Title: Prediction of Enzyme Specificity using Protein Graph Convolutional Neural Networks. Abstract: Specific molecular recognition by proteins, for example, protease enzymes, is critical for maintaining the robustness of key life processes.  The substrate specificity landscape of a protease enzyme comprises the set of all sequence motifs that are recognized/cut, or just as importantly, not recognized/cut by the enzyme. Current methods for predicting protease specificity landscapes rely on learning sequence patterns in experimentally derived data with a single enzyme, but are not robust to even small mutational changes. A comprehensive evaluation of specificity requires consideration of the three-dimensional structure and energetics of molecular interactions. In this work, we present a protein graph convolutional neural network (PGCN), which uses a physically intuitive, structure-based molecular interaction graph generated using the Rosetta energy function that describes the topology and energetic features, to determine substrate specificity. We use the PGCN to recapitulate and predict the specificity of the NS3/4 protease from the Hepatitic C virus. We compare our PGCN with previously used machine learning models and show that its performance in classification tasks is equivalent or better. Because PGCN is based on physical interactions, it is inherently more interpretable; determination of feature importance reveals key sub-graph patterns responsible for molecular recognition that are biochemically reasonable. The PGCN model also readily lends itself to the design of novel enzymes with tailored specificity against disease targets.