CoChat
Sign Up
Cited by 226Computational Drug Discovery MethodsBiomedical Text Mining and OntologiesReceptor Mechanisms and Signaling

The IUPHAR/BPS Guide to PHARMACOLOGY in 2024

Open access
Harding, Simon D, Armstrong, Jane F, Faccenda, Elena, Southan, Christopher, Alexander, Stephen P H, Davenport, Anthony P, Spedding, Michael, Davies, Jamie A·2024·Nucleic Acids Research
DOI 10.1093/nar/gkad944PMC PMC10767925
Open this paper in CoChat — free accountOpen PDF

Abstract

The IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb; https://www.guidetopharmacology.org) is an open-access, expert-curated, online database that provides succinct overviews and key references for pharmacological targets and their recommended experimental ligands. It includes over 3039 protein targets and 12 163 ligand molecules, including approved drugs, small molecules, peptides and antibodies. Here, we report recent developments to the resource and describe expansion in content over the six database releases made during the last two years. The database update section of this paper focuses on two areas relating to important global health challenges. The first, SARS-CoV-2 COVID-19, remains a major concern and we describe our efforts to expand the database to include a new family of coronavirus proteins. The second area is antimicrobial resistance, for which we have extended our coverage of antibacterials in partnership with AntibioticDB, a collaboration that has continued through support from GARDP. We discuss other areas of curation and also focus on our external links to resources such as PubChem that bring important synergies to the resources.

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 · 10.1093/nar/gkad944

Cites

4 of 4 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

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Early astrocyte changes included upregulation of cellular adhesion molecules, neuronal guidance cues, signaling receptors, and GFAP.

    “Early changes included upregulation of cellular adhesion molecules (CADM1, CDRH3, PCDHGA1, PCDHB14, PCDHB16, CLSTN1, ITGA6, NEO1, ANOS1) and neuronal guidance cues (NLGN3, NTRK3, SEMA4B, NTNG2), signaling receptors (PTCHD1, NRP1, BMPR2, UNC5C)70 and GFAP, a known hallmark of AD and astrogliosis71 (Fig. 6j, top).”

    From Integrated multimodal cell atlas of Alzheimer’s disease (2024) · cites this paper as [70]

    The 'Summary of content and curation' section describes pharmacological targets and ligands, not early astrocyte changes. Searches for astrocyte and GFAP upregulation and for the named signaling receptors found no passage in our copy addressing their upregulation. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

