Polypharmacy, the use of drug combinations, is common to treat patients with complex or co-existing diseases. However, a major consequence of polypharmacy is a high risk of adverse side effects, which emerge because of drug-drug interactions, in which activity of one drug changes if taken with another drug. Furthermore, polypharmacy is recognized as an increasingly serious problem in the health care system affecting nearly 15% of the U.S. population and costing more than $177 billion a year in the U.S. alone in treating side effects. In this talk, I will describe the methodology for large-scale predictive modeling of polypharmacy. We start by capturing molecular, drug, and patient data for all drugs prescribed in the U.S. These data are represented with a massively multimodal network of protein-protein interactions, drug-protein target interactions, and polypharmacy side effects. I will then describe a network embedding approach that embeds nodes in such multimodal networks into optimized low-dimensional vector spaces. Here, I will outline key advancements in learning embeddings for networks, with an emphasis on fundamentally new opportunities in computational biology enabled by these advancements. Finally, I will show how we can use the approach to, for the first time, predict safety and side effects of drug combinations and how we can validate predictions in the clinic using real patient data.