  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. Although
DNNs have been used in drug discovery for QSAR and ligand-based bioactivity
predictions, none of these models have benefited from this powerful
convolutional architecture. This paper introduces AtomNet, the first
structure-based, deep convolutional neural network designed to predict the
bioactivity of small molecules for drug discovery applications. We demonstrate
how 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, we show that AtomNet's application
of local convolutional filters to structural target information successfully
predicts new active molecules for targets with no previously known modulators.
Finally, we show that AtomNet outperforms previous docking approaches on a
diverse set of benchmarks by a large margin, achieving an AUC greater than 0.9
on 57.8% of the targets in the DUDE benchmark.