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: 42

  1. [1]Harmar A.J., Hills R.A., Rosser E.M., Jones M., Buneman O.P., Dunbar D.R., Greenhill S.D., Hale V.A., Sharman J.L., Bonner T.I. et al. IUPHAR-DB: the IUPHAR database of G protein-coupled receptors and ion channels. Nucleic Acids Res. 2009; 37: D680– D685. 18948278 PMC2686540
  2. [2]Sharman J.L., Benson H.E., Pawson A.J., Lukito V., Mpamhanga C.P., Bombail V., Davenport A.P., Peters J.A., Spedding M., Harmar A.J. et al. IUPHAR-DB: updated database content and new features. Nucleic Acids Res. 2013; 41: D1083– D1088. 23087376 PMC3531077
  3. [3]Sharman J.L., Mpamhanga C.P., Spedding M., Germain P., Staels B., Dacquet C., Laudet V., Harmar A.J. NC-IUPHAR IUPHAR-DB: new receptors and tools for easy searching and visualization of pharmacological data. Nucleic Acids Res. 2011; 39: D534– D538. 21087994 PMC3013670
  4. [5]Pawson A.J., Sharman J.L., Benson H.E., Faccenda E., Alexander S.P.H., Buneman O.P., Davenport A.P., McGrath J.C., Peters J.A., Southan C. et al. The IUPHAR/BPS Guide to PHARMACOLOGY: an expert-driven knowledgebase of drug targets and their ligands. Nucleic Acids Res. 2014; 42: D1098– D1106. 24234439 PMC3965070
  5. [6]Alexander S.P., Kelly E., Mathie A., Peters J.A., Veale E.L., Armstrong J.F., Faccenda E., Harding S.D., Pawson A.J., Southan C. et al. THE CONCISE GUIDE TO PHARMACOLOGY 2021/22: introduction and other protein targets. Br. J. Pharmacol. 2021; 178: S1– S26. 34529830 PMC9513948
  6. [7]Southan C., Sharman J.L., Benson H.E., Faccenda E., Pawson A.J., Alexander S.P.H., Buneman O.P., Davenport A.P., McGrath J.C., Peters J.A. et al. The IUPHAR/BPS Guide to PHARMACOLOGY in 2016: towards curated quantitative interactions between 1300 protein targets and 6000 ligands. Nucleic Acids Res. 2016; 44: D1054– D1068. 26464438 PMC4702778
  7. [8]Harding S.D., Sharman J.L., Faccenda E., Southan C., Pawson A.J., Ireland S., Gray A.J.G., Bruce L., Alexander S.P.H., Anderton S. et al. The IUPHAR/BPS Guide to PHARMACOLOGY in 2018: updates and expansion to encompass the new guide to IMMUNOPHARMACOLOGY. Nucleic Acids Res. 2018; 46: D1091– D1106. 29149325 PMC5753190
  8. [9]Harding S.D., Faccenda E., Southan C., Maffia P., Davies J.A. A new guide to immunopharmacology. Nat. Rev. Immunol. 2018; 18: 729. 30327546
  9. [10]Harding S.D., Faccenda E., Southan C., Pawson A.J., Maffia P., Alexander S.P.H., Davenport A.P., Fabbro D., Levi-Schaffer F., Spedding M. et al. The IUPHAR Guide to Immunopharmacology: connecting immunology and pharmacology. Immunology. 2020; 160: 10– 23. 32020584 PMC7160657
  10. [11]Armstrong J.F., Faccenda E., Harding S.D., Pawson A.J., Southan C., Sharman J.L., Campo B., Cavanagh D.R., Alexander S.P.H., Davenport A.P. et al. The IUPHAR/BPS Guide to PHARMACOLOGY in 2020: extending immunopharmacology content and introducing the IUPHAR/MMV Guide to MALARIA PHARMACOLOGY. Nucleic Acids Res. 2020; 48: D1006– D1021. 31691834 PMC7145572
  11. [12]Farrell L.J., Lo R., Wanford J.J., Jenkins A., Maxwell A., Piddock L.J.V. Revitalizing the drug pipeline: antibioticDB, an open access database to aid antibacterial research and development. J. Antimicrob. Chemother. 2018; 73: 2284– 2297. 29897476
  12. [13]Miethke M., Pieroni M., Weber T., Brönstrup M., Hammann P., Halby L., Arimondo P.B., Glaser P., Aigle B., Bode H.B. et al. Towards the sustainable discovery and development of new antibiotics. Nat. Rev. Chem. 2021; 5: 726– 749. PMC8374425 34426795
  13. [14]Balasegaram M., Piddock L.J.V. The Global Antibiotic Research and Development Partnership (GARDP) not-for-profit model of antibiotic development. ACS Infect. Dis. 2020; 6: 1295– 1298. 32406675
