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.

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.

SureChEMBL is a publicly available large-scale resource containing compounds extracted from the full text, images and attachments of patent documents. The data are extracted from the patent literature according to an automated text and image-mining pipeline on a daily basis. SureChEMBL provides access to a previously unavailable, open and timely set of annotated compound-patent associations, complemented with sophisticated combined structure and keyword-based search capabilities against the compound repository and patent document corpus; given the wealth of knowledge hidden in patent documents, analysis of SureChEMBL data has immediate applications in drug discovery, medicinal chemistry and other commercial areas of chemical science. Currently, the database contains 17 million compounds extracted from 14 million patent documents. Access is available through a dedicated web-based interface and data downloads at: https://www.surechembl.org/.

BackgroundThe process of discovering new drugs is a lengthy, time-consuming and expensive process. Modern day drug discovery relies heavily on the rapid identification of novel ‘targets’, usually proteins that can be modulated by small molecule drugs to cure or minimise the effects of a disease. Of the 20,000 proteins currently reported as comprising the human proteome, just under a quarter of these can potentially be modulated by known small molecules Storing information in curated, actively maintained drug discovery databases can help researchers access current drug discovery information quickly. However with the increase in the amount of data generated from both experimental and in silico efforts, databases can become very large very quickly and information retrieval from them can become a challenge. The development of database tools that facilitate rapid information retrieval is important to keep up with the growth of databases.DescriptionWe have developed a Gene Ontology-based navigation tool (Gene Ontology Tree) to help users retrieve biological information to single protein targets in the ChEMBL drug discovery database. 99 % of single protein targets in ChEMBL have at least one GO annotation associated with them. There are 12,500 GO terms associated to 6200 protein targets in the ChEMBL database resulting in a total of 140,000 annotations. The slim we have created, the ‘ChEMBL protein target slim’ allows broad categorisation of the biology of 90 % of the protein targets using just 300 high level, informative GO terms.We used the GO slim method of assigning fewer higher level GO groupings to numerous very specific lower level terms derived from the GOA to describe a set of GO terms relevant to proteins in ChEMBL. We then used the slim created to provide a web based tool that allows a quick and easy navigation of protein target space. Terms from the GO are used to capture information on protein molecular function, biological process and subcellular localisations. The ChEMBL database also provides compound information for small molecules that have been tested for their effects on these protein targets. The ‘ChEMBL protein target slim’ provides a means of firstly describing the biology of protein drug targets and secondly allows users to easily establish a connection between biological and chemical information regarding drugs and drug targets in ChEMBL.The ‘ChEMBL protein target slim’ is available as a browsable ‘Gene Ontology Tree’ on the ChEMBL site under the browse targets tab (https://www.ebi.ac.uk/chembl/target/browser). A ChEMBL protein target slim OBO file containing the GO slim terms pertinent to ChEMBL is available from the GOC website (http://geneontology.org/page/go-slim-and-subset-guide).ConclusionsWe have created a protein target navigation tool based on the ‘ChEMBL protein target slim’. The ‘ChEMBL protein target slim’ provides a way of browsing protein targets in ChEMBL using high level GO terms that describe the molecular functions, processes and subcellular localisations of protein drug target...