Exploring Polypharmacology in Drug Design

  • Patricia Saenz-MéndezEmail author
  • Leif A. Eriksson
Part of the Methods in Molecular Biology book series (MIMB, volume 1824)


Nowadays it is widely accepted that one compound can be able to hit several targets at once. This “magic shotgun” approach for drug development properly describes the mechanism of biomolecular recognition. The need to take into account the polypharmacology in structure-based drug design has led to the development of several computational tools. Here we present a computational protocol to identify promising compounds against several biological targets, a protocol known as inverse docking.

Key words

Multi-target docking Inverse docking Selectivity Polypharmacology Docking score normalization Target-fishing experiments 



This work has been supported by the People Program (Marie Curie Actions) of the European Union’s Seventh Framework Program (FP7/2007–2013) under REA grant agreement N° 608746. We gratefully acknowledge funding from the Swedish Research Council and the Faculty of Science at the University of Gothenburg. We also acknowledge the generous allocation of computer time at the C3SE supercomputing center via a grant from the Swedish National Infrastructure for Computing (SNIC).


  1. 1.
    Ehrlich P (1878) Beiträge zur theorie und praxis der histologischen färbung. Leipzig University, LeipzigGoogle Scholar
  2. 2.
    Ehrlich P (1897) Die wertbemessung des diphterieheilserums und deren theoretische grundlagen. Klinisches Jahrbuch 6:299–326Google Scholar
  3. 3.
    Strebhardt K, Ullrich A (2008) Paul Ehrlich’s magic bullet concept: 100 years of progress. Nat Rev Cancer 8:473–480CrossRefPubMedGoogle Scholar
  4. 4.
    Medina-Franco JL, Giulianotti MA, Welmaker GS et al (2013) Shifting from the single to the multitarget paradigm in drug discovery. Drug Discov Today 18(9–10):495–501. CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    AbdulHameed MDM, Chaudhury S, Singh N et al (2012) Exploring Polypharmacology using a ROCS-based target fishing approach. J Chem Inf Model 52(2):492–505. CrossRefPubMedGoogle Scholar
  6. 6.
    Hay M, Thomas DW, Craighead JL et al (2014) Clinical development success rates for investigational drugs. Nature Biotechnol 32:40–51CrossRefGoogle Scholar
  7. 7.
    Waring MJ, Arrowsmith J, Leach AR et al (2015) An analysis of the attrition of drug candidates from four major pharmaceutical companies. Nat Rev Drug Discov 14(7):475–486. CrossRefPubMedGoogle Scholar
  8. 8.
    Zimmermann GR, Lehar J, Keith CT (2007) Multi-target therapeutics: when the whole is greater than the sum of the parts. Drug Discov Today 12(1–2):34–42. CrossRefPubMedGoogle Scholar
  9. 9.
    Roth BL, Sheffler DJ, Kroeze WK (2004) Magic shotguns versus magic bullets: selectively non-selective drugs for mood disorders and schizophrenia. Nat Rev Drug Discov 3:353–359CrossRefPubMedGoogle Scholar
  10. 10.
    Peters J-U (2013) Polypharmacology – Foe or friend? J Med Chem 56(22):8955–8971. CrossRefPubMedGoogle Scholar
  11. 11.
    Ye H, Liu Q, Wei J (2014) Construction of drug network based on side effects and its application for drug repositioning. PLoS One 9(2):e87864. CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Chen YZ, Zhi DG (2001) Ligand–protein inverse docking and its potential use in the computer search of protein targets of a small molecule. Proteins 43:217–226CrossRefPubMedGoogle Scholar
  13. 13.
    Zahler S, Tietze S, Totzke F et al (2007) Inverse in silico screening for identification of kinase inhibitor targets. Chem Biol 14(11):1207–1214. CrossRefPubMedGoogle Scholar
  14. 14.
    Grinter SZ, Liang Y, Huang SY et al (2011) An inverse docking approach for identifying new potential anti-cancer targets. J Mol Graph Model 29(6):795–799. CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Xie L, Xie L, Bourne PE (2011) Structure-based systems biology for analyzing off-target binding. Curr Opin Struct Biol 21(2):189–199. CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Wang W, Zhou X, He W et al (2012) The interprotein scoring noises in glide docking scores. Proteins 80(1):169–183. CrossRefPubMedGoogle Scholar
