  Diffusion probabilistic models have made their way into a number of
high-profile applications since their inception. In particular, there has been
a wave of research into using diffusion models in the prediction and design of
biomolecular structures and sequences. Their growing ubiquity makes it
imperative for researchers in these fields to understand them. This paper
serves as a general overview for the theory behind these models and the current
state of research. We first introduce diffusion models and discuss common
motifs used when applying them to biomolecules. We then present the significant
outcomes achieved through the application of these models in generative and
predictive tasks. This survey aims to provide readers with a comprehensive
understanding of the increasingly critical role of diffusion models.
