Data extraction and curation

The core activity data in the ChEMBL database are manually extracted from the full text of peer-reviewed scientific publications in a variety of journals, such as Journal of Medicinal Chemistry, Bioorganic Medicinal Chemistry Letters and Journal of Natural Products. The set of journals covered is by no means comprehensive, but is selected to capture the greatest quantity of high-quality data in a cost, and time-effective manner. From each publication, details of the compounds tested, the assays performed and any target information for these assays are abstracted.

Structures for small molecules are drawn in full, in machine-readable format, despite the structure often being provided as a scaffold and a list of R-group substituents, or referred to only by name in the original publication. Information about the particular salt form tested is also captured, where available, although this is often inconsistent in the literature. Before loading to the database, structures are checked for potential problems (e.g. unusual valence on atoms, incorrect structures for common compounds/drugs), then normalized according to a set of rules, to ensure consistency in representation (e.g. compounds are neutralized by protonating/deprotonating acids and bases to ensure a formal charge of zero where possible). Preferred representations are used for certain common groups (e.g. sugars, sulphoxides and nitroxides). Some chemical structures are typically only reported in an implicit format, and this is checked and assigned on registration—for example, the stereochemistry of the steroid framework is invariably not published, but is assumed to be that of the naturally occurring configuration, unless otherwise defined. Common salts are also stripped from the extracted compounds, and both the salt form and the parent compound are entered into the database. This allows users to view all data associated with the same parent compound, regardless of the salt form tested, while still retaining the salt information if required.

Details of all types of assays performed are extracted from each publication, including binding assays (measuring the interaction of the compound with the target directly), functional assays (often measuring indirect effects of the compound on a pathway, system or whole organism) and ADMET assays (measuring pharmacokinetic properties of the compound, interaction with key metabolic enzymes or toxic effects on cells/tissues). The activity endpoints measured in these assays are recorded with the values and units as given in the paper, but for the purposes of improved querying are also standardized, where possible, to convert them to a preferred unit of measurement for a given activity type (e.g. IC50 values are displayed innM, rather thanµM/mM/M, half-life is reported in hours rather than minutes/days/weeks). This enables the user to more easily compare data across different assays.

To maximize the utility of bioactivity data, the targets of assays need to be represented robustly and consistently, in a manner independent of the various adopted names and synonyms used across different sources. To this end, detailed manual annotation of targets is carried out within ChEMBL. Where the intended molecular target of an assay is reported in a publication, this information is extracted, together with associated details of the relevant organism in which the assay was performed (or the organism from which the protein/cell-line was derived for an in vitro assay). Target assignments are carefully checked by our curators, and corrected where necessary, then further annotated where any ambiguity exists. For example, for an in vitro binding assay, it is often possible to determine the precise protein target with which the compound is interacting and assign a single relevant protein to the assay. However, in other cases this may not be possible. For example, an assay may describe interaction of a compound with a target which is known to be a protein/biomolecular complex (e.g. ribosomes, GABA-A receptors or integrins). In this case, several protein subunits may be assigned to the assay, but a ‘complex’ field in the database is used to record the fact that these proteins are associated as a specific protein complex. In other cases, the assay performed may not allow elucidation of the precise protein subtypes with which a compound is interacting (e.g. cell/tissue-based assays where several closely related subtypes of the protein are likely to be expressed, or those reported prior to the discovery of particular receptor/enzyme subtypes). Again, the assay may therefore be mapped to each of the possible protein targets, but a ‘multi’ field in the database records the fact that it is not clear whether the compound is interacting non-specifically with all of these proteins, and consequently less confidence should be placed in these assignments.

In many cases, such as whole organism-based phenotypic assays, it is not possible to unambiguously determine the protein target that is responsible for the observed effect of the compound. In these cases, the assay will be mapped to a ChEMBL target representing the non-molecular system on which an effect is observed. For example, an assay measuring the cytotoxicity of a compound against the human breast carcinoma-derived MCF-7 cells would be mapped to a ChEMBL cell-line target representing MCF-7. An in vitro assay measuring inhibition of growth of Mycobacterium tuberculosis would be mapped to a ChEMBL organism target representing M. tuberculosis. This allows users to easily retrieve information about other assays performed on the same systems, even though the underlying mechanism of action of the compounds might be different. Protein targets are further classified into a manually curated family hierarchy, according to nomenclature commonly used by drug discovery scientists (e.g. ligand-based classification of G-protein-coupled receptors, division of enzymes into proteases/kinases/phosphatases etc.), and organisms are classified according to a simplified subset of the NCBI taxonomic structure (20). This also allows data to be queried at a higher level (e.g. for all protein kinases or Mycobacterium species).

