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Computation and deep-learning-driven advances in CRISPR genome editing

Nature Structural & Molecular Biology volume 33, pages 203–214 (2026)Cite this article

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Abstract

Genome editing with CRISPR–Cas systems is revolutionizing medicine, molecular biology and biotechnology. In this Review, we discuss the contributions of deep learning-based structure prediction algorithms, physics-based simulations, neural networks, graph neural networks and generative models, including diffusion and large language models, in engineering and optimizing CRISPR systems and in understanding their mechanistic basis. We highlight the challenges and limitations to the transformative effects of computational modeling and tools in the context of the development of programmable genome editors for biomedicine and biotechnology.

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Fig. 1: NNs for genome editing.
Fig. 2: GNN framework for modeling TnsC filament dynamics.
Fig. 3: Design of Cas13a variants through network analysis and AI.
Fig. 4: Deep learning approaches for the design of gene-editing systems.

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Acknowledgements

This material is based upon work supported by the National Institutes of Health (R01GM141329) and the National Science Foundation (CHE-2144823). G.P. also acknowledges support from the Sloan Foundation (FG-2023-20431) and the Camille and Henry Dreyfus Foundation (TC-24-063).

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Authors and Affiliations

  1. Department of Bioengineering, University of California, Riverside, Riverside, CA, USA

    Chinmai Pindi & Giulia Palermo

  2. Department of Chemistry, University of California, Riverside, Riverside, CA, USA

    Giulia Palermo

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  1. Chinmai Pindi
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  2. Giulia Palermo
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G.P. conceptualized and drafted the review article. C.P. contributed to the writing. Both authors jointly developed the graphical representations.

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Correspondence to Giulia Palermo.

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C.P. and G.P. are coinventors on patent applications filed by the University of California, Riverside.

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Pindi, C., Palermo, G. Computation and deep-learning-driven advances in CRISPR genome editing. Nat Struct Mol Biol 33, 203–214 (2026). https://doi.org/10.1038/s41594-025-01739-7

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