Skip to main content

ChEMBL 26 Released



We are pleased to announce the release of ChEMBL_26

This version of the database, prepared on 10/01/2020 contains:

  • 2,425,876 compound records
  • 1,950,765 compounds (of which 1,940,733 have mol files)
  • 15,996,368 activities
  • 1,221,311 assays
  • 13,377 targets
  • 76,076 documents
You can query the ChEMBL 26 data online via the ChEMBL Interface and you can also download the data from the ChEMBL FTP site. Please see ChEMBL_26 release notes for full details of all changes in this release.

Changes since the last release:

* Deposited Data Sets:

CO-ADD antimicrobial screening data:
Two new data sets have been included from the Community for Open Access Drug Discovery (CO-ADD). These data sets are screening of the NIH NCI Natural Product Set III in the CO-ADD assays (src_id = 40, Document ChEMBL_ID = CHEMBL4296183, DOI = 10.6019/CHEMBL4296183) and screening of the NIH NCI Diversity Set V in the CO-ADD assays (src_id = 40, Document ChEMBL_ID = CHEMBL4296182, DOI = 10.6019/CHEMBL4296182).

HESI - Evaluation of the utility of stem-cell derived cardiomyocytes for drug proarrhythmic potential (src_id = 49, Document ChEMBL_ID = CHEMBL4295262 , DOI = 10.6019/CHEMBL4295262). Summary assay results for this data set have been included in ChEMBL_26 and further supplementary data will be added in ChEMBL_27.

* Changes to structure-processing and compound properties:
We are now using RDKit for almost all of our compound-related processing. For the first time in ChEMBL_26, this will include compound standardization, salt-stripping, generation of canonical smiles, structural alerts, image depiction, substructure searches and similarity searches (via FPSim2: https://github.com/chembl/FPSim2). Therefore, all molecules have been reprocessed and you may notice some differences in molfiles, smiles and structure search results compared with previous releases. The ChEMBL structure curation pipeline has been released as an open source package: https://github.com/chembl/ChEMBL_Structure_Pipeline, and incorporated into our Beaker web services (see below). More information can be found here: http://chembl.blogspot.com/2020/02/chembl-compound-curation-pipeline.html.

We are also now using ChemAxon tools to calculate most acidic and basic pKa, logP and logD (pH 7.4) predictions, rather than ACDLabs software. These properties have therefore been recalculated and renamed in the database.

* Target Predictions:
Target predictions in ChEMBL are now generated by a new method, using conformal prediction (https://jcheminf.biomedcentral.com/articles/10.1186/s13321-018-0325-4). A docker image is available for those wishing to use the models locally: https://hub.docker.com/repository/docker/chembl/mcp (see https://chembl.blogspot.com/2020/01/new-chembl-ligand-based-target.html for more information). We also plan to provide a new target prediction web service in the future. The current target prediction web service (https://www.ebi.ac.uk/chembl/api/data/target_prediction/) has now been deprecated.

* Updated Data Sets:
Scientific Literature
Patent Bioactivity Data
Orange Book
USP Dictionary of USAN and International Drug Names
Clinical Candidates
WHO Anatomical Therapeutic Chemical Classification
British National Formulary
Manually Added Drugs

Database changes:

# Columns Added:

CELL_DICTIONARY
CELL_ONTOLOGY_ID VARCHAR2(10) ID for the corresponding cell type in the Cell Ontology

VARIANT_SEQUENCES
TAX_ID   NUMBER(11,0) NCBI Tax ID for the organism from which the sequence was obtained

COMPOUND_PROPERTIES
CX_MOST_APKA NUMBER(9,2) The most acidic pKa calculated using ChemAxon v17.29.0
CX_MOST_BPKA NUMBER(9,2) The most basic pKa calculated using ChemAxon v17.29.0
CX_LOGP NUMBER(9,2) The calculated octanol/water partition coefficient using ChemAxon v17.29.0
CX_LOGD NUMBER(9,2) The calculated octanol/water distribution coefficient at pH7.4 using ChemAxon v17.29.0

# Columns Removed:

COMPOUND_PROPERTIES
ACD_MOST_APKA Replaced by CX_MOST_APKA
ACD_MOST Replaced by CX_MOST_BPKA
ACD_LOGP Replaced by CX_LOGP
ACD_LOGD Replaced by CX_LOGD


Funding acknowledgements:

Work contributing to ChEMBL26 was funded by the Wellcome Trust, EMBL Member States, Open Targets, National Institutes of Health (NIH), EU Innovative Medicines Initiative 2 (IMI2) and EU Horizon 2020 programmes. Please see https://chembl.gitbook.io/chembl-interface-documentation/acknowledgments for more details.


If you require further information about ChEMBL, please contact us: chembl-help@ebi.ac.uk

# To receive updates when new versions of ChEMBL are available, please sign up to our mailing list: http://listserver.ebi.ac.uk/mailman/listinfo/chembl-announce
# For general queries/feedback please email: chembl-help@ebi.ac.uk
# For details of upcoming webinars, please see: http://chembl.blogspot.com/search/label/Webinar

Comments

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

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

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

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