This dissertation covers work discussed in the following papers: "Spatial Graph Convolutions for Drug Discovery" describes new deep neural network architectures for modeling drug-receptor interactions. We argue that the future of predicting the interactions between a drug and its prospective target demands more than simply applying deep learning algorithms from other domains, like vision and natural language, to molecules. "Machine Learning Harnesses Molecular Dynamics to Discover New Opioid Chemotypes" describes an algorithm that leverages protein motion to enrich the search for active molecules. We then applied the method to find a new chemical scaffold that we experimentally verified is an agonist for the μ Opioid Receptor. "Kinetic Machine Learning Unravels Ligand-Directed Conformational Change of Opioid Receptor" describes differential pathways of deactivation and differential conformational states sampled by the μ Opioid Receptor in response to different opioid ligands.