Deep convolutional neural networks (neural nets with a constrained architecture that leverages the spatial and temporal structure of the domain they model) achieve the best predictive performance in areas such as speech and image recognition. Such neural networks autonomously discover and hierarchically compose simple local features into complex models. We demonstrate that biochemical interactions, being similarly local, are amenable to automatic discovery and modeling by similarly-constrained machine learning architectures. We describe the training of AtomNet, the first structure-based, deep convolutional neural network designed to predict the bioactivity of small molecules for drug discovery applications, on millions of training examples derived from ChEMBL and the PDB. We visualize the automatically-derived convolutional filters and demonstrate that the system is discovering chemically sensible interactions. Finally, we demonstrate the utility of autonomously-discovered filters by outperforming previous docking approaches and achieving an AUC greater than 0.9 on 57.8% of the targets in the DUDE benchmark. 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.