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Showing posts from November, 2014

Finding key compounds in med. chemistry patents: The open way

A couple of us attended the 3rd RDKit UGM, hosted by Merck in Darmstadt this year. It was an excellent opportunity to catch up with RDKit developments and applications and meet up with other loyal "RDKitters".
I presented a talk-torial there and went through an IPython Notebook, which some of you may find useful. It uses patent chemistry data extracted from SureChEMBL and after a series of filtering steps, it follows a few "traditional" chemoinformatics approaches with a set of claimed compounds. My ultimate aim was to identify "key compounds" in patents using compound information alone, inspired by papers such as this and this. The crucial difference is that these authors used commercial data and software, where in this implementation everything is free and open. At the same time, I wanted to show off what the combination of pandas, scikit-learn, mpld3, Beaker, RDKit, IPython Notebook and SureChEMBL can do nowadays (hint: a lot). 
So, here is the Notebook …

Using ChEMBL web services via proxy.

It is common practice for organizations and companies to make use of proxy servers to connect to services outside their network. This can cause problems for users of the ChEMBL web services who sit behind a proxy server. So to help those users who have asked, we provide the following quick guide, which demonstrates how to access ChEMBL web services via a proxy.

Most software libraries respect proxy settings from environmental variables. You can set the proxy variable once, normally HTTP_PROXY and then use that variable to set other related proxy environment variables:


Or if you have different proxies responsible for different protocols:


On Windows, this would be:



If you are accessing the ChEMBL web services programmatically and you prefer not to clutter your environment, you can consider adding the proxy settings to your scripts. Here are some python based recipes:


1. Official ChEMBL client library
If you are working in a python based environment, we recommend you to use our client …

An overview and invitation to contribute to ChEMBL curation with PPDMs

PPDMs has been in the making for more than a year and is a follow-up on a conference paper we published in 2012. As in 2012, our objective is to map small molecule binding sites to protein domains, the structural units that form recurring building blocks in the evolution of proteins. An application note describing PPDMs is just out in Bioinformatics.

Mapping small molecule binding to protein domains

The mapping facilitates the functional interpretation of small molecule-protein interactions - if you understand which domain in a protein is targeted, you are in a better position to anticipate the downstream effect.  Mapping small molecule binding to protein domains also provides a technical advantage to machine-learning approaches that incorporate protein sequence information as a descriptor to predict small molecule bioactivity. Reducing the sequence descriptor to the part that mediates small molecule binding increases the informative content of the descriptor. This is best exemplified…