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  • Review Article
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Diffusion models in bioinformatics and computational biology

Nature Reviews Bioengineering volume 2, pages 136–154 (2024)Cite this article

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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.

Key points

  • Diffusion models are a generative artificial intelligence technology that can be applied in natural language processing, image synthesis and bioinformatics.

  • Diffusion models have contributed greatly to computational protein design and generation, drug and small-molecule design, protein–ligand interaction modelling, cryo-electron microscopy data enhancement and single-cell data analysis.

  • Many diffusion models are also available as open-source tools.

  • Although diffusion models may potentially outperform other generative approaches, such as generative adversarial networks and variational auto-encoders, their computational resource requirements remain high.

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Fig. 1: Timeline of advances in diffusion models and their applications in bioinformatics.
Fig. 2: Forward and reverse processes of diffusion models.

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Acknowledgements

The work was partly supported by the US National Institutes of Health (grants R01GM146340 (to J.C.), R01GM093123 (to J.C.) and R35GM126985 (to D.X.)) and the US National Science Foundation (grant DBI2308699 (to J.C.)).

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Author notes

  1. These authors equally contributed to this work: Zhiye Guo, Jian Liu, Yanli Wang, Mengrui Chen.

Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA

    Zhiye Guo, Jian Liu, Yanli Wang, Mengrui Chen, Duolin Wang, Dong Xu & Jianlin Cheng

  2. NextGen Precision Health, University of Missouri, Columbia, MO, USA

    Zhiye Guo, Jian Liu, Yanli Wang, Mengrui Chen, Duolin Wang, Dong Xu & Jianlin Cheng

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  1. Zhiye Guo
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  2. Jian Liu
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Contributions

Z.G., J.L., Y.W. and M.C. collected data. J.C., D.X. and D.W. provided guidance on organizing the content. J.C. envisioned the developments for proteins, 3D genomics, single-cell Hi-C data analytics, cryo-EM, DNA design, peptide, proteomics and metabolomics. D.X. envisioned the developments in single-cell reconstruction and inference. Z.G., J.C., J.L., Y.W., D.X., D.W. and M.C. wrote and edited the manuscript.

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Correspondence to Jianlin Cheng.

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Nature Reviews Bioengineering thanks Bonnie Berger, Daniel Lazarev and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Guo, Z., Liu, J., Wang, Y. et al. Diffusion models in bioinformatics and computational biology. Nat Rev Bioeng 2, 136–154 (2024). https://doi.org/10.1038/s44222-023-00114-9

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