Skip to main content

ChEMBL_18 Released




We are pleased to announce the release of ChEMBL_18. This version of the database was prepared on 12th March 2014 and contains:
  • 1,566,466 compound records
  • 1,359,508 compounds (of which 1,352,681 have mol files)
  • 12,419,715 activities
  • 1,042,374 assays
  • 9,414 targets
  • 53,298 documents

The web front end at https://www.ebi.ac.uk/chembl is now connected to the ChEMBL 18 data, but you can also download the data from the ChEMBL ftpsite. Please see ChEMBL_18 release notes for full details of all changes in this release.

Changes since the last release

 

New data sets

 

The ChEMBL_18 release includes the following new datasets:
  • University of Vienna G-glycoprotein (pgp) screening data
  • UCSF MMV Malaria Box screening data
  • DNDi Trypanosoma cruzi screening data
  • DrugMatrix in vivo toxicology data
In addition, 43,335 new compound records from 2015 publications in the primary literature have been added to this release. Approved drug and usan data have also been updated, with 103 new structures added.

 

Updates to the protein family classification

 

A review and update of the ChEMBL protein family classification has been carried out. The main changes are listed below:

  • New ion channel/transporter classification, based on the BPS classification
  • New epigenetic protein classification, based on SGC/ChromoHub classification
  • Modification of kinase classification, to follow Human Kinome classification

 

Assay classification and ontology mapping

 

The following annotations and classifications have been added to the ChEMBL assay data:
  • Classification of assay format (e.g., biochemical, cell-based, organism-based) using BioAssay Ontology
  • Classification of endpoints (e.g., IC50, AUC, Ki) using BioAssay Ontology
  • Addition of Physicochemical and Toxicity assay type classification
  • Mapping of assay cell-lines to CLO, EFO and Cellosaurus
  • Mapping of standard units to Unit Ontology and QUDT

 

 

Capture of assay parameters

 

A new table in the database (assay_parameters), is used to capture additional properties of assays such as dose, administration route, time points. These additional parameters are displayed on the Assay Report Card.

 

Target predictions

 

Bioactivity data for single protein targets in ChEMBL have been used to train and validate two Naive Bayesian multi-label classifier models (at <= 1uM and <= 10uM bioactivity cutoffs respectively). These models have been subsequently employed to predict biological targets for a set of approved drugs, which are displayed on in the new Target Predictions section of the Compound Report Card, where applicable. Since some of the predictions correspond to compound/target pairs that were included in the training set for the models, these are shown in white, to distinguish them from genuine predictions (coloured light yellow). Only predictions scoring >= 0.2 are included in the result tables. The models were built with open source tools such as RDKit and scikit-learn and are available upon request.



We would appreciate any feedback on this feature, and any further ideas you may have on including predicted data on top of ChEMBL experimental data.

 

UniChem connectivity mapping

 

In addition to the standard UniChem cross-references shown on the report card (based on exact InChI Key matching), a new link is included to an expanded view of UniChem cross-references, generated based on InChI connectivity layer matching (e.g.,

https://www.ebi.ac.uk/chembl/compound/unichem_connectivity/GJJFMKBJSRMPLA-DZGCQCFKSA-N). 

This expanded view shows any compounds in UniChem that share the same connectivity as the query structure, even if they have stereochemical, isotopic or protonation state differences. The differences between the query and retrieved structures are shown by their position in the table: the first column shows compounds that match in all InChI layers, while the subsequent columns show those structures that differ in stereochemistry (s column), isotope (i column), protonation state (p column), or various combinations of these layers (final four columns). A button at the top of the table gives the additional option to retrieve compounds that match individual components of a mixture or salt. Where the query structure consists of multiple components, matches to each of these components will be coloured different colours (e.g., black, blue, red). 

 

ChEMBL RDF Update

 

The ChEMBL RDF data model has been enhanced and now includes the following information:
  • Drug mechanism of action and binding site information
  • Molecule hierarchy
  • Target relationships
  • Assay format
  • Cell-line information
More information (documentation, SPARQL endpoint and example queries), about the RDF version of the ChEMBL database can be found on the EBI-RDF Platform and you can download the RDF files from the ChEMBL ftpsite.

