  Artificial intelligence (AI) in the form of deep learning bears promise for
drug discovery and chemical biology, $\textit{e.g.}$, to predict protein
structure and molecular bioactivity, plan organic synthesis, and design
molecules $\textit{de novo}$. While most of the deep learning efforts in drug
discovery have focused on ligand-based approaches, structure-based drug
discovery has the potential to tackle unsolved challenges, such as affinity
prediction for unexplored protein targets, binding-mechanism elucidation, and
the rationalization of related chemical kinetic properties. Advances in deep
learning methodologies and the availability of accurate predictions for protein
tertiary structure advocate for a $\textit{renaissance}$ in structure-based
approaches for drug discovery guided by AI. This review summarizes the most
prominent algorithmic concepts in structure-based deep learning for drug
discovery, and forecasts opportunities, applications, and challenges ahead.
