  The knowledge of potentially druggable binding sites on proteins is an
important preliminary step towards the discovery of novel drugs. The
computational prediction of such areas can be boosted by following the recent
major advances in the deep learning field and by exploiting the increasing
availability of proper data. In this paper, a novel computational method for
the prediction of potential binding sites is proposed, called DeepSurf.
DeepSurf combines a surface-based representation, where a number of 3D
voxelized grids are placed on the protein's surface, with state-of-the-art deep
learning architectures. After being trained on the large database of scPDB,
DeepSurf demonstrates superior results on three diverse testing datasets, by
surpassing all its main deep learning-based competitors, while attaining
competitive performance to a set of traditional non-data-driven approaches.
