The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
The IUPHAR/BPS Guide to PHARMACOLOGY: an expert-driven knowledgebase of drug targets and their ligands
Open accessAbstract
The International Union of Basic and Clinical Pharmacology/British Pharmacological Society (IUPHAR/BPS) Guide to PHARMACOLOGY (http://www.guidetopharmacology.org) is a new open access resource providing pharmacological, chemical, genetic, functional and pathophysiological data on the targets of approved and experimental drugs. Created under the auspices of the IUPHAR and the BPS, the portal provides concise, peer-reviewed overviews of the key properties of a wide range of established and potential drug targets, with in-depth information for a subset of important targets. The resource is the result of curation and integration of data from the IUPHAR Database (IUPHAR-DB) and the published BPS 'Guide to Receptors and Channels' (GRAC) compendium. The data are derived from a global network of expert contributors, and the information is extensively linked to relevant databases, including ChEMBL, DrugBank, Ensembl, PubChem, UniProt and PubMed. Each of the ∼6000 small molecule and peptide ligands is annotated with manually curated 2D chemical structures or amino acid sequences, nomenclature and database links. Future expansion of the resource will complete the coverage of all the targets of currently approved drugs and future candidate targets, alongside educational resources to guide scientists and students in pharmacological principles and techniques.
Overview
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.
Integrity signals
Registry
Cites
Statistics
Verification
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
ChEMBL: a large-scale bioactivity database for drug discovery
Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr777 · Cited by 4,382
How many drug targets are there?
Overington, J. P., Al-Lazikani, B., & Hopkins, A. L. (2006) · Nature Reviews Drug Discovery · doi:10.1038/nrd2199 · Cited by 3,505
Guide to receptors and channels (GRAC), 5th edition
Alexander, S., Mathie, A., & Peters, J. (2011) · British Journal of Pharmacology · doi:10.1111/j.1476-5381.2011.01649_1.x · Cited by 1,947
Self-citationTrends in the exploitation of novel drug targets
Rask-Andersen, M., Almén, M. S., & Schiöth, H. B. (2011) · Nature Reviews Drug Discovery · doi:10.1038/nrd3478 · Cited by 853
Open PHACTS: semantic interoperability for drug discovery
Williams, A. J., Harland, L., Groth, P., Pettifer, S., Chichester, C., Willighagen, E. L., Evelo, C. T., Blomberg, N., Ecker, G., Goble, C., et al. (2012) · Drug Discovery Today · doi:10.1016/j.drudis.2012.05.016 · Cited by 321
Rask-Andersen, M., Masuram, S., & Schiöth, H. B. (2014) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-011613-135943 · Cited by 282
Cited by
A comprehensive map of molecular drug targets
Santos, R., Ursu, O., Gaulton, A., Bento, A. P., Donadi, R. S., Bologa, C. G., Karlsson, A., Al-Lazikani, B., Hersey, A., Oprea, T. I., et al. (2017) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2016.230 · Cited by 2,427
NicheNet: Modeling intercellular communication by linking ligands to target genes
Browaeys, R., Saelens, W., & Saeys, Y. (2020) · Nature Methods · doi:10.1038/s41592-019-0667-5 · Cited by 2,192
Microglial M1/M2 polarization and metabolic states
Orihuela, R., McPherson, C. A., & Harry, G. J. (2016) · British Journal of Pharmacology · doi:10.1111/bph.13139 · Cited by 2,070
Rouillard, A. D., Gundersen, G. W., Fernandez, N. F., Wang, Z., Monteiro, C. D., McDermott, M. G., & Ma’ayan, A. (2016) · Database · doi:10.1093/database/baw100 · Cited by 1,515
Deciphering cell–cell interactions and communication from gene expression
Armingol, E., Officer, A., Harismendy, O., & Lewis, N. E. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-020-00292-x · Cited by 1,267
