Skip to main content

Citing ChEMBL, and Data DOIs


There are now multiple formats and ways to access the ChEMBL data, and we have recently assigned DOIs to all available versions of ChEMBL (and will archive these on the ftp server, permanently).

So when you publish use of ChEMBL, could you reference the following papers:

ChEMBL Database
A. Gaulton, L. Bellis, J. Chambers, M. Davies, A. Hersey, Y. Light, S. McGlinchey, R. Akhtar, A.P. Bento, B. Al-Lazikani, D. Michalovich, & J.P. Overington (2012) ‘ChEMBL: A Large-scale Bioactivity Database For Chemical Biology and Drug Discovery’ Nucleic Acids Res. Database Issue, 40 D1100-1107. DOI:10.1093/nar/gkr777 PMID:21948594

A.P. Bento, A. Gaulton, A. Hersey, L.J. Bellis, J. Chambers, M. Davies, F.A. Krüger, Y. Light, L. Mak, S. McGlinchey, M. Nowotka, G. Papadatos, R. Santos & J.P. Overington (2014) ‘The ChEMBL bioactivity database: an update’ Nucleic Acids Res. Database Issue, 42 1083-1090. DOI:10.1093/nar/gkt103 PMID: 24214965

myChEMBL
R. Ochoa, M. Davies, G. Papadatos, F. Atkinson and J.P. Overington (2014) 'myChEMBL: A virtual machine implementation of open data and cheminformatics tools' Bioinformatics. 30 298-300. DOI10.1093/bioinformatics/btt666 PMID: 24262214

ChEMBL RDF
S. Jupp, J. Malone, J. Bolleman, M. Brandizi, M. Davies, L. Garcia, A. Gaulton, S. Gehant, C. Laibe, N. Redaschi, S.M Wimalaratne, M. Martin, N. Le Novère, H. Parkinson, E. Birney and A.M Jenkinson (2014) 'The EBI RDF Platform: Linked Open Data for the Life Sciences' Bioinformatics 30 1338-1339 DOI:10.1093/bioinformatics/btt765 PMID:24413672

Also please reference the version of ChEMBL you may have used in any published analyses, using the following DOIs:

Dataset
DOI
ChEMBL

CHEMBL01
10.6019/CHEMBL.database.01
CHEMBL02
10.6019/CHEMBL.database.02
CHEMBL03
10.6019/CHEMBL.database.03
CHEMBL04
10.6019/CHEMBL.database.04
CHEMBL05
10.6019/CHEMBL.database.05
CHEMBL06
10.6019/CHEMBL.database.06
CHEMBL07
10.6019/CHEMBL.database.07
CHEMBL08
10.6019/CHEMBL.database.08
CHEMBL09
10.6019/CHEMBL.database.09
CHEMBL10
10.6019/CHEMBL.database.10
CHEMBL11
10.6019/CHEMBL.database.11
CHEMBL12
10.6019/CHEMBL.database.12
CHEMBL13
10.6019/CHEMBL.database.13
CHEMBL14
10.6019/CHEMBL.database.14
CHEMBL15
10.6019/CHEMBL.database.15
CHEMBL16
10.6019/CHEMBL.database.16
CHEMBL17
10.6019/CHEMBL.database.17
CHEMBL18
10.6019/CHEMBL.database.18
CHEMBL19
10.6019/CHEMBL.database.19


ChEMBL-RDF

ChEMBL-RDF/16.0
10.6019/CHEMBL.RDF.16.0
ChEMBL-RDF/17.0
10.6019/CHEMBL.RDF.17.0
ChEMBL-RDF/18.0
10.6019/CHEMBL.RDF.18.0
ChEMBL-RDF/18.1
10.6019/CHEMBL.RDF.19.0


myChEMBL

myChEMBL-17_0
10.6019/CHEMBL.myCHEMBL.17.0
myChEMBL-18_0
10.6019/CHEMBL.myCHEMBL.18.0

Future releases will adhere to the following patterns. We will be modifying the attribution part of the ChEMBL license to require reporting of these DOIs in publications that use ChEMBL. We hope this will contribute to reproducibility of analyses.

Comments

Popular posts from this blog

New SureChEMBL announcement

(Generated with DALL-E 3 ∙ 30 October 2023 at 1:48 pm) We have some very exciting news to report: the new SureChEMBL is now available! Hooray! What is SureChEMBL, you may ask. Good question! In our portfolio of chemical biology services, alongside our established database of bioactivity data for drug-like molecules ChEMBL , our dictionary of annotated small molecule entities ChEBI , and our compound cross-referencing system UniChem , we also deliver a database of annotated patents! Almost 10 years ago , EMBL-EBI acquired the SureChem system of chemically annotated patents and made this freely accessible in the public domain as SureChEMBL. Since then, our team has continued to maintain and deliver SureChEMBL. However, this has become increasingly challenging due to the complexities of the underlying codebase. We were awarded a Wellcome Trust grant in 2021 to completely overhaul SureChEMBL, with a new UI, backend infrastructure, and new f

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

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

Using ChEMBL activity comments

We’re sometimes asked what the ‘activity_comments’ in the ChEMBL database mean. In this Blog post, we’ll use aspirin as an example to explain some of the more common activity comments. First, let’s review the bioactivity data included in ChEMBL. We extract bioactivity data directly from   seven core medicinal chemistry journals . Some common activity types, such as IC50s, are standardised  to allow broad comparisons across assays; the standardised data can be found in the  standard_value ,  standard_relation  and  standard_units  fields. Original data is retained in the database downloads in the  value ,  relation  and  units  fields. However, we extract all data from a publication including non-numerical bioactivity and ADME data. In these cases, the activity comments may be populated during the ChEMBL extraction-curation process  in order to capture the author's  overall  conclusions . Similarly, for deposited datasets and subsets of other databases (e.g. DrugMatrix, PubChem), th