In case you have been too busy to notice, ChEMBL_21 has arrived with the usual additions, improvements and enhancements both on the data/annotation side, as well as on the interface/services. To complement this, we have also updated the target prediction models , which can be downloaded from our ftp here . 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 (see MultiLabelBinarizer ) to several of you while trying to use the models. We've also put together a quick Jupyter Notebook demo on how to get predictions from the models here: https://github.com/madgpap/notebooks/blob/master/target_pred_21_demo.ipynb The new models will also be available on myChEMBL 21 along with a more detai...
The Organization of Drug Discovery Data
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