ChEMBL Resources


Thursday, 24 January 2019

FPSim2, a simple Python3 molecular similarity tool

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 fully written in Python/Cython and features:

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 rdkit 
conda 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 http://localhost:9999/notebooks/demo.ipynb in a browser

Or if you prefer to try it without installing anything (yet)... Click on the binder image!

Data files used in the demos are also available to download.

I would also like to thank Andrew Dalke and Greg Landrum for their blogs, they have been very useful resources!


Tuesday, 8 January 2019

2019 and ChEMBL – News, jobs and birthdays

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 have also enhanced the data model so that we can capture information about assays and individual bioactivities in a more structured and detailed way.  The top level view of the data is essentially the same but it also enhances the options for people wanting to deposit data in ChEMBL.  So if you have experimental data that you would like to make publicly available through ChEMBL please contact us at and we would be happy to discuss with you.  Incidentally this is the same email address to use if you have any questions about using ChEMBL.

Have you tried our new ChEMBL interface yet?  If not please give it a go here and let us know what you think.  We are still working on refining it and so it’s not too late to influence its development. If you have suggestions now is the time to let us know by emailing chembl-help. 

Last but not least this year it is 10 years since the first release of ChEMBL – watch this space for more details on how we plan to celebrate this milestone …….

Friday, 4 January 2019

RDKit, C++ and Jupyter Notebook

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: