The ChEMBL database in 2017

By Anna Gaulton, Anne Hersey, M. Nowotka, A. P. Bento, Jon Chambers, David Mendez, Prudence Mutowo-Meullenet, Francis Atkinson, L. Bellis, Elena Cibrián-Uhalte and 8 more. 2016

2,134 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 and 2014 Nucleic Acids Research Database Issues. Since then, alongside the continued extraction of data from the medicinal chemistry literature, new sources of bioactivity data have also been added to the database. These include: deposited data sets from neglected disease screening; crop protection data; drug metabolism and disposition data and bioactivity data from patents. A number of improvements and new features have also been incorporated. These include the annotation of assays and targets using ontologies, the inclusion of targets and indications for clinical candidates, addition of metabolic pathways for drugs and calculation of structural alerts. The ChEMBL data can be accessed via a web-interface, RDF distribution, data downloads and RESTful web-services.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

Ligand biological activity predicted by cleaning positive and negative chemical correlations (2019, background): “1 shows that the eigenvalue distribution of 200 random molecules drawn from ChEMBL (32) indeed agrees with the random matrix null distribution (Eq.”
RCSB Protein Data Bank (RCSB.org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning (2022, background): “ChEMBL (91) Manually curated database of bioactive molecules with”
Rethinking drug design in the artificial intelligence era (2019, background): “Over the past two decades, we have seen the appearance of a number of public databases containing millions of biological assay results, such as ChEMBL 54 and PubChem 55 , which can provide input for machine learning models predicting a variety of biological activities or physical properties for drug”
Review on natural products databases: where to find data in 2020 (2020, background): “It contains over 3500 NPs, although, similarly to ChEMBL, this number is very underestimated due to the unclear labelling of compounds as NPs. ChemSpider is a chemical database offering very rich metadata, cross-references to a lot of other chemical sources and advanced search.”
SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules (2019, methodology): “This set relies on the bioactivity data of ChEMBL version 23 (14,15) and thus describes more molecules and more targets than the old version (based on ChEMBL version 16).”
Empowering Biomedical Discovery with AI Agents (2024, positive): “Molecular search engines retrieve information from these databases [39–41].”
PDBe: improved findability of macromolecular structure data in the PDB (2019, background): “◦ Cross-references to DrugBank (including UniProtKB accessions for drug targets), ChEBI (22), ChEMBL (21) and CSD (37).”
PubChem 2019 update: improved access to chemical data (2018, background): “The Target View page also presents known drugs and curated ligands, collected from ChEMBL (9), DrugBank (10) and IUPHAR / BPS Guide to PHARMACOLOGY (11).”
DeepDock: Enhancing Ligand-protein Interaction Prediction by a Combination of Ligand and Structure Information (2019, background): “On the other hand, the model without ligand information ranked an active ligand (ChEMBL ID: 421947) as the 151st , while our model ranked it as the 24th.”