2021. Communications in Computer and Information Science, vol 1562. Springer, Cham. https://doi.org/10.1007/978-3-030-98883-8_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-98883-8_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-98882-1

  • Online ISBN: 978-3-030-98883-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation