On Friday 11th May 2012 we are running a small (and free) interactive workshop on "Shaping the Future of ChEMBL". We would like your input to develop some easy to use workflows for several key tasks that drug discoverers often want to do, and could do more efficiently with the ChEMBL data. These workflows would be aimed at medicinal chemists and would cover the use of ChEMBL data in lead generation and optimization tasks, hence we would like to involve medicinal chemists in their creation and implementation. The workshop will be on campus here at Hinxton, and will start around 10am and finish around 3pm (lunch, coffee and cakes will be provided). If you are interested in helping, with what will hopefully be a fun day, please contact us.
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…
Fancy playing with RDKit C++ API without needing to set up a C++ project and compile it? But wait... isn't C++ a compiled programming language? How this can be even possible?
Thanks to Cling (CERN's C++ interpreter) and xeus-cling jupyter kernel is possible to use C++ as an intepreted language inside a jupyter notebook!
We prepared a simple notebook showing few examples of RDKit functionalities and a docker image in case you want to run it.
With the single requirement of docker being installed in your computer you'll be able to easily run the examples following the three steps below: docker pull eloyfelix/rdkit_jupyter_clingdocker run -d -p 9999:9999 eloyfelix/rdkit_jupyter_clingopen http://localhost:9999/notebooks/rdkit_cling.ipynb in a browser
FPSim2 is a new tool for fast similarity search on big compound datasets (>100 million) being developed at ChEMBL. We started developing it as we needed a Python3 library able to run either in memory or out-of-core fast similarity searches on such dataset sizes.
It's written in Python/Cython and features: A fast population count algorithm (builtin-popcnt-unrolled) from https://github.com/WojciechMula/sse-popcount using SIMD instructions.Bounds for sub-linear speed-ups from 10.1021/ci600358fA compressed file format with optimised read speed based in PyTables and BLOSCUse of multiple cores in a single search In memory and on disk search modesSimple and easy to use
Source code is available on github and Conda packages are also available for either mac or linux. To install it type:
conda install rdkit -c rdkitconda install fpsim2 -c efelix
Try it with docker (much better performance than binder):
docker pull eloyfelix/fpsim2 docker run -p 9999:9999 eloyfelix/fpsim2 open htt…
Happy New Year from the ChEMBL Group to all our users and collaborators. Firstly, do you want a new challenge in 2019? If so, we have a position for a bioinformatician in the ChEMBL Team to develop pipelines for identifying links between therapeutic targets, drugs and diseases. You will be based in the ChEMBL team but also work in collaboration with the exciting Open Targets initiative. More details can be found here(closing date 24thJanuary). In case you missed it, we published a paper at the end of last on the latest developments of the ChEMBL database “ChEMBL: towards direct deposition of bioassay data”. You can read it here. Highlights include bioactivity data from patents, human pharmacokinetic data from prescribing information, deposited data from neglected disease screening and data from the IMI funded K4DD project. We have also added a lot of new annotations on the therapeutic targets and indications for clinical candidates and marketed drugs to ChEMBL. Importantly we ha…
Kuster Lab Chemical Proteomics Drug Profiling (src_id = 48, Document ChEMBL_ID = CHEMBL3991601):
Data have been included from the publication: The target landscape of clinical kinase drugs. Klaeger S, Heinzlmeir S and Wilhelm M et al (2017), Science, 358-6367 (https://doi.org/10.1126/science.aan4368)