  14. [15]Antimicrobial Resistance Collaborators Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet Lond. Engl. 2022; 399: 629– 655. PMC8841637 35065702
  15. [16]Harding S.D., Armstrong J.F., Faccenda E., Southan C., Alexander S.P.H., Davenport A.P., Pawson A.J., Spedding M., Davies J.A. NC-IUPHAR The IUPHAR/BPS guide to PHARMACOLOGY in 2022: curating pharmacology for COVID-19, malaria and antibacterials. Nucleic Acids Res. 2022; 50: D1282– D1294. 34718737 PMC8689838
  16. [17]Gaulton A., Hersey A., Nowotka M., Bento A.P., Chambers J., Mendez D., Mutowo P., Atkinson F., Bellis L.J., Cibrián-Uhalte E. et al. The ChEMBL database in 2017. Nucleic Acids Res. 2017; 45: D945– D954. 27899562 PMC5210557
  17. [18]Mendez D., Gaulton A., Bento A.P., Chambers J., De Veij M., Félix E., Magariños M.P., Mosquera J.F., Mutowo P., Nowotka M. et al. ChEMBL: towards direct deposition of bioassay data. Nucleic Acids Res. 2019; 47: D930– D940. 30398643 PMC6323927
  18. [20]Škuta C., Southan C., Bartůněk P. Will the chemical probes please stand up?. RSC Med. Chem. 2021; 12: 1428– 1441. 34447939 PMC8372204
  19. [21]Rajan K., Brinkhaus H.O., Agea M.I., Zielesny A., Steinbeck C. DECIMER.ai: an open platform for automated optical chemical structure identification, segmentation and recognition in scientific publications. Nat. Commun. 2023; 14: 5045. 37598180 PMC10439916
  20. [22]Lowe D.M., Corbett P.T., Murray-Rust P., Glen R.C. Chemical name to structure: OPSIN, an open source solution. J. Chem. Inf. Model. 2011; 51: 739– 753. 21384929
  21. [24]Attwood M.M., Fabbro D., Sokolov A.V., Knapp S., Schiöth H.B. Trends in kinase drug discovery: targets, indications and inhibitor design. Nat. Rev. Drug Discov. 2021; 20: 839– 861. 34354255
  22. [25]Anderson B., Rosston P., Ong H.W., Hossain M.A., Davis-Gilbert Z.W., Drewry D.H. How many kinases are druggable? A review of our current understanding. Biochem. J. 2023; 480: 1331– 1363. 37642371 PMC10586788
  23. [26]Gilson M.K., Liu T., Baitaluk M., Nicola G., Hwang L., Chong J. BindingDB in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res. 2016; 44: D1045– D1053. 26481362 PMC4702793
  24. [27]Shen Z., Ratia K., Cooper L., Kong D., Lee H., Kwon Y., Li Y., Alqarni S., Huang F., Dubrovskyi O. et al. Design of SARS-CoV-2 PLpro Inhibitors for COVID-19 Antiviral Therapy Leveraging Binding Cooperativity. J. Med. Chem. 2022; 65: 2940– 2955. 34665619 PMC8547495
  25. [28]Freitas B.T., Durie I.A., Murray J., Longo J.E., Miller H.C., Crich D., Hogan R.J., Tripp R.A., Pegan S.D. Characterization and noncovalent inhibition of the Deubiquitinase and deISGylase activity of SARS-CoV-2 Papain-like protease. ACS Infect. Dis. 2020; 6: 2099– 2109. 32428392
  26. [29]Osipiuk J., Azizi S.-A., Dvorkin S., Endres M., Jedrzejczak R., Jones K.A., Kang S., Kathayat R.S., Kim Y., Lisnyak V.G. et al. Structure of papain-like protease from SARS-CoV-2 and its complexes with non-covalent inhibitors. Nat. Commun. 2021; 12: 743. 33531496 PMC7854729
  27. [30]Davis H.E., McCorkell L., Vogel J.M., Topol E.J. Long COVID: major findings, mechanisms and recommendations. Nat. Rev. Microbiol. 2023; 21: 133– 146. 36639608 PMC9839201
  28. [31]Guarnieri J.W., Dybas J.M., Fazelinia H., Kim M.S., Frere J., Zhang Y., Soto Albrecht Y., Murdock D.G., Angelin A., Singh L.N. et al. Core mitochondrial genes are down-regulated during SARS-CoV-2 infection of rodent and human hosts. Sci. Transl. Med. 2023; 15: eabq1533. 37556555 PMC11624572
