With the release of ChEMBL 21 has come a set of updated target predicted models.

The good news is that, besides the increase in terms of training data (compounds and targets), the new models were built using the latest stable versions of RDKit (2015.09.2) and scikit-learn (0.17). The latter was upgraded from the much older 0.14 version, which was causing incompatibility issues while trying to use the models.

I've been using the models and I thought I'd share an iPython Notebook I have created. This is based on the ChEMBL notebook with code tidbits taken from the absolutely invaluable Stack Overflow. I'm often in the situation where I actually want to know the predicted activity at specific targets, and specifically want to confirm lack of predicted activity at potential off-targets. I could have a notebook for each target but actually the speed of calculation means that I can calculate all the models and then just cherry pick those of interest.

Read on…