ChEMBL web services: streamlining access to drug discovery data and utilities

By M. Davies, M. Nowotka, G. Papadatos, Nathan Dedman, Anna Gaulton, Francis Atkinson, L. Bellis, John P. Overington. 2015

644 citations recorded by the source corpus.

ChEMBL is now a well-established resource in the fields of drug discovery and medicinal chemistry research. The ChEMBL database curates and stores standardized bioactivity, molecule, target and drug data extracted from multiple sources, including the primary medicinal chemistry literature. Programmatic access to ChEMBL data has been improved by a recent update to the ChEMBL web services (version 2.0.x, https://www.ebi.ac.uk/chembl/api/data/docs), which exposes significantly more data from the underlying database and introduces new functionality. To complement the data-focused services, a utility service (version 1.0.x, https://www.ebi.ac.uk/chembl/api/utils/docs), which provides RESTful access to commonly used cheminformatics methods, has also been concurrently developed. The ChEMBL web services can be used together or independently to build applications and data processing workflows relevant to drug discovery and chemical biology.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

GPCRdb in 2021: integrating GPCR sequence, structure and function (2020, methodology): “All GPCR ligands and their biological activities were imported from ChEMBL version 27 (21) using the ChEMBL web services (22) which were queried using ChEMBL identifiers.”
ChEMBL: towards direct deposition of bioassay data (2018, methodology): “For users preferring to access ChEMBL programmatically, a comprehensive set of RESTful web services are provided (24,25), allowing retrieval of ChEMBL data in XML, JSON and YAML formats.”
KLIFS: a structural kinase-ligand interaction database (2015, positive): “Fourth, kinase records in ChEMBL (Ki or IC50, confidence≥8) for the bound ligand are listed if present.”
WormBase 2016: expanding to enable helminth genomic research (2015, background): “On the specific issue of human disease caused by parasitic worms, we plan to augment WormBase ParaSite with additional features for the identification of putative targets for anti-helminthic drugs, for example by linking gene products to the ChEMBL (44) database of medicinal chemistry.”
The ChEMBL database in 2017 (2016, methodology): “For users requiring programmatic access to ChEMBL, a comprehensive set of RESTful web services are provided (51), allowing retrieval of ChEMBL data in XML, JSON and YAML formats (see https://www.”
Mapping the Landscape of Artificial Intelligence Applications against COVID-19 (2020, methodology): “The model is ﬁrst trained on 1.6 million molecules from the ChEMBL database (Davies et al., 2015; Mendez et al., 2019), and then adapted to a smaller dataset of protease inhibitors using transfer learning.”
The BioGRID interaction database: 2017 update (2016, background): “We surveyed the content of the major specialized chemical interaction databases, including DrugBank (37), HMdb (61), T3DB (62), BindingDB (63), CTD (64), Therapeutic Target DB (65), ChemBank (66), PharmGKB (67), DGIdb (68), PubChem (69) and ChEMBL (70) to determine the shared fields housed in each o”
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development (2021, methodology): “TDC.ChEMBL: ChEMBL is a manually curated database of bioactive molecules with drug-like properties (Mendez et al. 2019, Davies et al. 2015).”