Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Automated design of ligands to polypharmacological profiles

Nature volume 492, pages 215–220 (2012)Cite this article

Subjects

Abstract

The clinical efficacy and safety of a drug is determined by its activity profile across many proteins in the proteome. However, designing drugs with a specific multi-target profile is both complex and difficult. Therefore methods to design drugs rationally a priori against profiles of several proteins would have immense value in drug discovery. Here we describe a new approach for the automated design of ligands against profiles of multiple drug targets. The method is demonstrated by the evolution of an approved acetylcholinesterase inhibitor drug into brain-penetrable ligands with either specific polypharmacology or exquisite selectivity profiles for G-protein-coupled receptors. Overall, 800 ligand–target predictions of prospectively designed ligands were tested experimentally, of which 75% were confirmed to be correct. We also demonstrate target engagement in vivo. The approach can be a useful source of drug leads when multi-target profiles are required to achieve either selectivity over other drug targets or a desired polypharmacology.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Adaptive drug design.
Figure 2: Polypharmacology profiles of designed ligands.
Figure 3: Reducing α 1 anti-target activity by evolutionary design.
Figure 4: Evolution of D4 dopamine ligands from donepezil.

References

  1. Hughes, J. D. et al. Physicochemical drug properties associated with in vivo toxicological outcomes. Bioorg. Med. Chem. Lett. 18, 4872–4875 (2008)

    Article  CAS  Google Scholar 

  2. Roth, B. L. Drugs and valvular heart disease. N. Engl. J. Med. 356, 6–9 (2007)

    Article  CAS  Google Scholar 

  3. Campillos, M., Kuhn, M., Gavin, A. C., Jensen, L. J. & Bork, P. Drug target identification using side-effect similarity. Science 321, 263–266 (2008)

    Article  CAS  ADS  Google Scholar 

  4. Roth, B. L., Sheffler, D. J. & Kroeze, W. K. Magic shotguns versus magic bullets: selectively non-selective drugs for mood disorders and schizophrenia. Nature Rev. Drug Discov. 3, 353–359 (2004)

    Article  CAS  Google Scholar 

  5. Knight, Z. A., Lin, H. & Shokat, K. M. Targeting the cancer kinome through polypharmacology. Nature Rev. Cancer 10, 130–137 (2010)

    Article  CAS  Google Scholar 

  6. Brötz-Oesterhelt, H. & Brunner, N. A. How many modes of action should an antibiotic have? Curr. Opin. Pharmacol. 8, 564–573 (2008)

    Article  Google Scholar 

  7. Hopkins, A. L. Network pharmacology: the next paradigm in drug discovery. Nature Chem. Biol. 4, 682–690 (2008)

    Article  MathSciNet  CAS  Google Scholar 

  8. Morphy, R. & Rankovic, Z. Designed multiple ligands. An emerging drug discovery paradigm. J. Med. Chem. 48, 6523–6543 (2005)

    Article  CAS  Google Scholar 

  9. Paolini, G. V., Shapland, R. H. B., van Hoorn, W. P., Mason, J. S. & Hopkins, A. L. Global mapping of pharmacological space. Nature Biotechnol. 24, 805–815 (2006)

    Article  CAS  Google Scholar 

  10. Keiser, M. J. et al. Relating protein pharmacology by ligand chemistry. Nature Biotechnol. 25, 197–206 (2007)

    Article  CAS  Google Scholar 

  11. Keiser, M. J. et al. Predicting new molecular targets for known drugs. Nature 462, 175–181 (2009)

    Article  CAS  ADS  Google Scholar 

  12. Vidal, D. & Mestres, J. In silico receptorome screening of antipsychotic drugs. Mol. Inf. 29, 543–551 (2010)

    Article  CAS  Google Scholar 

  13. Lounkine, E. et al. Large-scale prediction and testing of drug activity on side-effect targets. Nature 486, 361–367 (2012)

    Article  CAS  ADS  Google Scholar 

  14. Schneider, G. & So, S.-S. Adaptive Systems in Drug Design (Landes Biosciences, 2002)

    Book  Google Scholar 

  15. Schneider, G. et al. Voyages to the (un)known: adaptive design of bioactive compounds. Trends Biotechnol. 27, 18–26 (2009)

