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• W2617750324 abstract "Abstract 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. Supplementary information Supplementary data are available at Bioinformatics online." @default.
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• W2617750324 date "2017-05-31" @default.
• W2617750324 modified "2023-10-16" @default.
• W2617750324 title "DeepSite: protein-binding site predictor using 3D-convolutional neural networks" @default.
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• W2617750324 doi "https://doi.org/10.1093/bioinformatics/btx350" @default.
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