  29. [32]Li G., Hilgenfeld R., Whitley R., De Clercq E. Therapeutic strategies for COVID-19: progress and lessons learned. Nat. Rev. Drug Discov. 2023; 22: 449– 475. 37076602 PMC10113999
  30. [34]Sansone S.-A., McQuilton P., Rocca-Serra P., Gonzalez-Beltran A., Izzo M., Lister A.L., Thurston M. FAIRsharing as a community approach to standards, repositories and policies. Nat. Biotechnol. 2019; 37: 358– 367. 30940948 PMC6785156
  31. [35]Pándy-Szekeres G., Munk C., Tsonkov T.M., Mordalski S., Harpsøe K., Hauser A.S., Bojarski A.J., Gloriam D.E. GPCRdb in 2018: adding GPCR structure models and ligands. Nucleic Acids Res. 2018; 46: D440– D446. 29155946 PMC5753179
  32. [36]Munk C., Isberg V., Mordalski S., Harpsøe K., Rataj K., Hauser A.S., Kolb P., Bojarski A.J., Vriend G., Gloriam D.E. GPCRdb: the G protein-coupled receptor database – an introduction. Br. J. Pharmacol. 2016; 173: 2195– 2207. 27155948 PMC4919580
  33. [37]Pándy-Szekeres G., Caroli J., Mamyrbekov A., Kermani A.A., Keserű G.M., Kooistra A.J., Gloriam D.E. GPCRdb in 2023: state-specific structure models using AlphaFold2 and new ligand resources. Nucleic Acids Res. 2023; 51: D395– D402. 36395823 PMC9825476
  34. [38]Bajorath J., Chávez-Hernández A.L., Duran-Frigola M., Fernández-de Gortari E., Gasteiger J., López-López E., Maggiora G.M., Medina-Franco J.L., Méndez-Lucio O., Mestres J. et al. Chemoinformatics and artificial intelligence colloquium: progress and challenges in developing bioactive compounds. J. Cheminformatics. 2022; 14: 82. PMC9716667 36461094
  35. [39]Willighagen E.L., Mayfield J.W., Alvarsson J., Berg A., Carlsson L., Jeliazkova N., Kuhn S., Pluskal T., Rojas-Chertó M., Spjuth O. et al. The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching. J. Cheminformatics. 2017; 9: 33. PMC5461230 29086040
  36. [40]Armstrong J.F., Campo B., Alexander S.P.H., Arendse L.B., Cheng X., Davenport A.P., Faccenda E., Fidock D.A., Godinez-Macias K.P., Harding S.D. et al. Advances in malaria pharmacology and the online guide to MALARIA PHARMACOLOGY: IUPHAR review 38. Br. J. Pharmacol. 2023; 180: 1899– 1929. 37197802
  37. [41]Chambers J., Davies M., Gaulton A., Hersey A., Velankar S., Petryszak R., Hastings J., Bellis L., McGlinchey S., Overington J.P. UniChem: a unified chemical structure cross-referencing and identifier tracking system. J. Cheminformatics. 2013; 5: 3. PMC3616875 23317286
  38. [42]Seal R.L., Braschi B., Gray K., Jones T.E.M., Tweedie S., Haim-Vilmovsky L., Bruford E.A. Genenames.org: the HGNC resources in 2023. Nucleic Acids Res. 2023; 51: D1003– D1009. 36243972 PMC9825485
  39. [43]Gillespie M., Jassal B., Stephan R., Milacic M., Rothfels K., Senff-Ribeiro A., Griss J., Sevilla C., Matthews L., Gong C. et al. The reactome pathway knowledgebase 2022. Nucleic Acids Res. 2022; 50: D687– D692. 34788843 PMC8689983
  40. [44]RESOLUTE, REsolution consortia Wiedmer T., Ingles-Prieto A., Goldmann U., Steppan C.M., Superti-Furga G. Accelerating SLC Transporter Research: streamlining Knowledge and Validated Tools. Clin. Pharmacol. Ther. 2022; 112: 439– 442. 35938294 PMC9540488
  41. [45]Sayers E.W., Agarwala R., Bolton E.E., Brister J.R., Canese K., Clark K., Connor R., Fiorini N., Funk K., Hefferon T. et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2019; 47: D23– D28. 30395293 PMC6323993
  42. [46]Zaslavsky L., Cheng T., Gindulyte A., He S., Kim S., Li Q., Thiessen P., Yu B., Bolton E.E. Discovering and Summarizing Relationships Between Chemicals, Genes, Proteins, and Diseases in PubChem. Front. Res. Metr. Anal. 2021; 6: 689059. 34322655 PMC8311438

