ChEMBL: towards direct deposition of bioassay data
Open accessAbstract
ChEMBL is a large, open-access bioactivity database (https://www.ebi.ac.uk/chembl), previously described in the 2012, 2014 and 2017 Nucleic Acids Research Database Issues. In the last two years, several important improvements have been made to the database and are described here. These include more robust capture and representation of assay details; a new data deposition system, allowing updating of data sets and deposition of supplementary data; and a completely redesigned web interface, with enhanced search and filtering capabilities.
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The other half of activity endpoints originates from ChEMBLdb and IUPHAR/BPS Guide to Pharmacology.
“The other half originates from ChEMBLdb (27) and the IUPHAR/BPS Guide to Pharmacology (30).”
From DrugCentral 2023 extends human clinical data and integrates veterinary drugs (2023) · cites this paper as [27]
More than 85% (234 drugs) of newly added drugs have external identifiers in PubChem, WHO INN, DrugBank, ChEMBLdb, KEGG, UNII, UMLS, etc.
“More than 85% (234 drugs) of the newly added drugs have external identifiers in PubChem (25), World Health Organization (WHO) International Nonproprietary Names (INN), DrugBank (26), ChEMBLdb (27), KEGG (28), FDA’s Global Substance Registration System—Unique Ingredient Identifier (UNII; https://bit.ly/3MNMqwQ), Unified Medical Language System, UMLS (29), etc. (Table 1).”
From DrugCentral 2023 extends human clinical data and integrates veterinary drugs (2023) · cites this paper as [27]
The CC is limited by the availability of experimental data and the coverage of its source databases.
“In essence, the CC is an alternative representation of the small-molecule knowledge deposited in the public domain and, as such, it is also limited by the availability of experimental data and the coverage of its source databases (e.g., ChEMBL9 or DrugBank10).”
From Bioactivity descriptors for uncharacterized chemical compounds (2021) · cites this paper as [9]
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Cites
UniProt: the Universal Protein knowledgebase
Apweiler, R. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkh131 · Cited by 7,535 · see citing works
Goodman and Gilman's the Pharmacological Basis of Therapeutics
Stigelman, W. H., & Pharm, D. (1986) · Military Medicine · doi:10.1093/milmed/151.1.386 · Cited by 6,796 · see citing works
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 · see citing works
Self-citationGaulton, A., Hersey, A., Nowotka, M., Bento, A. P., Chambers, J. K., Méndez, D., Mutowo, P., Atkinson, F. L., Bellis, L. J., Cibrián–Uhalte, E., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw1074 · Cited by 2,351 · see citing works
Self-citationUniProt: the universal protein knowledgebase
The UniProt Consortium (2018) · Nucleic Acids Research · doi:10.1093/nar/gky092 · Cited by 2,043 · see citing works
Gilson, M. K., Liu, T., Baitaluk, M., Nicola, G., Hwang, L., & Chong, J. P. C. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1072 · Cited by 1,632 · see citing works
The ChEMBL bioactivity database: an update
Bento, A. P., Gaulton, A., Hersey, A., Bellis, L. J., Chambers, J. K., Davies, M., Krüger, F. A., Light, Y., Mak, L., McGlinchey, S., et al. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1031 · Cited by 1,487 · see citing works
Self-citationInChI, the IUPAC International Chemical Identifier
Heller, S. R., Mcnaught, A. D., Плетнев, И. В., Stein, S. E., & Tchekhovskoi, D. V. (2015) · Journal of Cheminformatics · doi:10.1186/s13321-015-0068-4 · Cited by 875 · see citing works
ChEMBL web services: streamlining access to drug discovery data and utilities
Davies, M., Nowotka, M., Papadatos, G., Dedman, N., Gaulton, A., Atkinson, F. L., Bellis, L. J., & Overington, J. P. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv352 · Cited by 765 · see citing works
Self-citationWang, Y., Bryant, S. H., Cheng, T., Wang, J., Gindulytė, A., Shoemaker, B. A., Thiessen, P. A., He, S., & Zhang, J. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw1118 · Cited by 675 · see citing works
Large-scale comparison of machine learning methods for drug target prediction on ChEMBL
Mayr, A., Klambauer, G., Unterthiner, T., Steijaert, M., Wegner, J. K., Ceulemans, H., Clevert, D.-A., & Hochreiter, S. (2018) · Chemical Science · doi:10.1039/c8sc00148k · Cited by 584 · see citing works
Goodman and Gilman's, The Pharmacological Basis of Therapeutics
DiStefano, V. (1986) · Journal of Pediatric Ophthalmology & Strabismus · doi:10.3928/0191-3913-19860501-17 · Cited by 579 · see citing works
Modeling sample variables with an Experimental Factor Ontology
Malone, J., Holloway, E., Adamusiak, T., Kapushesky, M., Zheng, J., Kolesnikov, N. N., Zhukova, A., Brāzma, A., & Parkinson, H. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq099 · Cited by 571 · see citing works
Open Targets: a platform for therapeutic target identification and validation
Koscielny, G., An, P., Carvalho‐Silva, D., Cham, J. A., Fumis, L., Gasparyan, R., Hasan, S., Karamanis, N., Maguire, M., Papa, E., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw1055 · Cited by 500 · see citing works
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Nguyen, D.-T., Mathias, S., Bologa, C., Brunak, S., Fernandez, N., Gaulton, A., Hersey, A., Holmes, J., Jensen, L. J., Karlsson, A., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1072 · Cited by 279 · see citing works
Self-citationThe EBI RDF platform: linked open data for the life sciences
Jupp, S., MALONE, J. M., Bolleman, J. T., et al. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt765 · Cited by 249 · see citing works
Kinetics for Drug Discovery: an industry-driven effort to target drug residence time.
