Skip to main content
SpringerLink
Log in

Evolutionary Multi-objective Design of SARS-CoV-2 Protease Inhibitor Candidates

  • Conference paper
  • First Online:
Parallel Problem Solving from Nature – PPSN XVI (PPSN 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12270))

Included in the following conference series:

Abstract

Computational drug design based on artificial intelligence is an emerging research area. At the time of writing this paper, the world suffers from an outbreak of the coronavirus SARS-CoV-2. A promising way to stop the virus replication is via protease inhibition. We propose an evolutionary multi-objective algorithm (EMOA) to design potential protease inhibitors for SARS-CoV-2 ’s main protease. Based on the SELFIES representation the EMOA maximizes the binding of candidate ligands to the protein using the docking tool QuickVina 2, while at the same time taking into account further objectives like drug-likeness or the fulfillment of filter constraints. The experimental part analyzes the evolutionary process and discusses the inhibitor candidates.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

Notes

  1. 1.

    https://www.ibm.org/OpenPandemics.

  2. 2.

    https://www.rdkit.org.

  3. 3.

    http://mgltools.scripps.edu.

  4. 4.

    PDB: protein data base, https://www.rcsb.org.

References

  1. 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 

  2. Beyer, H.G., Schwefel, H.P.: Evolution strategies – a comprehensive introduction. Nat. Comput. 1(1), 3–52 (2002). https://doi.org/10.1023/A:1015059928466

    Article  MathSciNet  MATH  Google Scholar 

  3. Bickerton, G.R., Paolini, G.V., Besnard, J., Muresan, S., Hopkins, A.L.: Quantifying the chemical beauty of drugs. Nat. Chem. 4(2), 90–98 (2012). https://doi.org/10.1038/nchem.1243

    Article  Google Scholar 

  4. Brown, N., Fiscato, M., Segler, M.H., Vaucher, A.C.: GuacaMol: benchmarking models for de novo molecular design. J. Chem. Inf. Model. 59(3), 1096–1108 (2019). https://doi.org/10.1021/acs.jcim.8b00839

    Article  Google Scholar 

  5. Brown, N., McKay, B., Gilardoni, F., Gasteiger, J.: A graph-based genetic algorithm and its application to the multiobjective evolution of median molecules. J. Chem. Inf. Comput. Sci. 44(3), 1079–1087 (2004). https://doi.org/10.1021/ci034290p

    Article  Google Scholar 

  6. Caly, L., Druce, J.D., Catton, M.G., Jans, D.A., Wagstaff, K.M.: The FDA-approved Drug Ivermectin inhibits the replication of SARS-CoV-2 in vitro. Antiviral Res. 104787 (2020). https://doi.org/10.1016/j.antiviral.2020.104787

  7. Dai, W., et al.: Structure-based design of antiviral drug candidates targeting the SARS-CoV-2 main protease. Science (2020). https://doi.org/10.1126/science.abb4489

    Article  Google Scholar 

  8. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002). https://doi.org/10.1109/4235.996017

    Article  Google Scholar 

  9. Devi, R.V., Sathya, S.S., Coumar, M.S.: Evolutionary algorithms for de novo drug design – a survey. Appl. Soft Comput. 27, 543–552 (2015). https://doi.org/10.1016/j.asoc.2014.09.042

    Article  Google Scholar 

  10. Douguet, D., Thoreau, E., Grassy, G.: A genetic algorithm for the automated generation of small organic molecules: drug design using an evolutionary algorithm. J. Comput. Aided Mol. Des. 14(5), 449–466 (2000). https://doi.org/10.1023/A:1008108423895

    Article  Google Scholar 

  11. Ertl, P., Roggo, S., Schuffenhauer, A.: Natural product-likeness score and its application for prioritization of compound libraries. J. Chem. Inf. Model. 48(1), 68–74 (2008). https://doi.org/10.1021/ci700286x

    Article  Google Scholar 

  12. Ertl, P., Schuffenhauer, A.: Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J. Cheminform. 1(1), 8 (2009). https://doi.org/10.1186/1758-2946-1-8

