The ChEMBL bioactivity database: an update

By A. P. Bento, Anna Gaulton, Anne Hersey, L. Bellis, Jon Chambers, M. Davies, Felix A. Krüger, Yvonne Light, L. Mak, S. McGlinchey and 4 more. 2013

1,420 citations recorded by the source corpus.

ChEMBL is an open large-scale bioactivity database (https://www.ebi.ac.uk/chembl), previously described in the 2012 Nucleic Acids Research Database Issue. Since then, a variety of new data sources and improvements in functionality have contributed to the growth and utility of the resource. In particular, more comprehensive tracking of compounds from research stages through clinical development to market is provided through the inclusion of data from United States Adopted Name applications; a new richer data model for representing drug targets has been developed; and a number of methods have been put in place to allow users to more easily identify reliable data. Finally, access to ChEMBL is now available via a new Resource Description Framework format, in addition to the web-based interface, data downloads and web services.

Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology, Computational Biology & Bioinformatics, Computational Chemistry & Drug Discovery

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Citations in the corpus

PubChem Substance and Compound databases (2015, background): “For instance, ChEMBL contributes their bioactivity data to PubChem, and at the same time, a small portion of PubChem’s bioactivity data (from confirmatory and panel assays with dose-response endpoints) are integrated into ChEMBL.”
AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery (2015, methodology): “That work was followed by Untherhiner et al. [11, 12] and Ramsundar et al. [13] that demonstrated the MT-DNN technique scales to large biochemical databases such as PubChem Bioassays [14] and ChEMBL [15].”
A Structure-Based Drug Discovery Paradigm (2019, methodology): “Statistically, a DNN outperforms other ML models such as SVM [120], RF, and others when applied to seven datasets selected from ChEMBL database [121].”
Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery (2018, methodology): “The model [15] was trained to predict bioactivities of about 6 000 assays available in three major drug discovery databases (ChEMBL [2], ZINC [11], PubChem [24]).”
Open Targets: a platform for therapeutic target identification and validation (2016, background): “Database icons are clockwise from bottom left IntOGen (32), Cancer Gene Census (31), Europe PMC (21), ChEMBL (49), PhenoDigm (50), GWAS Catalog (51), European Variation Archive (http://www.”
5-HT2C Receptor Structures Reveal the Structural Basis of GPCR Polypharmacology (2018, methodology): “ChEMBL Privileged Structure Search All ligands with Ki < 1 mM on any class A GPCR was retrieved from the ChEMBL (Bento et al., 2014) database (release CHEMBL22, https://doi.”
Machine learning approaches and databases for prediction of drug–target interaction: a survey paper (2020, methodology): “The data stored in the ChEMBL database [237–239] were manually extracted from published literatures.”
UniChem: extension of InChI-based compound mapping to salt, connectivity and stereochemistry layers (2014, methodology): “For example, the ChEMBL resource [12-14] uses just such a web service query to alert users to alternative molecular forms of ChEMBL compounds that exist else-where.”
GPCRdb in 2018: adding GPCR structure models and ligands (2017, methodology): “The most comprehensive resource for biological activities is the ChEMBL database, which has compiled data from publications, patents and commercial sources (11).”