DeepSite: protein‐binding site predictor using 3D‐convolutional neural networks

Motivation: An important step in structure‐based drug design consists in the prediction of druggable binding sites. Several algorithms for detecting binding cavities, those likely to bind to a small drug compound, have been developed over the years by clever exploitation of geometric, chemical and evolutionary features of the protein. Results: Here we present a novel knowledge‐based approach that uses state‐of‐the‐art convolutional neural networks, where the algorithm is learned by examples. In total, 7622 proteins from the scPDB database of binding sites have been evaluated using both a distance and a volumetric overlap approach. Our machine‐learning based method demonstrates superior performance to two other competitive algorithmic strategies. Availability and implementation: DeepSite is freely available at www.playmolecule.org. Users can submit either a PDB ID or PDB file for pocket detection to our NVIDIA GPU‐equipped servers through a WebGL graphical interface. Contact: gianni.defabritiis@upf.edu Supplementary information: Supplementary data are available at Bioinformatics online.