With remarkable successes of machine learning in a variety of application areas, we are witnessing an increasing interest in applications of machine learning to drug discovery and development.

We cover key advancements in machine learning over the last few years, with an emphasis on fundamentally new opportunities in drug development enabled by these advancements. We are interested in why and how these advances can help drug-related tasks. We elaborate uses of machine learning in drug development through six key tasks: (a) synthesis prediction and de novo drug design, (b) molecular property prediction, (c) virtual drug screening and drug-target interactions, (d) clinical trial recruitment, (e) drug repurposing, (f) adverse drug effects and polypharmacy.

We discuss theoretical foundations behind methods for these key drug-related tasks, illustrate various approaches based on different formulations, and summarize representative applications. We cover generative models, reinforcement learning, as well as very recent advancements in deep representation learning and embeddings.