The ChEMBL interface

The ChEMBL database is accessible via a simple, user-friendly interface at: https://www.ebi.ac.uk/chembldb. This interface allows users to search for compounds, targets or assays of interest in a variety of ways.

For example, users wishing to retrieve potential tool compounds for a target of interest can perform a keyword search of the database using a protein name, synonym, UniProt accession or ChEMBL target identifier of interest. Alternatively, targets can be browsed according to protein family (e.g. to retrieve all chemokine receptors), or organism (e.g. to retrieve all Plasmodium falciparum targets). Since the database only includes protein targets for which bioactivity data are available, users can also perform a BLAST search of the ChEMBL target dictionary with a protein sequence of interest. This can be useful to identify closely related proteins with activity data, even if the sequence of interest is not represented in the database (e.g. activity data for a mouse orthologue of a human target).

Having retrieved a target, or multiple targets, of interest, a simple drop-down menu allows users to display all associated bioactivity data, or to filter the available data to select activity types of interest (for example to include only IC50 and Ki measurements below a given concentration threshold, or only certain ADMET endpoints, see Supplementary Figure 1). The resulting bioactivity table gives details of each compound that was tested (together with the particular salt form used in the assay), the measured activity type, value and units, a description of the assay, details of the target (including the organism) and, importantly, a link to the publication from which the data have been extracted. Data from this view can be exported as a text file or spread sheet for further analysis.

Alternatively, users may have a particular compound of interest and wish to retrieve potency, selectivity or ADMET information for this, or closely related compounds. Again, users can search for compounds using a keyword search with names/synonyms or ChEMBL identifiers. However, a more effective strategy will often be to search by compound structure. The interface provides a choice of several different drawing tools (26), allowing users to sketch in a structure or substructure of interest (Figure 1). A compound similarity or substructure search of the database (implemented using the Accelrys Direct Oracle Cartridge: http://accelrys.com/products/informatics/cheminformatics/accelrys-direct.html) can then be carried out to retrieve ChEMBL compounds similar to, or containing, the input structure.

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Figure 1.

Retrieving bioactivity data with a substructure search. A choice of sketchers allows the user to enter a structure of interest and search the database for compounds similar to, or containing that substructure (a). The resulting list of compounds can then be filtered graphically, according to their physicochemical properties (e.g. calculated lipophilicity AlogP and molecular weight) using the sliders and ‘update chart’ button (b). When a suitable compound set has been created, a drop-down menu allows the user to retrieve all relevant bioactivity results from the database, or filter the results further by activity type (c).

Having retrieved a list of compounds of interest, a variety of calculated properties such as molecular weight, calculated lipophilicity (AlogP, 27) and polar surface area (28) can be viewed and filtered via a graphical display. This may be useful to restrict the set of compounds to those that are likely to have appropriate drug-like properties (29), before retrieving or filtering the associated bioactivity data.

For each of the main data types in ChEMBL (compounds, targets, assays and documents), report card pages are available. These provide further details about the entity of interest, such as names and synonyms (for targets and compounds), journal/article details (for documents), drug annotation, structures and calculated physicochemical properties (for compounds), together with cross-references to other resources (e.g. UniProt, PDBe, ChEBI, DrugBank and CiteXplore: http://www.ebi.ac.uk/citexplore). Each report card also contains a series of clickable graphical ‘widgets’ summarizing and providing rapid access to all of the bioactivity data available for that entity (Figure 2).

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Figure 2.

Compound report card for Fingolimod (CHEMBL314854) showing synonyms, approved drug features (see Supplementary Figure 2), a link to retrieve clinical trial data, calculated compound properties and structure representations, and different salt forms of the molecule (in this case, a hydrochloride salt). The lower portion of the page has a series of clickable widgets, showing breakdown of the activity data for this compound by activity type (e.g. IC50, EC50), assay type (e.g. binding/functional/ADMET) or target type (e.g. enzyme, receptor). Clicking on a portion of one of the pie charts takes the user directly to the relevant bioactivity results.

A table view of approved drugs is also provided, with relevant annotation (e.g. drug type, administration route, ‘black box’ safety warnings) indicated by a series of sortable icons (see Supplementary Figure 2). Users can download the structures for these drugs or go to report cards to access further information, such as bioactivity data.