Schuetz, D., de Witte, W. D., Wong, Y., Knasmueller, B., Richter, L., Kokh, D., Sadiq, S., Bosma, R., Nederpelt, I., Heitman, L., et al. (2017) · Drug Discovery Today · doi:10.1016/j.drudis.2017.02.002 · Cited by 197 · see citing works
Activity, assay and target data curation and quality in the ChEMBL database
Papadatos, G., Gaulton, A., Hersey, A., & Overington, J. P. (2015) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-015-9860-5 · Cited by 169 · see citing works
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Tym, J. E., Mitsopoulos, C., Coker, E. A., Razaz, P., Schierz, A. C., Antolín, A. A., & Al‐Lazikani, B. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1030 · Cited by 135 · see citing works
Sarntivijai, S., Lin, Y., Xiang, Z., Meehan, T. F., Diehl, A. D., Vempati, U. D., Schürer, S. C., Pang, C., MALONE, J. M., Parkinson, H., et al. (2014) · Journal of Biomedical Semantics · doi:10.1186/2041-1480-5-37 · Cited by 127 · see citing works
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Abeyruwan, S. W., Vempati, U. D., Küçük‐McGinty, H., Visser, U., Koleti, A., Mir, A., Sakurai, K., Chung, C., Bittker, J. A., Clemons, P. A., et al. (2014) · Journal of Biomedical Semantics · doi:10.1186/2041-1480-5-s1-s5 · Cited by 98 · see citing works
Nowotka, M., Gaulton, A., Méndez, D., Bento, A. P., Hersey, A., & Leach, A. R. (2017) · PubMed · doi:10.1080/17460441.2017.1339032 · Cited by 66 · see citing works
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Sateriale, A., Bessoff, K. E., Sarkar, I. N., et al. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-001700 · Cited by 30 · see citing works
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Gaulton, A., Kale, N., van Westen, G. J. P., et al. (2015) · Scientific Data · doi:10.1038/sdata.2015.32 · Cited by 25 · see citing works
Self-citationVlot, A. H. C., de Witte, W. E. A., Danhof, M., van der Graaf, P. H., van Westen, G. J. P., & de Lange, E. C. M. (2017) · The AAPS Journal · doi:10.1208/s12248-017-0172-7 · Cited by 18 · see citing works
Many Approved Drugs Have Bioactive Analogs With Different Target Annotations
Hu, Y., Lounkine, E., & Bajorath, J. (2014) · The AAPS Journal · doi:10.1208/s12248-014-9621-8 · Cited by 15 · see citing works
Open Data For Drug Discovery: Learning From The Biological Community
Hersey, A., Senger, S., & Overington, J. P. (2012) · Future Medicinal Chemistry · doi:10.4155/fmc.12.159 · Cited by 14 · see citing works
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“Further, the ChEMBL database (https:// www. ebi. ac. uk/ chembl/) [56] is an open access big database containing data of numerous bioactive compounds exhibiting drug-like properties [57].”
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“For a given target, these can be found in the scienti fi c literature, patent literature or public databases such as IUPHAR/BPS 114 , ChEMBL 115 or ZINC 9,99,116 , or available in-house.”
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- Authors
- Mendez, D., Gaulton, A., Bento, A. P., Chambers, J., De Veij, M., Félix, E., Magariños, M. P., Mosquera, J. F., Mutowo, P., Nowotka, M., Gordillo-Marañón, M., Hunter, F., Junco, L., Mugumbate, G., Rodriguez-Lopez, M., Atkinson, F., Bosc, N., Radoux, C. J., Segura-Cabrera, A., Hersey, A., & Leach, A. R.
- Affiliations
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
- European Bioinformatics Institute (GB)
- Institute of Cardiovascular Science, University College London, Gower Street, London WC1E 6BT, UK
- Corresponding author
- Leach, Andrew R
- ORCID
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-11-06
- Volume
- 47
- Issue
- D1
- Pages
- D930-D940
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC6323927
- OpenAlex
- W2900090807
- Cited by
- 2,372
- References
- 28
- Citations by year
- 2026: 306 · 2025: 391 · 2024: 464 · 2023: 439 · 2022: 314 · 2021: 304 · 2020: 125 · 2019: 26 · 2018: 1
- FWCI
- 84.82
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Wellcome Trust (WT104104/Z/14/Z)
- National Institutes of Health (U54CA189205)
- European Union Seventh Framework Programme (FP7/2007–2013, 602156)
- Innovative Medicines Initiative Joint Undertaking (115002)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- ChEMBL, bioassay data, data deposition
- MeSH terms
- Biological Assay, Periodicals as Topic, User-Computer Interface, Drug Discovery, Databases, Pharmaceutical
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