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Global mapping of pharmacological space

Nature Biotechnology volume 24, pages 805–815 (2006)Cite this article

Abstract

We present the global mapping of pharmacological space by the integration of several vast sources of medicinal chemistry structure-activity relationships (SAR) data. Our comprehensive mapping of pharmacological space enables us to identify confidently the human targets for which chemical tools and drugs have been discovered to date. The integration of SAR data from diverse sources by unique canonical chemical structure, protein sequence and disease indication enables the construction of a ligand-target matrix to explore the global relationships between chemical structure and biological targets. Using the data matrix, we are able to catalog the links between proteins in chemical space as a polypharmacology interaction network. We demonstrate that probabilistic models can be used to predict pharmacology from a large knowledge base. The relationships between proteins, chemical structures and drug-like properties provide a framework for developing a probabilistic approach to drug discovery that can be exploited to increase research productivity.

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Figure 1: Human polypharmacology interaction network representing relationships between proteins in chemical space.
Figure 2: Degree of intra- and inter-gene family promiscuity illustrated as a polypharmacology interaction matrix.
Figure 3: Bayesian predictions of pharmacology.
Figure 4: Molecular weight (MW) distribution of compounds by gene family.
Figure 5: Trends in medicinal chemistry of compounds in the database.
Figure 6: Chemical space of drugs and leads.

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Acknowledgements

We want to thank an unknown referee for very helpful comments and suggestions. Thanks to Federica Massagrande, Emma Williamson, Sid Martin, Phil Brain, Bryn Williams-Jones, Jens Loesel, Mark Gardner, Nigel Wilkinson, Steve Pimblett, Giles Ratcliffe, Jerry Lanfear, Carolyn Barker, Tony Wood, Frank Burslem and Colin Groom. In particular, we would like to thank John Overington, Bissan Al-Lazikani, John Bradshaw and Yosi Taitz. Thanks to Alan Newton and the PGRDi Innovation Fund for financial support.

Author information

Author notes

  1. Gaia V Paolini and Andrew L Hopkins: These authors contributed equally to this work.

Authors and Affiliations

  1. The Department of Knowledge Discovery, Pfizer Global Research and Development, Sandwich, CT13 9NJ, Kent, UK

    Gaia V Paolini, Richard H B Shapland & Andrew L Hopkins

  2. The Department of Computational Chemistry, Pfizer Global Research and Development, Sandwich, CT13 9NJ, Kent, UK

    Willem P van Hoorn

  3. The Department of Medicinal Informatics, Structure and Design, Pfizer Global Research and Development, Sandwich, CT13 9NJ, Kent, UK

    Gaia V Paolini, Willem P van Hoorn, Jonathan S Mason & Andrew L Hopkins

  4. The Department of Research Informatics, Pfizer Global Research and Development, Sandwich, CT13 9NJ, Kent, UK

    Richard H B Shapland

  5. Servefile Software Ltd., Nailsea, Bristol, BS48 4SG, North Somerset, UK

    Richard H B Shapland

  6. Lundbeck Research, Ottiliavej 9, DK-2500 Valby, Copenhagen, Denmark

    Jonathan S Mason

Authors
  1. Gaia V Paolini
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Contributions

G.V.P., database design and production and knowledge discovery; R.H.B.S., database design and production; W.P.v.H., predictive modeling; J.S.M., chemical representation; A.L.H., database design and knowledge discovery.

Corresponding author

Correspondence to Andrew L Hopkins.

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Competing interests

All of the authors were or are employees (or contract employees) of Pfizer.

Supplementary information

Supplementary Fig. 1

Bayesian polypharmacology prediction results of Cerep BioPrint data set. (PDF 60 kb)

Supplementary Fig. 2

Bayesian predicted polypharmacology interaction network. (PDF 169 kb)

Supplementary Fig. 3

Supplementary Information to Figure 4. (PDF 598 kb)

Supplementary Table 1

Table of Supplementary Information for Figure 2. (PDF 128 kb)

Supplementary Table 2

Table of Supplementary Information to Figure 3. (PDF 59 kb)

Supplementary Table 3

Supplementary Information for Supplementary Figure 1. (PDF 60 kb)

Supplementary Table 4

Bayesian predicted polypharmacology interaction network for Cerep Bioprint set. (PDF 54 kb)

Supplementary Table 5

Predictions of gene family classification of biologically-active compounds by physico-chemical properties, using Linear Discriminant Analysis. (PDF 60 kb)

Supplementary Data (XLS 205 kb)

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Paolini, G., Shapland, R., van Hoorn, W. et al. Global mapping of pharmacological space. Nat Biotechnol 24, 805–815 (2006). https://doi.org/10.1038/nbt1228

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