Convolutional Neural Network for Structure-based Drug Discovery
نویسنده
چکیده
Deep convolutional neural networks comprise a subclass of deep neural networks (DNN) with a constrained architecture that leverages the spatial and temporal structure of the domain they model. Convolutional networks achieve the best predictive performance in areas such as speech and image recognition by hierarchically composing simple local features into complex models. We try to apply the convolutional concepts of feature locality and hierarchical composition to the modeling of bioactivity and chemical interactions. In further contrast to existing DNN techniques, some application of local convolutional filters to structural target information successfully predicts new active molecules for targets with no previously known modulators. Keywords-neural network;drug discovery
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