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…

• FE
  Federación Española de Enfermedades Raras
• MD
  Ministerio de Economía y Competitividad

  Award: BIO2014-53095-P