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 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., 

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). 




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.

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

The ChEMBL Team


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

Popular posts from this blog

Target predictions in the browser with RDKit MinimalLib (JS) and ONNX.js

Some time ago we showed an example of how a model trained in Python's PyTorch could be run in a C++ backend by exporting it to the ONNX format.  Greg also showed us in his blogpost how our multitask neural network model could be used in a very nice KNIME workflow by exporting it to ONNX. That was possible thanks to RDKit's Java bindings and the ONNX Java runtime. As a refresher, most of the most popular machine learning frameworks can export their models to this format and many programming languages can load them to run the predictions. This certainly is a beautiful example of interoperability! In November 2019 RDKit introduced a reduced functionality Javascript library which is able to do all we need in order to use our multitask model in the browser. So, the only thing that was left to do was to combine these two awesome tools... and we did it! Here is our demo with its available source code . Start typing a smiles into the box and enjoy! Updated code to generate the m

Identifying relevant compounds in patents

  As you may know, patents can be inherently noisy documents which can make it challenging to extract drug discovery information from them, such as the key targets or compounds being claimed. There are many reasons for this, ranging from deliberate obfuscation through to the long and detailed nature of the documents. For example, a typical small molecule patent may contain extensive background information relating to the target biology and disease area, chemical synthesis information, biological assay protocols and pharmacological measurements (which may refer to endogenous substances, existing therapies, reaction intermediates, reagents and reference compounds), in addition to description of the claimed compounds themselves.  The SureChEMBL system extracts this chemical information from patent documents through recognition of chemical names, conversion of images and extraction of attached files, and allows patents to be searched for chemical structures of interest. However, the curren

This Python InChI Key resolver will blow your mind

This scientific clickbait title introduces our promised blog post about the integration of UniChem into our ChEMBL python client. UniChem is a very important resource, as it contains information about 134 million (and counting) unique compound structures and cross references between various chemistry resources. Since UniChem is developed in-house and provides its own web services , we thought it would make sense to integrate it with our python client library . Before we present a systematic translation between raw HTTP calls described in the UniChem API documentation and client calls, let us provide some preliminary information: In order to install the client, you should use pip : pip install -U chembl_webresource_client Once you have it installed, you can import the unichem module: from chembl_webresource_client.unichem import unichem_client as unichem OK, so how to resolve an InChI Key to InChI string? It's very simple: Of course in order to reso

ChEMBL 28 Released!

  We are pleased to announce the release of ChEMBL_28. This version of the database, prepared on 15/01/2021 contains: * 2,680,904 compound records * 2,086,898 compounds (of which 2,066,376 have mol files) * 17,276,334 activities * 1,358,549 assays * 14,347 targets * 80,480 documents Data can be downloaded from the ChEMBL FTP site: . Please see ChEMBL_26 release notes for full details of all changes in this release: DATA CHANGES SINCE THE LAST RELEASE This release includes several new deposited data sets: Donated Chemical Probes data from SGC Frankfurt (src_id = 54) SARS-CoV-2 screening data from the Fraunhofer Institute (src_id = 52) Antimicrobial screening data sets from CO-ADD (src_id = 40) Plasmodium screening data from the UCSD Winzeler lab (src_id = 51) MMV pathogen box screening data (src_id = 34) Curated data

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