It can take 15 years and cost $1 billion for a new drug to reach patients as the question of identifying which diseases a new drug could treat is tremendously complex. Diseases are not independent of each other, and a large number of genes are shared between often quite distinct diseases. Similarly, the effects of drugs are not limited to proteins to which they directly bind in the body; instead, these effects spread throughout biological networks in which they act. Therefore, the effect of a drug on a disease is inherently a network phenomenon. In this talk, I will describe a framework for large-scale prediction of medical indications from biological network data. The framework is based on a new insight that the structure of a small network neighborhood of a drug target is similar to the structure of the neighborhood of the disease the drug treats. The approach first learns deep embeddings, compact representations of subnetworks of proteins targeted by drugs and diseases. Importantly, the geometry of the learned embedding space is optimized such that performing algebraic operations in that space reflects interactions, the essence of biological networks. The embeddings are then used to predict what diseases a new drug could treat and to provide explanations for predictions. These explanations give insights into network mechanisms of drugs' therapeutic effects. Finally, such network embedding approaches make correct predictions for a large number of recently repurposed drugs, and can operate even on the hardest, yet extremely important, cases when a drug has no indicated disease or when a disease does not yet have any drug treatment.