Cannabidiol is a negative allosteric modulator of the cannabinoid CB 1 receptor
Laprairie, R. B., Bagher, A. M., Kelly, M. E. M., & Denovan‐Wright, E. M. (2015) · British Journal of Pharmacology · doi:10.1111/bph.13250 · Cited by 1,039
Davenport, A. P., Hyndman, K. A., Dhaun, N., Southan, C., Kohan, D. E., Pollock, J. S., Pollock, D. M., Webb, D. J., & Maguire, J. J. (2016) · Pharmacological Reviews · doi:10.1124/pr.115.011833 · Cited by 772
Self-citationDrug–target interaction prediction: databases, web servers and computational models
Chen, X., Yan, C. C., Zhang, X., Zhang, X., Dai, F., Yin, J., & Zhang, Y. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbv066 · Cited by 658
Network-based approach to prediction and population-based validation of in silico drug repurposing
Cheng, F., Desai, R. J., Handy, D. E., Wang, R., Schneeweiss, S., Barabási, A.-L., & Loscalzo, J. (2018) · Nature Communications · doi:10.1038/s41467-018-05116-5 · Cited by 552
An update on PPAR activation by cannabinoids
O'Sullivan, S. E. (2016) · British Journal of Pharmacology · doi:10.1111/bph.13497 · Cited by 449
Graaf, C. D., Donnelly, D., Wootten, D., Lau, J., Sexton, P. M., Miller, L. J., Ahn, J.-M., Liao, J., Fletcher, M. M., Yang, D., et al. (2016) · Pharmacological Reviews · doi:10.1124/pr.115.011395 · Cited by 403
DGIdb 2.0: mining clinically relevant drug–gene interactions
Wagner, A. H., Coffman, A. C., Ainscough, B. J., Spies, N. C., Skidmore, Z. L., Campbell, K. M., Krysiak, K., Pan, D., McMichael, J. F., Eldred, J. M., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1165 · Cited by 413
Nuclear FAK Controls Chemokine Transcription, Tregs, and Evasion of Anti-tumor Immunity
Serrels, A., Lund, T., Serrels, B., Byron, A., McPherson, R. C., von Kriegsheim, A., Gómez-Cuadrado, L., Canel, M., Muir, M., Ring, J. E., et al. (2015) · Cell · doi:10.1016/j.cell.2015.09.001 · Cited by 407
Selectivity determinants of GPCR–G-protein binding
Flock, T., Hauser, A. S., Lund, N., Gloriam, D. E., Balaji, S., & Babu, M. M. (2017) · Nature · doi:10.1038/nature22070 · Cited by 384
Target identification among known drugs by deep learning from heterogeneous networks
Zeng, X., Zhu, S., Lu, W., Liu, Z., Huang, J., Zhou, Y., Fang, J., Huang, Y., Guo, H., Li, L., et al. (2020) · Chemical Science · doi:10.1039/c9sc04336e · Cited by 357
Ge, Y., Tian, T., Huang, S., Wan, F., Li, J., Li, S., Wang, X., Yang, H., Hong, L., Wu, N., et al. (2021) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-021-00568-6 · Cited by 300
The IUPHAR/BPS Guide to PHARMACOLOGY in 2024
Harding, S. D., Armstrong, J. F., Faccenda, E., Southan, C., Alexander, S. P. H., Davenport, A. P., Spedding, M., & Davies, J. A. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkad944 · Cited by 226
Self-citationUpdate on leukotriene, lipoxin and oxoeicosanoid receptors: IUPHAR Review 7
Bäck, M., Powell, W. S., Dahlén, S., Drazen, J. M., Evans, J. F., Serhan, C. N., Shimizu, T., Yokomizo, T., & Rovati, G. E. (2014) · British Journal of Pharmacology · doi:10.1111/bph.12665 · Cited by 217
A genome-wide positioning systems network algorithm for in silico drug repurposing
Cheng, F., Lu, W., Liu, C., Fang, J., Hou, Y., Handy, D. E., Wang, R., Zhao, Y., Yang, Y., Huang, J., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-10744-6 · Cited by 215
High-fat diet-induced metabolic disorders impairs 5-HT function and anxiety-like behavior in mice
Zemdegs, J., Quesseveur, G., Jarriault, D., Pénicaud, L., Fioramonti, X., & Guiard, B. P. (2016) · British Journal of Pharmacology · doi:10.1111/bph.13343 · Cited by 180
Zhou, Y., Hou, Y., Shen, J., Mehra, R., Kallianpur, A., Culver, D. A., Gack, M. U., Farha, S., Zein, J., Comhair, S., et al. (2020) · PLoS Biology · doi:10.1371/journal.pbio.3000970 · Cited by 180