Cited by

Sorted by citations

  • Integrated multimodal cell atlas of Alzheimer’s disease

    Gabitto, M. I., Travaglini, K. J., Rachleff, V. M., Kaplan, E. S., Long, B., Ariza, J., Ding, Y., Mahoney, J. T., Dee, N., Goldy, J., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01774-5 · Cited by 397 · see citing works

    “Early changes included upregulation of cellular adhesion molecules (CADM1, CDRH3, PCDHGA1, PCDHB14, PCDHB16, CLSTN1, ITGA6, NEO1, ANOS1) and neuronal guidance cues (NLGN3, NTRK3, SEMA4B, NTNG2), signaling receptors (PTCHD1, NRP1, BMPR2, UNC5C) 70 and GFAP, a known hallmark of AD and astrogliosis 71 (Fig.”

Authors
Harding, S. D., Armstrong, J. F., Faccenda, E., Southan, C., Alexander, S. P. H., Davenport, A. P., Spedding, M., & Davies, J. A.
Affiliations
  • University of Edinburgh (GB)
  • University of Nottingham (GB)
  • University of Cambridge (GB)
  • Spedding Research Solutions SAS , Le Vésinet 78110 , France
Corresponding author
Harding, Simon D
ORCID
Venue
Nucleic Acids Research
Publisher
Oxford University Press (OUP)
Published
2023-10-28
Volume
52
Issue
D1
Pages
D1438-D1449
ISSN
  • 0305-1048 (ISSN-L)
  • 1362-4962
DOI
10.1093/nar/gkad944
PMC
PMC10767925
OpenAlex
W4388007339
Cited by
226
References
4
Citations by year
2026: 55 · 2025: 81 · 2024: 78 · 2023: 1 · 2022: 1 · 2021: 2 · 2018: 2 · 2017: 4 · 2016: 1 · 2015: 1
FWCI
32.35
Type
journal article
Language
en
Open access
Licence
Locations
  • Nucleic Acids Research (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by-sa)
  • PubMed (publishedVersion)
  • Repository@Nottingham (University of Nottingham) (publishedVersion, other-oa)
  • Edinburgh Research Explorer (University of Edinburgh) (publishedVersion, cc-by)
  • Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by)
  • Apollo (University of Cambridge) (submittedVersion, cc-by)
  • Apollo (cc-by)
Funders
  • International Union of Basic and Clinical Pharmacology
  • British Pharmacological Society (MED791)
  • GARDP (c-12440474)
  • Medicines for Malaria Venture (CT-5535)
  • Antibiotic Research UK (ANTSRG 02/2019)
  • Wellcome Trust (108420/Z/15/Z)
  • American Society for Pharmacology and Experimental Therapeutics
  • Laboratoires Servier
  • The University of Edinburgh
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Receptor Mechanisms and Signaling
Keywords
GtoPdb, SARS-CoV-2, coronavirus proteins, antimicrobial resistance, antibacterial agents, therapeutic target, database curation
MeSH terms
Antibodies, Ligands, Pharmacology, Proteins, Databases, Factual, Drug Discovery, Databases, Pharmaceutical

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