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.; Overington, J. P.

doi:10.1093/nar/gkr777

Cited by 4,382Computational Drug Discovery MethodsBiomedical Text Mining and OntologiesBioinformatics and Genomic Networks

ChEMBL: a large-scale bioactivity database for drug discovery

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Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S. +3 more·2012·Nucleic Acids Research

DOI 10.1093/nar/gkr777PMC PMC3245175

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Abstract

ChEMBL is an Open Data database containing binding, functional and ADMET information for a large number of drug-like bioactive compounds. These data are manually abstracted from the primary published literature on a regular basis, then further curated and standardized to maximize their quality and utility across a wide range of chemical biology and drug-discovery research problems. Currently, the database contains 5.4 million bioactivity measurements for more than 1 million compounds and 5200 protein targets. Access is available through a web-based interface, data downloads and web services at: https://www.ebi.ac.uk/chembldb.

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The last ten years have seen a dramatic increase in the amount of public chemical and biological data in PubChem, ChEMBL, and other databases that include screening data.
“The last ten years have seen a dramatic increase in the amount of public chemical and biological data in PubChem16, ChEMBL17, and other databases that include screening data.”
From Exploiting machine learning for end-to-end drug discovery and development (2019) · cites this paper as [17]
Outside of the pharmaceutical industry, the availability of large volumes of published or otherwise public domain data in databases like ChEMBL and PubChem has transformed the field.
“Outside of the pharmaceutical industry, the availability of large volumes of published, or otherwise public domain data in databases like ChEMBL29 and PubChem 30 has transformed the field.”
From QSAR without borders (2020) · cites this paper as [29]

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Authors: Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., & Overington, J. P.
Affiliations: European Bioinformatics Institute (GB)
Corresponding author: Overington, J. P.
ORCID: Gaulton, A.
Bellis, L. J.
Bento, A. P.
Davies, M.
Hersey, A.
Michalovich, D.
Overington, J. P.
Venue: Nucleic Acids Research
Publisher: Oxford University Press (OUP)
Published: 2011-09-23
Volume: 40
Issue: D1
Pages: D1100-D1107
ISSN: 0305-1048 (ISSN-L)
1362-4962
DOI: 10.1093/nar/gkr777
PMC: PMC3245175
OpenAlex: W2096541451
Cited by: 4,382
References: 40
Citations by year: 2026: 379 · 2025: 455 · 2024: 421 · 2023: 351 · 2022: 276 · 2021: 330 · 2020: 286 · 2019: 275 · 2018: 233 · 2017: 229 · 2016: 241 · 2015: 269 · 2014: 257 · 2013: 251 · 2012: 109
FWCI: 193.90
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by-nc
creativecommons.org/licenses/by-nc/3.0 (vor, 2058 d)
Locations: Nucleic Acids Research (publishedVersion, cc-by-nc)
PubMed Central (publishedVersion, cc-by-nc)
PubMed (publishedVersion)
HighWire Press Open Archive (submittedVersion)
Funders: Wellcome Trust (086151, 086151/Z/08/Z)
European Molecular Biology Laboratory
Cancer Research UK (11566)
Engineering and Physical Sciences Research Council (EP/I037229/1)
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 › Bioinformatics and Genomic Networks
Keywords: ChEMBL, drug discovery, functional activity, ADMET properties, protein targets, drug-like molecules, literature curation, chemical biology
MeSH terms: Pharmaceutical Preparations, Humans, Proteins, User-Computer Interface, Databases, Factual, Databases, Protein, Drug Discovery

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