Haslund‐Vinding, J., McBean, G., Jaquet, V, & Vilhardt, F. (2017) · British Journal of Pharmacology · doi:10.1111/bph.13425 · Cited by 169
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 (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1010 · Cited by 149
Self-citationChen, J., Lai, J., Yang, L., Ruan, G., Chaugai, S., Ning, Q., Chen, C., & Wang, D. W. (2016) · British Journal of Pharmacology · doi:10.1111/bph.13386 · Cited by 120
Zhu, G., Li, J., He, L., Wang, X., & Hong, X. (2015) · British Journal of Pharmacology · doi:10.1111/bph.13061 · Cited by 118
Bower, R. L., & Hay, D. L. (2016) · British Journal of Pharmacology · doi:10.1111/bph.13496 · Cited by 104
Adrenaline: insights into its metabolic roles in hypoglycaemia and diabetes
Verberne, A. J. M., Korim, W. S., Sabetghadam, A., & Llewellyn‐Smith, I. J. (2016) · British Journal of Pharmacology · doi:10.1111/bph.13458 · Cited by 99
Slowik, A., Schmidt, T., Beyer, C., Amor, S., Clarner, T., & Kipp, M. (2015) · British Journal of Pharmacology · doi:10.1111/bph.12938 · Cited by 94
Clark, I. A., & Vissel, B. (2015) · British Journal of Pharmacology · doi:10.1111/bph.13181 · Cited by 84
Effects of glucagon-like peptide-1 on the differentiation and metabolism of human adipocytes
El Bekay, R., Coín‐Aragüez, L., Fernández‐García, D., Oliva‐Olivera, W., Bernal‐López, R., Clemente‐Postigo, M., Delgado‐Lista, J., Diaz‐Ruiz, A., Guzman‐Ruiz, R., Vázquez‐Martínez, R., et al. (2016) · British Journal of Pharmacology · doi:10.1111/bph.13481 · Cited by 78
Sarwar, M., Samuel, C. S., Bathgate, R. A., Stewart, D. R., & Summers, R. J. (2015) · British Journal of Pharmacology · doi:10.1111/bph.12964 · Cited by 74
The role of opioid processes in reward and decision-making
Laurent, V., Morse, A. K., & Balleine, B. W. (2015) · British Journal of Pharmacology · doi:10.1111/bph.12818 · Cited by 64
Hydrogen sulphide protects against NSAID ‐enteropathy through modulation of bile and the microbiota
Blackler, R. W., Motta, J., Manko, A., Workentine, M., Bercik, P., Surette, M. G., & Wallace, J. L. (2015) · British Journal of Pharmacology · doi:10.1111/bph.12961 · Cited by 66
Digoxin reduces atherosclerosis in apolipoprotein E‐deficient mice
Shi, H., Mao, X., Zhong, Y., Liu, Y., Zhao, X., Yu, K., Zhu, R., Wei, Y., Zhu, J., Sun, H., et al. (2016) · British Journal of Pharmacology · doi:10.1111/bph.13453 · Cited by 49
Khroyan, T. V., Wu, J., Polgar, W. E., Cami‐Kobeci, G., Fotaki, N., Husbands, S. M., & Toll, L. (2015) · British Journal of Pharmacology · doi:10.1111/bph.12796 · Cited by 40
Tate, M., Chong, A., Robinson, E., Green, B. D., & Grieve, D. J. (2015) · British Journal of Pharmacology · doi:10.1111/bph.12943 · Cited by 33
Andreetta, F., Carboni, L., Grafton, G., Jeggo, R., Whyment, A. D., van den Top, M., Hoyer, D., Spanswick, D., & Barnes, N. M. (2016) · British Journal of Pharmacology · doi:10.1111/bph.13432 · Cited by 28
Single-Cell RNA Sequencing for Studying Human Cancers
Aran, D. (2023) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-020722-091857 · Cited by 31
Enantioselective inhibition of d‐serine transport by (S)‐ketamine
Singh, N. S., Bernier, M., Camandola, S., Khadeer, M. A., Moaddel, R., Mattson, M. P., & Wainer, I. W. (2015) · British Journal of Pharmacology · doi:10.1111/bph.13239 · Cited by 11
Metadata
- Authors
- 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., Spedding, M., Yu, W., Harmar, A. J., & NC-IUPHAR
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-11-14
- Volume
- 42
- Issue
- D1
- Pages
- D1098-D1106
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W1971825415
- Cited by
- 849
- References
- 6
- FWCI
- 251.74
- Type
- journal article
- Language
- en
- Open access
- gold
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