 

Web Service Update

 

Three new Web Service calls focused on approved drugs, mechanism of action and compound forms are now available. Example calls to these methods can be seen below and also please visit the ChEMBL Web Service page for more details.


http://www.ebi.ac.uk/chemblws/targets/CHEMBL1824/approvedDrug.json
http://www.ebi.ac.uk/chemblws/compounds/CHEMBL1642/drugMechanism.json
http://www.ebi.ac.uk/chemblws/compounds/CHEMBL278020/form.json



As always, we greatly appreciate to reporting of any omissions or errors.

The ChEMBL Team

Comments

Unknown said…
Nice work on the predictions. But how exactly are they done? I'm curious as to which features are used, and what are the performance statistics at various posterior probability cutoffs.
Unknown said…
Hi David,
For more info see http://chembl.blogspot.co.uk/2014/04/ligand-based-target-predictions-in.html

Popular posts from this blog

ChEMBL & SureChEMBL anniversary symposium

  In 2024 we celebrate the 15th anniversary of the first public release of the ChEMBL database as well as the 10th anniversary of SureChEMBL. To recognise this important landmark we are organising a two-day symposium to celebrate the work achieved by ChEMBL and SureChEMBL, and look forward to its future.   Save the date for the ChEMBL 15 Year Symposium October 1-2, 2024     Day one will consist of four workshops, a basic ChEMBL drug design workshop; an advanced ChEMBL workshop (EUbOPEN community workshop); a ChEMBL data deposition workshop; and a SureChEMBL workshop. Day two will consist of a series of talks from invited speakers, a few poster flash talks, a local nature walk, as well as celebratory cake. During the breaks, the poster session will be a great opportunity to catch up with other users and collaborators of the ChEMBL resources and chat to colleagues, co-workers and others to find out more about how the database is being used. Lunch and refreshments will be pro

ChEMBL 34 is out!

We are delighted to announce the release of ChEMBL 34, which includes a full update to drug and clinical candidate drug data. This version of the database, prepared on 28/03/2024 contains:         2,431,025 compounds (of which 2,409,270 have mol files)         3,106,257 compound records (non-unique compounds)         20,772,701 activities         1,644,390 assays         15,598 targets         89,892 documents Data can be downloaded from the ChEMBL FTP site:  https://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/releases/chembl_34/ Please see ChEMBL_34 release notes for full details of all changes in this release:  https://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/releases/chembl_34/chembl_34_release_notes.txt New Data Sources European Medicines Agency (src_id = 66): European Medicines Agency's data correspond to EMA drugs prior to 20 January 2023 (excluding vaccines). 71 out of the 882 newly added EMA drugs are only authorised by EMA, rather than from other regulatory bodies e.g.

A python client for accessing ChEMBL web services

Motivation The CheMBL Web Services provide simple reliable programmatic access to the data stored in ChEMBL database. RESTful API approaches are quite easy to master in most languages but still require writing a few lines of code. Additionally, it can be a challenging task to write a nontrivial application using REST without any examples. These factors were the motivation for us to write a small client library for accessing web services from Python. Why Python? We choose this language because Python has become extremely popular (and still growing in use) in scientific applications; there are several Open Source chemical toolkits available in this language, and so the wealth of ChEMBL resources and functionality of those toolkits can be easily combined. Moreover, Python is a very web-friendly language and we wanted to show how easy complex resource acquisition can be expressed in Python. Reinventing the wheel? There are already some libraries providing access to ChEMBL d

Accessing SureChEMBL data in bulk

It is the peak of the summer (at least in this hemisphere) and many of our readers/users will be on holiday, perhaps on an island enjoying the sea. Luckily, for the rest of us there is still the 'sea' of SureChEMBL data that awaits to be enjoyed and explored for hidden 'treasures' (let me know if I pushed this analogy too far). See here and  here for a reminder of SureChEMBL is and what it does.  This wealth of (big) data can be accessed via the SureChEMBL interface , where users can submit quite sophisticated and granular queries by combining: i) Lucene fields against full-text and bibliographic metadata and ii) advanced structure query features against the annotated compound corpus. Examples of such queries will be the topic of a future post. Once the search results are back, users can browse through and export the chemistry from the patent(s) of interest. In addition to this functionality, we've been receiving user requests for  local (behind the