Small molecules can potentially bind to a variety of bimolecular targets and whilst counter-screening against a wide variety of targets is feasible it can be rather expensive and probably only realistic for when a compound has been identified as of particular interest. For this reason there is considerable interest in building computational models to predict potential interactions. With the advent of large data sets of well annotated biological activity such as ChEMBL and BindingDB this has become possible.

ChEMBL 24 contains 15,207,914 activity data on 12,091 targets, 2,275,906 compounds, BindingDB contains 1,454,892 binding data, for 7,082 protein targets and 652,068 small molecules.

These predictions may aid understanding of molecular mechanisms underlying the molecules bioactivity and predicting potential side effects or cross-reactivity.

Whilst there are a number of sites that can be used to predict bioactivity data I'm going to compare one site, Polypharmacology Browser 2 (PPB2) http://ppb2.gdb.tools with two tools that can be downloaded to run the predictions locally. One based on Jupyter notebooks models built using ChEMBL built by the ChEMBL group https://github.com/madgpap/notebooks/blob/master/targetpred21_demo.ipynb and a more recent random forest model PIDGIN. If you are using proprietary molecules it is unwise to use the online tools.

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