    Article  Google Scholar 

  13. Fischer, A., Sellner, M., Neranjan, S., Lill, M.A., Smieško, M.: Inhibitors for Novel Coronavirus Protease Identified by Virtual Screening of 687 Million Compounds (2020). https://doi.org/10.26434/chemrxiv.11923239.v1

  14. de Freitas, R.F., Schapira, M.: A systematic analysis of atomic protein–ligand interactions in the PDB. Med. Chem. Commun. 8(10), 1970–1981 (2017). https://doi.org/10.1039/C7MD00381A

    Article  Google Scholar 

  15. Gaillard, T.: Evaluation of AutoDock and AutoDock Vina on the CASF-2013 Benchmark. J. Chem. Inf. Model. 58(8), 1697–1706 (2018). https://doi.org/10.1021/acs.jcim.8b00312

    Article  Google Scholar 

  16. Jin, Z., et al.: Structure of M pro from COVID-19 virus and discovery of its inhibitors. Nature 1–9 (2020). https://doi.org/10.1038/s41586-020-2223-y

  17. Kaplan, S.S., Hicks, C.B.: Safety and antiviral activity of lopinavir/ritonavir-based therapy in human immunodeficiency virus type 1 (HIV-1) infection. J. Antimicrob. Chemother. 56(2), 273–276 (2005). https://doi.org/10.1093/jac/dki209

    Article  Google Scholar 

  18. Khaerunnisa, S., Kurniawan, H., Awaluddin, R., Suhartati, S., Soetjipto, S.: Potential Inhibitor of COVID-19 Main Protease (Mpro) From Several Medicinal Plant Compounds by Molecular Docking Study (2020). https://doi.org/10.20944/preprints202003.0226.v1

  19. Klimek, M.D., Perelstein, M.: Neural network-based approach to phase space integration. arXiv:1810.11509 [hep-ex, physics:hep-ph, physics:physics, stat] (2018)

  20. Krause, T., et al.: Breeding cell penetrating peptides: optimization of cellular uptake by a function-driven evolutionary process. Bioconjugate Chem. 29(12), 4020–4029 (2018). https://doi.org/10.1021/acs.bioconjchem.8b00583

    Article  Google Scholar 

  21. Krenn, M., Häse, F., Nigam, A., Friederich, P., Aspuru-Guzik, A.: Self-referencing embedded strings (SELFIES): a 100% robust molecular string representation. arXiv:1905.13741 [physics, physics:quant-ph, stat] (2020)

  22. Lameijer, E.W., Kok, J.N., Bäck, T., IJzerman, A.P.: The molecule evoluator. an interactive evolutionary algorithm for the design of drug-like molecules. J. Chem. Inf. Model. 46(2), 545–552 (2006). https://doi.org/10.1021/ci050369d

  23. Lipinski, C.A., Lombardo, F., Dominy, B.W., Feeney, P.J.: Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 23(1), 3–25 (1997). https://doi.org/10.1016/S0169-409X(96)00423-1

    Article  Google Scholar 

  24. Liu, X., Zhang, B., Jin, Z., Yang, H., Rao, Z.: 6LU7: The crystal structure of COVID-19 main protease in complex with an inhibitor N3 (2020). https://www.rcsb.org/structure/6lu7

  25. Macchiagodena, M., Pagliai, M., Procacci, P.: Inhibition of the Main Protease 3CL-pro of the Coronavirus Disease 19 via Structure-Based Ligand Design and Molecular Modeling. arXiv:2002.09937 [q-bio] (2020)

  26. Morris, G.M., et al.: AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J. Comput. Chem. 30(16), 2785–2791 (2009). https://doi.org/10.1002/jcc.21256

  27. Nguyen, H., Case, D.A., Rose, A.S.: NGLview–interactive molecular graphics for Jupyter notebooks. Bioinformatics 34(7), 1241–1242 (2018). https://doi.org/10.1093/bioinformatics/btx789

    Article  Google Scholar 

  28. Nicolaou, C.A., Brown, N.: Multi-objective optimization methods in drug design. Drug Discov. Today Technol. 10(3), e427–e435 (2013). https://doi.org/10.1016/j.ddtec.2013.02.001