  17. 17.
    Eric S, Ke S, Barata T et al (2012) Target fishing and docking studies of the novel derivatives of aryl-aminopyridines with potential anticancer activity. Bioorg Med Chem 20(17):5220–5228. CrossRefPubMedGoogle Scholar
  18. 18.
    Saenz-Méndez P, Eriksson M, Eriksson LA (2017) Ligand selectivity between the ADP-Ribosylating toxins: an inverse-docking study for multitarget drug discovery. ACS Omega 2(4):1710–1719. CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Pettersen EF, Goddard TD, Huang CC et al (2004) UCSF chimera—a visualization system for exploratory research and analysis. J Comput Chem 25(13):1605–1612CrossRefGoogle Scholar
  20. 20.
    DOCK 6.7 (2015) University of California San Francisco.
  21. 21.
    Lang PT, Brozell SR, Mukherjee S et al (2009) DOCK 6: combining techniques to model RNA-small molecule complexes. RNA 15(6):1219–1230. CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
  23. 23.
    Li M, Dyda F, Benhar I et al (1996) Crystal structure of the catalytic domain of Pseudomonas exotoxin a complexed with a nicotinamide adenine dinucleotide analog: implications for the activation process and for ADP ribosylation. Proc Natl Acad Sci U S A 93:6902–6906CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Weiss MS, Blanke SR, Collier RJ et al (1995) Structure of the isolated catalytic domain of diphtheria toxin. Biochemistry 34:773–781CrossRefPubMedGoogle Scholar
  25. 25.
    Jorgensen R, Purdy AE, Fieldhouse RJ et al (2008) Cholix toxin, a novel ADP-ribosylating factor from vibrio cholerae. J Biol Chem 283(16):10671–10678. CrossRefPubMedGoogle Scholar
  26. 26.
    Jakalian A, Bush BL, Jack DB et al (2000) Fast, efficient generation of high-quality atomic charges. AM1-BCC model: I. Method. J Comput Chem 21(2):132–146CrossRefGoogle Scholar
  27. 27.
    Jakalian A, Jack DB, Bayly CI (2002) Fast, efficient generation of high-quality atomic charges. AM1-BCC model: II. Parameterization and validation. J Comput Chem 23(16):1623–1641. CrossRefPubMedGoogle Scholar
  28. 28.
    Wang J, Wang W, Kollman PA et al (2006) Automatic atom type and bond type perception in molecular mechanical calculations. J Mol Graph Mod 25(2):247–260. CrossRefGoogle Scholar
  29. 29.
    Irwin JJ, Sterling T, Mysinger MM et al (2012) ZINC: a free tool to discover chemistry for biology. J Chem Inf Model 52(7):1757–1768CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Richards FM (1977) Areas, volumes, packing, and protein structure. Ann Rev Biophys Bioeng 6:151–176CrossRefGoogle Scholar
  31. 31.
    Kuntz ID, Blaney JM, Oatley SJ et al (1982) A geometric approach to macromolecule-ligand interactions. J Mol Biol 161:269–288CrossRefPubMedGoogle Scholar
  32. 32.
    Vigers GPA, Rizzi JP (2004) Multiple active site corrections for docking and virtual screening. J Med Chem 47:80–89CrossRefPubMedGoogle Scholar
  33. 33.
    Maier JA, Martinez C, Kasavajhala K et al (2015) ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J Chem Theory Comput 11(8):3696–3713. CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Feinstein WP, Brylinski M (2015) Calculating an optimal box size for ligand docking and virtual screening against experimental and predicted binding pockets. J Cheminform 7(1):1–10. CrossRefGoogle Scholar
  35. 35.
    Lauro G, Romano A, Riccio R et al (2011) Inverse virtual screening of antitumor targets: pilot study on a small database of natural bioactive compounds. J Nat Prod 74(6):1401–1407. CrossRefPubMedGoogle Scholar
  36. 36.
    Lauro G, Masullo M, Piacente S et al (2012) Inverse virtual screening allows the discovery of the biological activity of natural compounds. Bioorg Med Chem 20(11):3596–3602. CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Chemistry and Molecular BiologyUniversity of GothenburgGothenburgSweden
  2. 2.Computational Chemistry and Biology Group, Facultad de Química, UdelaRMontevideoUruguay

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