    Article  CAS  Google Scholar 

  16. Schneider, G., Lee, M. L., Stahl, M. & Schneider, P. De novo design of molecular architectures by evolutionary assembly of drug-derived building blocks. J. Comput. Aided Mol. Des. 14, 487–494 (2000)

    Article  CAS  ADS  Google Scholar 

  17. Gillet, V. J., Willett, P., Fleming, P. J. & Green, D. V. Designing focused libraries using MoSELECT. J. Mol. Graph. Model. 20, 491–498 (2002)

    Article  CAS  Google Scholar 

  18. Brown, N., McKay, B. & Gasteiger, J. The de novo design of median molecules within a property range of interest. J. Comput. Aided Mol. Des. 18, 761–771 (2004)

    Article  CAS  ADS  Google Scholar 

  19. Nicolaou, C. A., Brown, N. & Pattichis, C. S. Molecular optimization using computational multi-objective methods. Curr. Opin. Drug Discov. Devel. 10, 316–324 (2007)

    CAS  PubMed  Google Scholar 

  20. Liu, Q., Masek, B., Smith, K. & Smith, J. Tagged fragment method for evolutionary structure-based de novo lead generation and optimization. J. Med. Chem. 50, 5392–5402 (2007)

    Article  CAS  Google Scholar 

  21. Dey, F. & Caflisch, A. Fragment-based de novo ligand design by multiobjective evolutionary optimization. J. Chem. Inf. Model. 48, 679–690 (2008)

    Article  CAS  Google Scholar 

  22. Vinkers, H. M. et al. SYNOPSIS: SYNthesize and OPtimize system in silico. J. Med. Chem. 46, 2765–2773 (2003)

    Article  CAS  Google Scholar 

  23. Heikkilä, T. et al. The first de novo designed inhibitors of Plasmodium falciparum dihydroorotate dehydrogenase. Bioorg. Med. Chem. Lett. 16, 88–92 (2006)

    Article  Google Scholar 

  24. Roche, O. & Rodríguez Sarmiento, R. M. A new class of histamine H3 receptor antagonists derived from ligand based design. Bioorg. Med. Chem. Lett. 17, 3670–3675 (2007)

    Article  CAS  Google Scholar 

  25. Alig, L. et al. Benzodioxoles: novel cannabinoid-1 receptor inverse agonists for the treatment of obesity. J. Med. Chem. 51, 2115–2127 (2008)

    Article  CAS  Google Scholar 

  26. Schneider, G. et al. Reaction-driven de novo design, synthesis and testing of potential type II kinase inhibitors. Future Med. Chem. 3, 415–424 (2011)

    Article  CAS  Google Scholar 

  27. Wermuth, C. G. Selective optimization of side activities: the SOSA approach. Drug Discov. Today 11, 160–164 (2006)

    Article  CAS  Google Scholar 

  28. Gaulton, A. et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 40, D1100–D1107 (2012)

    Article  CAS  Google Scholar 

  29. Ribeiz, S. R. et al. Cholinesterase inhibitors as adjunctive therapy in patients with schizophrenia and schizoaffective disorder: a review and meta-analysis of the literature. CNS Drugs 24, 303–317 (2010)

    Article  CAS  Google Scholar 

  30. Arnsten, A. F., Murphy, B. & Merchant, K. The selective dopamine D4 receptor antagonist, PNU-101387G, prevents stress-induced cognitive deficits in monkeys. Neuropsychopharmacology 23, 405–410 (2000)

    Article  CAS  Google Scholar 

  31. Gillet, V., Johnson, A. P., Mata, P., Sike, S. & Williams, P. SPROUT: a program for structure generation. J. Comput. Aided Mol. Des. 7, 127–153 (1993)

    Article  CAS  ADS  Google Scholar 

  32. Stahl, M. et al. A validation study on the practical use of automated de novo design. J. Comput. Aided Mol. Des. 16, 459–478 (2002)