    Article  Google Scholar 

  29. Nigam, A., Friederich, P., Krenn, M., Aspuru-Guzik, A.: Augmenting genetic algorithms with deep neural networks for exploring the chemical space. arXiv:1909.11655 [physics] (2020)

  30. Pegg, S.C.H., Haresco, J.J., Kuntz, I.D.: A genetic algorithm for structure-based de novo design. J. Comput. Aided Mol. Des. 15(10), 911–933 (2001). https://doi.org/10.1023/A:1014389729000

    Article  Google Scholar 

  31. Polykovskiy, D., et al.: Molecular sets (MOSES): a benchmarking platform for molecular generation models. arXiv:1811.12823 [cs, stat] (2019)

  32. Röckendorf, N., Borschbach, M., Frey, A.: molecular evolution of peptide ligands with custom-tailored characteristics for targeting of glycostructures. PLOS Comput. Biol. 8(12), e1002,800 (2012). https://doi.org/10.1371/journal.pcbi.1002800

  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. van der Horst, E., et al.: Multi-objective evolutionary design of adenosine receptor ligands. J. Chem. Inf. Model. 52(7), 1713–1721 (2012). https://doi.org/10.1021/ci2005115

    Article  Google Scholar 

  35. Wager, T.T., Hou, X., Verhoest, P.R., Villalobos, A.: central nervous system multiparameter optimization desirability: application in drug discovery. ACS Chem. Neurosci. 7(6), 767–775 (2016). https://doi.org/10.1021/acschemneuro.6b00029

    Article  Google Scholar 

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

    Article  Google Scholar 

  37. Yuan, Y., Pei, J., Lai, L.: LigBuilder V3: a multi-target de novo drug design approach. Front. Chem. 8 (2020). https://doi.org/10.3389/fchem.2020.00142

  38. Zhang, L., et al.: Crystal structure of SARS-CoV-2 main protease provides a basis for design of improved \(\alpha \)-ketoamide inhibitors. Science 368(6489), 409–412 (2020). https://doi.org/10.1126/science.abb3405

    Article  Google Scholar 

  39. Zitzler, E., Thiele, L.: Multiobjective optimization using evolutionary algorithms — a comparative case study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 292–301. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0056872

    Chapter  Google Scholar 

Download references

Acknowledgements

We thank Ahmad Reza Mehdipour, Max Planck Institute of Biophysics, Frankfurt, Germany, for useful comments improving this manuscript. Furthermore, we thank the German Research Foundation (DFG) for supporting our work within the Research Training Group SCARE (GRK 1765/2).

Author information

Authors and Affiliations

  1. Computational Intelligence Lab, Department of Computer Science, University of Oldenburg, Oldenburg, Germany

    Tim Cofala, Lars Elend, Philip Mirbach, Jonas Prellberg & Oliver Kramer

  2. Theoretical Chemistry Group, Department of Chemistry, University of Oldenburg, Oldenburg, Germany

    Thomas Teusch

Authors
  1. Tim Cofala
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lars Elend
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Philip Mirbach
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jonas Prellberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Thomas Teusch
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Oliver Kramer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Oliver Kramer .

Editor information

Editors and Affiliations

  1. Leiden University, Leiden, The Netherlands

    Thomas Bäck

  2. Leiden University, Leiden, The Netherlands

    Mike Preuss

  3. Leiden University, Leiden, The Netherlands

    André Deutz

  4. Sorbonne University, Paris, France

    Hao Wang

  5. Sorbonne University, Paris, France

    Carola Doerr

  6. Leiden University, Leiden, The Netherlands

    Michael Emmerich

  7. University of Münster, Münster, Germany

    Heike Trautmann

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Cofala, T., Elend, L., Mirbach, P., Prellberg, J., Teusch, T., Kramer, O. (2020). Evolutionary Multi-objective Design of SARS-CoV-2 Protease Inhibitor Candidates. In: Bäck, T., et al. Parallel Problem Solving from Nature – PPSN XVI. PPSN 2020. Lecture Notes in Computer Science(), vol 12270. Springer, Cham. https://doi.org/10.1007/978-3-030-58115-2_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-58115-2_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58114-5

  • Online ISBN: 978-3-030-58115-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation