  Denoising diffusion models have emerged as one of the most powerful
generative models in recent years. They have achieved remarkable success in
many fields, such as computer vision, natural language processing (NLP), and
bioinformatics. Although there are a few excellent reviews on diffusion models
and their applications in computer vision and NLP, there is a lack of an
overview of their applications in bioinformatics. This review aims to provide a
rather thorough overview of the applications of diffusion models in
bioinformatics to aid their further development in bioinformatics and
computational biology. We start with an introduction of the key concepts and
theoretical foundations of three cornerstone diffusion modeling frameworks
(denoising diffusion probabilistic models, noise-conditioned scoring networks,
and stochastic differential equations), followed by a comprehensive description
of diffusion models employed in the different domains of bioinformatics,
including cryo-EM data enhancement, single-cell data analysis, protein design
and generation, drug and small molecule design, and protein-ligand interaction.
The review is concluded with a summary of the potential new development and
applications of diffusion models in bioinformatics.