    Article  CAS  ADS  Google Scholar 

  33. 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, 1079–1087 (2004)

    Article  CAS  Google Scholar 

  34. Nicolaou, C. A., Apostolakis, J. & Pattichis, C. S. De novo drug design using multiobjective evolutionary graphs. J. Chem. Inf. Model. 49, 295–307 (2009)

    Article  CAS  Google Scholar 

  35. Stewart, K. D., Shiroda, M. & James, C. A. Drug Guru: a computer software program for drug design using medicinal chemistry rules. Bioorg. Med. Chem. 14, 7011–7022 (2006)

    Article  CAS  Google Scholar 

  36. Kryssanov, V. V., Tamaki, H. & Kitamura, S. Understanding design fundamentals: how synthesis and analysis drive creativity, resulting in emergence. Artif. Intell. Eng. 15, 329–342 (2001)

    Article  Google Scholar 

  37. Deb, K., Sundar, J., Udaya Bhaskara Rao, N. & Chaudhuri, S. Reference point based multi-objective optimization using evolutionary algorithms. Int. J. Comp. Intell. Res. 2, 273–286 (2006)

    MathSciNet  Google Scholar 

  38. 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, 3–25 (1997)

    Article  CAS  Google Scholar 

  39. Ertl, P. & Schuffenhauer, A. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J. Cheminform. 10, 8 (2009)

    Article  Google Scholar 

  40. Fanelli, F. & De Benedetti, P. G. in Antitargets: Prediction and Prevention of Drug Side Effects (eds Vaz, R. J. & Klabunde, T. ) Ch. 8, 155–193 (Wiley-VCH, 2008)

    Book  Google Scholar 

  41. Bemis, G. W. & Murcko, M. A. The properties of known drugs. 1. molecular frameworks. J. Med. Chem. 39, 2887–2893 (1996)

    Article  CAS  Google Scholar 

  42. Ortega, R. et al. Synthesis, binding affinity and SAR of new benzolactam derivatives as dopamine D3 receptor ligands. Bioorg. Med. Chem. Lett. 19, 1773–1778 (2009)

    Article  CAS  Google Scholar 

  43. Löber, S., Hübner, H., Tschammer, N. & Gmeiner, P. Recent advances in the search for D3 and D4 selective drugs: probes, models and candidates. Trends Pharmacol. Sci. 32, 148–157 (2011)

    Article  Google Scholar 

  44. Martin, R. E., Green, L. G., Guba, W., Kratochwil, N. & Christ, A. Discovery of the first nonpeptidic, small-molecule, highly selective somatostatin receptor subtype 5 antagonists: a chemogenomics approach. J. Med. Chem. 50, 6291–6294 (2007)

    Article  CAS  Google Scholar 

  45. Bender, A. et al. Chemogenomic data analysis: prediction of small-molecule targets and the advent of biological fingerprint. Comb. Chem. High Throughput Screen. 10, 719–731 (2007)

    Article  CAS  Google Scholar 

  46. Rogers, D., Brown, R. D. & Hahn, M. Using extended-connectivity fingerprints with Laplacian-modified Bayesian analysis in high-throughput screening follow-up. J. Biomol. Screen. 10, 682–686 (2005)

    Article  CAS  Google Scholar 

  47. Huang, X. P., Mangano, T., Hufeisen, S., Setola, V. & Roth, B. L. Identification of human Ether-à-go-go related gene modulators by three screening platforms in an academic drug-discovery setting. Assay Drug Dev. Technol. 8, 727–742 (2010)

    Article  CAS  Google Scholar 

  48. Ruda, G. F. et al. Aryl phosphoramidates of 5-phosho erythronohydroxamic acid, a new class of potent trypanocidal agents. J. Med. Chem. 53, 6071–6078 (2010)

    Article  CAS  Google Scholar 

  49. Pogorelov, V. M., Rodriguiz, R. M., Insco, M. L., Caron, M. G. & Wetsel, W. C. Novelty seeking and stereotypic activation of behavior in mice with disruption of the Dat1 gene. Neuropsychopharmacology. 30, 1818–1831 (2005)

