Skip to main content

Open Data for Neglected Tropical Disease Discovery, and Release of ChEMBL-04

It was clearly a slow news day in Swindon that day; but, in a way, wouldn't it be nice to live a place where this was big news. I for one, am glad the tortoise is OK (google with the headline and you'll get the full, detailed story).

Anyway, there are some significant publications in Nature this week on HTS screening and follow-up for Malaria screens (the papers are free content at the moment - Gamo et al, and Guiguemde et al. There are also some press releases for these papers and the public data release. We won't repeat the content of these formal things, but here provide some informal commentary...

The magic data pixies here at EMBL-EBI have been working hard and we have loaded all the data into the latest release of our SAR database - ChEMBL04. The data is now live in the web interface, and the ftp download of the whole database will be in the near future (we are still optimising our production processes, so sorry that the data is available in the front-end before the download files are fully ready and tested - but we took the view that people would probably not want us to hold back access where possible. However, the gap between loading into the front-end schema and the packaged export release will shorten.

We have also put together a 'microsite' called ChEMBL-NTD (NTD stands for Neglected Tropical Disease) accessible at - this showcases and provides easy download of raw data from ChEMBL for this strategically important set of diseases, and also allows the addition of extra functionality for visualisation that isn't available in the ChEMBL front end. We have some exciting plans for community annotation of these data-sets, and more on this later. At the moment, there are download links, in a variety of common formats, for the GSK, St. Judes, and Novartis. Unfortunately we only had time to build some interactive query tools for the ChEMBL plasmodium and GSK datasets; but rest assured, were putting together some tools for cross datasets analysis and querying (given the scientific limits of analysis of large sets of single point screening data).

As you will probably guess, there are more data-sets in the pipeline for release, and we would be delighted if others with similar datasets would be interested in publicly archiving them here at the EMBL-EBI. As always, all the EMBL-EBI data is freely accessible, redistributable, etc>.

If you have any feedback on data formats, the interface, etc please let us know.

Chembl04 contains 680,293 compound records, 565,243 distinct compounds, and 2,705,136 assay data points.

Finally, a heartfelt thanks to many people who have helped us put this together, championed the release of data from their organisations, and acted as the social glue that is so important in getting these sort of things actually done. As the youth the world over now say - respect to Rick Keenan, Jose Garcia-Bustos, Frederic Bost, Pascal Fantauzzi, Richard Glynne, Thierry Diagana, Anang Shelat and Kip Guy!

%T Thousands of chemical starting points for antimalarial lead identification
%J Nature
%V 465
%P 305-310
%D 2010
%A F.-J. Gamo
%A L.M. Sanz
%A J. Vidal
%A C. de Cozar
%A E. Alvarez
%A J.-L. Lavandera
%A D.E. Vanderwall
%A D.V.S. Green
%A V. Kumar 
%A S. Hasan
%A J.R. Brown
%A C.E. Peishoff
%A L.R. Cardon
%A J.F. Garcia-Bustos

%T Chemical genetics of Plasmodium falciparum
%J Nature
%V 465
%P 311-315
%A W.A. Guiguemde 
%A A.A. Shelat
%A D. Bouck
%A S. Duffy
%A G.J. Crowther
%A P.H. Davis
%A D.C. Smithson
%A M. Connelly
%A J. Clark
%A F. Zhu
%A M.B. Jimnez-D─▒az
%A M.S. Martinez
%A E.B. Wilson
%A A.K. Tripathi 
%A J. Gut
%A E.R. Sharlow
%A I. Bathurst
%A F. El Mazouni1
%A J.W. Fowble 
%A I. Forquer
%A P.L. McGinley
%A S. Angulo-Barturen
%A S. Ferrer
%A P.J. Rosenthal
%A J.L. DeRisi
%A D.J. Sullivan Jr.
%A J.S. Lazo
%A D.S. Roos
%A M.K. Riscoe
%A M.A. Phillips
%A P.K. Rathod 
%A W.C. Van Voorhis
%A V.M. Avery 
%A R.K. Guy


Unknown said…
Can anyone tell me how to get these compounds and where i can get the available vendor list
jpo said…

Some of the compounds are available commercially - it is pretty simple to do a search against ZINC to get a good starting point for sourcing the compounds. Let us know if you'd like us to look at this for you.

You could also try contacting either St. Judes, Novartis, or GSK (as appropriate and using the contact details in the papers) to get some samples. But they may not have sufficient sample left, and also I know have been inundated with request following the publications.


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 . In addition to it, the most popular legacy REST endpoints will also remain implemented in the new web services: 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: Please see ChEMBL_30 release notes for full details of all changes in this release: 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