  In recent years machine learning (ML) took bio- and cheminformatics fields by
storm, providing new solutions for a vast repertoire of problems related to
protein sequence, structure, and interactions analysis. ML techniques, deep
neural networks especially, were proven more effective than classical models
for tasks like predicting binding affinity for molecular complex. In this work
we investigated the earlier stage of drug discovery process - finding druggable
pockets on protein surface, that can be later used to design active molecules.
For this purpose we developed a 3D fully convolutional neural network capable
of binding site segmentation. Our solution has high prediction accuracy and
provides intuitive representations of the results, which makes it easy to
incorporate into drug discovery projects. The model's source code, together
with scripts for most common use-cases is freely available at
http://gitlab.com/cheminfIBB/kalasanty