    Article  CAS  Google Scholar 

  50. Porton, B. et al. Mice lacking synapsin III show abnormalities in explicit memory and conditioned fear. Genes Brain Behav. 9, 257–268 (2010)

    Article  CAS  Google Scholar 

  51. Glick, M., Jenkins, J. L., Nettles, J. H., Hitchings, H. & Davies, J. W. Enrichment of high-throughput screening data with increasing levels of noise using support vector machines, recursive partitioning, and laplacian-modified naive bayesian classifiers. J. Chem. Inf. Model. 46, 193–200 (2006)

    Article  CAS  Google Scholar 

  52. Rogers, D. & Hahn, M. Extended-connectivity fingerprints. J. Chem. Inf. Model. 50, 742–754 (2010)

    Article  CAS  Google Scholar 

  53. Truchon, J. F. & Bayly, C. I. Evaluating virtual screening methods: good and bad metrics for the “early recognition” problem. J. Chem. Inf. Model. 47, 488–508 (2007)

    Article  CAS  Google Scholar 

  54. Zhao, W., Hevener, K. E., White, S. W., Lee, R. E. & Boyett, J. M. A statistical framework to evaluate virtual screening. BMC Bioinformatics 10, 225 (2009)

    Article  Google Scholar 

  55. Cannon, E. O., Nigsch, F. & Mitchell, J. B. A novel hybrid ultrafast shape descriptor method for use in virtual screening. Chem. Cent. J. 2, 3 (2008)

    Article  Google Scholar 

  56. Corne, D. W. & Knowles, J. D. in Proc. 9th Annual Conf. Genetic Evolutionary Computation 773–780 (ACM, 2007)

    Google Scholar 

  57. Obrezanova, O., Csanyi, G., Gola, J. M. & Segall, M. D. Gaussian processes: a method for automatic QSAR modelling of ADME properties. J. Chem. Inf. Model. 47, 1847–1857 (2007)

    Article  CAS  Google Scholar 

  58. Obrezanova, O., Gola, J. M. R., Champness, E. J. & Segall, M. D. Automatic QSAR modeling of ADME properties: blood-brain barrier penetration and aqueous solubility. J. Comput. Aided Mol. Des. 22, 431–440 (2008)

    Article  CAS  ADS  Google Scholar 

Download references

Acknowledgements

This work is supported by SULSA (HR07019), the BBSRC Doctoral Training Programme, the BBSRC Pathfinder (BB/FOF/PF/15/09) and the BBSRC Follow On Fund schemes (BB/J010510/1) (A.L.H.), the University of Dundee’s Pump Priming Fund for Translational Medical Research (I.H.G. and A.L.H.) and by grants from the National Institutes of Health (NIH) supporting drug discovery receptor pharmacology (B.L.R.) and the NIH grant MH082441 (W.C.W.). The chemical synthesis and informatics benefits from the infrastructure investments from the Wellcome Trust Strategic Award (WT 083481). We thank J. Overington for StARlite and ChEMBL. We wish to thank D. Murugesan for compound purification and C. Means and T. Rhodes for helping with the open field, hole-board and zero-maze tests. We also wish to thank C. Elms and J. Zhou for their support in the husbandry and generation of the mice used for behavioural testing. We also wish to thank F. Y. Li for customizing the software configuration for the hole-board tests. Some of the equipment used in the behavioural testing was purchased with a grant from the North Carolina Biotechnology Center. B.L.R. also received support from the Michael Hooker Chair of Pharmacology.

Author information

Authors and Affiliations

  1. Division of Biological Chemistry and Drug Discovery, College of Life Sciences, University of Dundee, Dundee DD1 5EH, UK,

    Jérémy Besnard, Gian Filippo Ruda, Keren Abecassis, Suzanne Norval, Lauren A. Webster, Frederick R. C. Simeons, Laste Stojanovski, G. Richard Bickerton, Kevin D. Read, Ian H. Gilbert & Andrew L. Hopkins

  2. Department of Pharmacology, NIMH Psychoactive Drug Screening Program, The University of North Carolina Chapel Hill School of Medicine, Chapel Hill, 27759, North Carolina, USA

