  We introduce a deep learning architecture for structure-based virtual
screening that generates fixed-sized fingerprints of proteins and small
molecules by applying learnable atom convolution and softmax operations to each
compound separately. These fingerprints are further transformed non-linearly,
their inner-product is calculated and used to predict the binding potential.
Moreover, we show that widely used benchmark datasets may be insufficient for
testing structure-based virtual screening methods that utilize machine
learning. Therefore, we introduce a new benchmark dataset, which we constructed
based on DUD-E and PDBBind databases.
