The ChEMBL-og

What a time! For most of us, this is the first time that we have experienced a pandemic and its impact on our daily life. Although working from home has become our routine in the ChEMBL group, we are still working as hard as ever! Of course, COVID-19 data is taking up some of our attention,  (see ChEMBL_27) but we are also continuing our work relevant to other diseases that affect large populations around the world.

Today, we are going to talk about malaria. As you may know, this disease of the Plasmodium parasite family threatens nearly half of the world’s population and led to over 400,000 deaths in 2019, predominantly among children in resource-limited areas in Africa, Asia and Central and South America. New therapies are desperately needed, in particular to cope with increased resistance against artemisinin-based combination therapies.

To help address this challenge, we have been involved in a public-private consortium that aims to deliver a tool to predict potential bl…

The COVID-19 pandemic has resulted in an unprecedented effort across the global scientific community. Drug discovery groups are contributing in several ways, including the screening of compounds to identify those with potential anti-SARS-CoV-2 activity. When the compounds being assayed are marketed drugs or compounds in clinical development then this may identify potential repurposing opportunities (though there are many other factors to consider including safety and PK/PD considerations; see for example https://www.medrxiv.org/content/10.1101/2020.04.16.20068379v1.full.pdf+html). The results from such compound screening can also help inform and drive our understanding of the complex interplay between virus and host at different stages of infection.
Several large-scale drug screening studies have now been described and made available as pre-prints or as peer-reviewed publications. The ChEMBL team has been following these developments with significant interest, and as a contribution t…

Python is nowadays the favourite platform for many machine learning scientists. It is an easy to learn language that provides a vast number of nice data science and AI tools perfect for rapid prototyping.

Models trained using Python DNN libraries like PyTorch and Tensorflow usually perform well enough to be used for production runs, but there are some situations that require the predictions to be run in C++ i.e., when best performance is required or when the model needs to be integrated with an existing C++ codebase.

ONNX (Open Neural Network Exchange) is an AI framework designed to allow interoperability between ML/DL frameworks. It allows, for example, models trained in scikit-learn, PyTorch, TensorFlow and other popular frameworks to be converted to the "standard" ONNX format for later use in any programming language with an existing ONNX runtime.

In this mini-project we trained a "dummy" DNN network (single task target predictor) in Python with PyTorch, exporte…

We’ve now retired the old ChEMBL website but the new interface, with a range of improved features and functionality, is fully up and running!
If you’re new to ChEMBL or eager to learn more, then register for our webinar on March 11th @ 15:30.
Don’t forget, further information can also be found in our ChEMBL Quick Tour, ChEMBL-og and FAQs.
Questions? Send us a message through the Helpdesk.

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 records1,950,765 compounds (of which 1,940,733 have mol files)15,996,368 activities1,221,311 assays13,377 targets76,076 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 t…

At the end of last year we mentioned that we are now using RDKit for our compound structure processing (see here). Most excitingly, as a part of this we have been working with Greg Landrum the developer of RDKit over the last year to reimplement our curation pipeline using RDKit. 
The pipeline includes three functions:
1. Check Identifies and validates problem structures before they are added to the database
2. Standardize
Standardises chemical structures according to a set of predefined ChEMBL business rules 
3. GetParent
Generates parent structures of multi-component compounds based on a set of rules and defined list of salts and solvents
We are now pleased to announce that we are making all the code from this project freely available inGitHub. The functions can also now be used through ourChEMBL BeakerAPI. 

Live notebook with examples available here.
For ChEMBL26 (shortly to be released) we have created new molfiles for all the ChEMBL compounds using this pipeline and we will continue to r…