    Vincent Setola, Xi-Ping Huang & Bryan L. Roth

  3. Mouse Behavioral and Neuroendocrine Analysis Core Facility, Duke University Medical School, Durham, 27710, North Carolina, USA

    Ramona M. Rodriguiz, Antony I. Shin & William C. Wetsel

  4. Department of Pharmacology and Division of Chemical Biology and Medicinal Chemistry, The University of North Carolina Chapel Hill School of Medicine, Chapel Hill, 27759, North Carolina, USA

    Maria F. Sassano & Bryan L. Roth

  5. Laboratory of Biochemical Neuroendocrinology, Clinical Research Institute of Montreal (IRCM), affiliated with the University of Montreal, Montreal, Quebec, H2W 1R7, Canada,

    Annik Prat & Nabil G. Seidah

  6. Ecole Polytechnique Fédérale de Lausanne (EPFL) SV ISREC, Station 19, CH-1015 Lausanne, Switzerland,

    Daniel B. Constam

  7. Departments of Psychiatry and Behavioral Sciences, Cell Biology, and Neurobiology, Duke University Medical School, Durham, 27710, North Carolina, USA

    William C. Wetsel

Authors
  1. Jérémy Besnard
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gian Filippo Ruda
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vincent Setola
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Keren Abecassis
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ramona M. Rodriguiz
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xi-Ping Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Suzanne Norval
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Maria F. Sassano
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Antony I. Shin
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Lauren A. Webster
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Frederick R. C. Simeons
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Laste Stojanovski
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Annik Prat
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Nabil G. Seidah
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Daniel B. Constam
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. G. Richard Bickerton
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Kevin D. Read
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. William C. Wetsel
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Ian H. Gilbert
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Bryan L. Roth
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Andrew L. Hopkins
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

A.L.H. devised the method, developed the algorithm and designed the study. J.B. coded the algorithm and undertook the calculations. G.R.B. developed the databases. A.L.H. and J.B. with I.H.G., G.F.R. and K.A. selected the compounds for synthesis. I.H.G., G.F.R. and K.A. designed the synthetic routes and G.F.R. and K.A. undertook the chemical synthesis. L.A.W. purified and analysed several of the compounds. B.L.R. and V.S. designed the empirical tests for the synthesized compound predictions, analysed and interpreted the results and performed the experiments. X.-P.H. performed the 5-HT2B functional assays and the hERG assays. M.F.S. conducted the dopamine D2 and D4 functional assays. K.D.R. designed the drug metabolism and pharmacokinetics studies and analysed the results. S.N., L.S. and F.R.C.S. carried out the DMPK experiments. For the behavioural experiments, D.B.C. created the mice in which the Pcsk7 gene was disrupted. A.P. and N.G.S. verified the Pcsk7 deletion in many tissues including brain, and then backcrossed the mice onto a C57BL/6 background. W.C.W. designed the studies; R.M.R. and A.I.S. conducted the experiments and analysed the results; W.C.W., R.M.R., A.I.S., D.B.C., A.P. and N.G.S. interpreted the findings; A.L.H. and B.L.R. wrote the manuscript; I.H.G. wrote the synthetic methods with help from G.F.R., K.A. and L.A.W.; W.C.W. and R.M.R. wrote the behavioural section of the manuscript and J.B., V.S., W.C.W. and R.M.R. prepared the figures. All the authors discussed the results and commented on the manuscript.

Corresponding authors

Correspondence to Bryan L. Roth or Andrew L. Hopkins.

Ethics declarations

Competing interests

A.L.H., J.B. and G.R.B. are shareholders in Ex Scientia Ltd, a University of Dundee spin-off company that has licensed the technology.

Supplementary information

Supplementary Information

This file contains Supplementary Figures 1-13, Supplementary Methods, Supplementary Tables 1-12 and Supplementary References – see contents for further details. (PDF 12182 kb)

PowerPoint slides

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Besnard, J., Ruda, G., Setola, V. et al. Automated design of ligands to polypharmacological profiles. Nature 492, 215–220 (2012). https://doi.org/10.1038/nature11691

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature11691

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research