  Discovering new medicines is the hallmark of human endeavor to live a better
and longer life. Yet the pace of discovery has slowed down as we need to
venture into more wildly unexplored biomedical space to find one that matches
today's high standard. Modern AI-enabled by powerful computing, large
biomedical databases, and breakthroughs in deep learning-offers a new hope to
break this loop as AI is rapidly maturing, ready to make a huge impact in the
area. In this paper we review recent advances in AI methodologies that aim to
crack this challenge. We organize the vast and rapidly growing literature of AI
for drug discovery into three relatively stable sub-areas: (a) representation
learning over molecular sequences and geometric graphs; (b) data-driven
reasoning where we predict molecular properties and their binding, optimize
existing compounds, generate de novo molecules, and plan the synthesis of
target molecules; and (c) knowledge-based reasoning where we discuss the
construction and reasoning over biomedical knowledge graphs. We will also
identify open challenges and chart possible research directions for the years
to come.
