Cited by 2,402Computational Drug Discovery MethodsReceptor Mechanisms and SignalingBioinformatics and Genomic Networks

Relating protein pharmacology by ligand chemistry

Open access
Keiser, Michael J, Roth, Bryan L, Armbruster, Blaine N, Ernsberger, Paul, Irwin, John J, Shoichet, Brian K·2007·Nature Biotechnology

Abstract

The identification of protein function based on biological information is an area of intense research. Here we consider a complementary technique that quantitatively groups and relates proteins based on the chemical similarity of their ligands. We began with 65,000 ligands annotated into sets for hundreds of drug targets. The similarity score between each set was calculated using ligand topology. A statistical model was developed to rank the significance of the resulting similarity scores, which are expressed as a minimum spanning tree to map the sets together. Although these maps are connected solely by chemical similarity, biologically sensible clusters nevertheless emerged. Links among unexpected targets also emerged, among them that methadone, emetine and loperamide (Imodium) may antagonize muscarinic M3, alpha2 adrenergic and neurokinin NK2 receptors, respectively. These predictions were subsequently confirmed experimentally. Relating receptors by ligand chemistry organizes biology to reveal unexpected relationships that may be assayed using the ligands themselves.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/nbt1284

Cites

48 of 48 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 14

  1. Scott, L.J. & Perry, C.M. Delavirdine: a review of its use in HIV infection. Drugs 60, 1411-1444 (2000).
  2. Roth, B. L. Drugs and valvular heart disease. The New England journal of medicine 356, 6-9 (2007).
  3. Oprea, T. I. Chemoinformatics in drug discovery. (Wiley-VCH, 2005).
  4. Scott, L. J. & Perry, C. M. Delavirdine: a review of its use in HIV infection. Drugs 60, 1411-1444 (2000).
  5. Romero, P. et al. Computational prediction of human metabolic pathways from the complete human genome. Genome Biol 6, R2 (2005).
  6. Roth B, Sheffler D, Kroeze W. Magic shotguns versus magic bullets: selectively non-selective drugs for mood disorders and schizophrenia. Nature Reviews Drug Discovery 2004;3:353-9.
  7. Paolini G, Shapland R, van Hoorn W, Mason J, Hopkins A. Global mapping of pharmacological space. Nature Biotechnology 2006;24:805-15.
  8. Campillos M, Kuhn M, Gavin AC, Jensen LJ, Bork P. Drug target identification using side-effect similarity. Science 2008;321:263- 6.
  9. Keiser M, Roth B, Armbruster B, Ernsberger P, Irwin J, Shoichet B. Relating protein pharmacology by ligand chemistry. Nature Biotechnology 2007;25:197-206.
  10. Johnson MA, Maggiora GM, eds. Concepts and Applications of Molecular Similarity. New York: John Wiley; 1990.
  11. Frye SV. Structure-activity relationship homology (SARAH): a conceptual framework for drug discovery in the genomic era. Chemistry & Biology 1999;6:R3-R7.
  12. Jacoby E, Schuffenhauer A, Floersheim P. Chemogenomics knowledge-based 204 strategies in drug discovery. Drug News & Perspectives 2003;16:93-9102.
  13. Hert J, Keiser M, Irwin J, Oprea T, Shoichet B. Quantifying the Relationships among Drug Classes. Journal of Chemical Information and Modeling 2008;48:755-65.
  14. Shannon P, Markiel A, Ozier O, et al. Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Research 2003;13:2498-504.

Cited by

Sorted by citations

Authors
Keiser, M. J., Roth, B. L., Armbruster, B. N., Ernsberger, P., Irwin, J. J., & Shoichet, B. K.
Affiliations
  • University of California, San Francisco (US)
  • University of North Carolina at Chapel Hill (US)
  • National Institute of Mental Health (US)
Corresponding author
  • Irwin, John J
  • Shoichet, Brian K
Venue
Nature Biotechnology
Published
2007-02
Volume
25
Issue
2
Pages
197-206
ISSN
  • 0733-222X (ISSN-L)
  • 1087-0156
  • 1546-1696
OpenAlex
W2162011385
Cited by
2,402
References
48
Citations by year
2026: 243 · 2025: 223 · 2024: 215 · 2023: 206 · 2022: 176 · 2021: 180 · 2020: 149 · 2019: 128 · 2018: 109 · 2017: 85 · 2016: 94 · 2015: 95 · 2014: 78 · 2013: 105 · 2012: 77
FWCI
64.47
Type
journal article
Language
en
Open access
Locations
  • Nature Biotechnology (publishedVersion)
  • PubMed (publishedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
  • Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
  • UNC Libraries
Funders
  • Johns Hopkins University
  • National Institute of General Medical Sciences (GM71896, F32GM074554, T32GM067547, GM67547, R01GM071896)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Receptor Mechanisms and Signaling
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords
ligand topology, drug-target interactions, protein clustering, minimum spanning tree, M3 muscarinic receptor, alpha2-adrenoceptors, methadone, emetine, loperamide
MeSH terms
Binding Sites, Pharmaceutical Preparations, Ligands, Protein Binding, Proteins, Drug Design, Sequence Alignment, Drug Delivery Systems, Sequence Analysis, Protein, Protein Interaction Mapping, Databases, Protein

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review