Diffusion models are a class of generative models that operate through a two-step forward and reverse diffusion process. The former progressively noises a sample until it approximates a normal distribution, and the latter reconstructs the original data distribution from the Gaussian noise. These models have been applied across many areas, including image synthesis, computer vision and temporal data modelling, as well as in chemistry for organic molecule generation, protein ligand docking and de novo drug design. For more information on diffusion models, please refer to Refs. .