Skip to main content

ChEMBL 21 Released


We are pleased to announce the release of ChEMBL_21. This version of the database was prepared on 1st February 2016 and contains:

• 1,929,473 compound records
• 1,592,191 compounds (of which 1,583,897 have mol files)
• 13,968,617 activities
• 1,212,831 assays
• 11,019 targets
• 62,502 source documents

Data can be downloaded from the ChEMBL ftpsite or viewed via the ChEMBL interface. Please see ChEMBL_21 release notes for full details of all changes in this release.


CHANGES SINCE THE LAST RELEASE

In addition to the regular updates to the Scientific Literature, PubChem, FDA Orange Book and USP Dictionary of USAN and INN Investigational Drug Names this release of ChEMBL also includes the following new data:

* Data Depositions

Eight new deposited data sets have been included in ChEMBL_21. These include HepG2 cell viability data for the Gates Library Compound Collection from the University of Dundee, three depositions from groups screening the MMV Malaria Box (from TropIQ, Netherlands; Mahidol University, Thailand and Keele University, United Kingdom), anti-protozoal screening data from DNDi, anti-kinetoplastid screening data from GSK Tres Cantos, Leishmania screening data from St. Jude Children’s Hospital and carnitine palmitoyltransferase modulator screening data from Roche.

* Clinical Candidates

We have added >900 additional compounds in clinical development (phase I-III) to ChEMBL. These candidates mainly cover kinase, GPCR and nuclear hormone receptor targets. For each of these new candidates, we have curated likely efficacy targets (from a variety of sources such as scientific literature and pharmaceutical company pipeline documents) and added this information to the database. We have also updated the highest known development phase for compounds already in ChEMBL (such as monoclonal antibodies and compounds with USAN applications). We will continue to add further clinical candidate and target information in future releases (including targets for the existing monoclonal antibody candidates, and candidates for ion channel targets). Mechanism of action information for clinical candidates has been added to the 'Browse Drug Targets' tab on the ChEMBL interface, as well as the Compound and Target Report Cards:


* Drug Indications

We have identified indications for FDA approved drugs from a number of sources including Prescribing Information, ClinicalTrials.gov and the WHO ATC classification and mapped these to both MeSH disease identifiers and Experimental Factor Ontology disease identifiers. We will add further indications for compounds in clinical development in future ChEMBL releases. Drug indications can be viewed on Compound Report Cards and also on the 'Browse Drug Indications' tab:



* Drug Metabolism and Pharmacokinetic data

We have extracted drug metabolism and pharmacokinetic (PK) data from a number of data sources:
- Curated Drug Metabolism Pathways from a variety of literature/reference sources
- FDA Drug Approval Packages
- Drug Metabolism and Disposition Journal

Experimental assay data from these sources can be viewed via the Compound and Assay Report Cards and Bioactivity Summary views as usual. Further interface enhancements will be included in the near future to allow browsing of drug metabolism pathway data.

* GO Drug Target Slim:

We have created a Gene Ontology slim (GO slim) containing a subset of Gene Ontology terms that are well represented in protein targets from the ChEMBL database (see http://geneontology.org/page/go-slim-and-subset-guide and http://www.geneontology.org/ontology/subsets/goslim_chembl.obo). Targets can now be browsed by GO slim terms on the interface:



* HELM Notation

HELM Notations for monoclonal antibodies have been generated by Stefan Klostermann and team at Roche Diagnostics, and added to the database. In addition, HELM Notations have been generated for ~1100 new peptides that have been added to the ChEMBL_21 release.

* Improved Organism Classification

The organism classification has been enhanced for plant, insect and fungal targets to facilitate retrieval of crop protection data from the database. 


* RDF Update

ChEMBL 21 RDF files have been updated and can be downloaded from the ChEMBL-RDF ftpsite. The EBI-RDF Platform will be updated with the ChEMBL 21 RDF shortly.


We recommend you review the ChEMBL_21 release notes for a comprehensive overview of all updates and changes in ChEMBL 21, including schema changes, and as always, we greatly appreciate the reporting of any omissions or errors.

Keep an eye on the ChEMBL twitter and blog accounts for news and updates.

The ChEMBL Team






Comments

Unknown said…
This comment has been removed by the author.

Popular posts from this blog

UniChem 2.0

UniChem new beta interface and web services We are excited to announce that our UniChem beta site will become the default one on the 11th of May. The new system will allow us to better maintain UniChem and to bring new functionality in a more sustainable way. The current interface and web services will still be reachable for a period of time at https://www.ebi.ac.uk/unichem/legacy . In addition to it, the most popular legacy REST endpoints will also remain implemented in the new web services: https://www.ebi.ac.uk/unichem/api/docs#/Legacy Some downtime is expected during the swap.  What's new? UniChem’s current API and web application is implemented with a framework version that’s not maintained and the cost of updating it surpasses the cost of rebuilding it. In order to improve stability, security, and support the implementation and fast delivery of new features, we have decided to revamp our user-facing systems using the latest version of widely used and maintained frameworks, i

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

LSH-based similarity search in MongoDB is faster than postgres cartridge.

TL;DR: In his excellent blog post , Matt Swain described the implementation of compound similarity searches in MongoDB . Unfortunately, Matt's approach had suboptimal ( polynomial ) time complexity with respect to decreasing similarity thresholds, which renders unsuitable for production environments. In this article, we improve on the method by enhancing it with Locality Sensitive Hashing algorithm, which significantly reduces query time and outperforms RDKit PostgreSQL cartridge . myChEMBL 21 - NoSQL edition    Given that NoSQL technologies applied to computational chemistry and cheminformatics are gaining traction and popularity, we decided to include a taster in future myChEMBL releases. Two especially appealing technologies are Neo4j and MongoDB . The former is a graph database and the latter is a BSON document storage. We would like to provide IPython notebook -based tutorials explaining how to use this software to deal with common cheminformatics p

ChEMBL 30 released

  We are pleased to announce the release of ChEMBL 30. This version of the database, prepared on 22/02/2022 contains: 2,786,911 compound records 2,157,379 compounds (of which 2,136,187 have mol files) 19,286,751 activities 1,458,215 assays 14,855 targets 84,092 documents Data can be downloaded from the ChEMBL FTP site: https://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/releases/chembl_30/ Please see ChEMBL_30 release notes for full details of all changes in this release:  https://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/releases/chembl_30/chembl_30_release_notes.txt New Deposited Datasets EUbOPEN Chemogenomic Library (src_id = 55, ChEMBL Document ID CHEMBL4689842):   The EUbOPEN consortium is an Innovative Medicines Initiative (IMI) funded project to enable and unlock biology in the open. The aims of the project are to assemble an open access chemogenomic library comprising about 5,000 well annotated compounds covering roughly 1,000 different proteins, to synthesize at least

Multi-task neural network on ChEMBL with PyTorch 1.0 and RDKit

  Update: KNIME protocol with the model available thanks to Greg Landrum. Update: New code to train the model and ONNX exported trained models available in github . The use and application of multi-task neural networks is growing rapidly in cheminformatics and drug discovery. Examples can be found in the following publications: - Deep Learning as an Opportunity in VirtualScreening - Massively Multitask Networks for Drug Discovery - Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set But what is a multi-task neural network? In short, it's a kind of neural network architecture that can optimise multiple classification/regression problems at the same time while taking advantage of their shared description. This blogpost gives a great overview of their architecture. All networks in references above implement the hard parameter sharing approach. So, having a set of activities relating targets and molecules we can tra