Diffusion models in bioinformatics and computational biology.

Journal: Nature reviews bioengineering
Published Date: Oct 27, 2023

Abstract

Denoising diffusion models embody a type of generative artificial intelligence that can be applied in computer vision, natural language processing and bioinformatics. In this Review, we introduce the key concepts and theoretical foundations of three diffusion modelling frameworks (denoising diffusion probabilistic models, noise-conditioned scoring networks and score stochastic differential equations). We then explore their applications in bioinformatics and computational biology, including protein design and generation, drug and small-molecule design, protein-ligand interaction modelling, cryo-electron microscopy image data analysis and single-cell data analysis. Finally, we highlight open-source diffusion model tools and consider the future applications of diffusion models in bioinformatics.

Authors

  • Zhiye Guo
    Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.
  • Jian Liu
    Department of Rheumatology, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, China.
  • Yanli Wang
    Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.
  • Mengrui Chen
    Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.
  • Duolin Wang
    Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.
  • Dong Xu
    Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.
  • Jianlin Cheng
    Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.

Keywords

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