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Catalyzing CAR-T innovation with AI virtual cells: Breakthroughs, bottlenecks, and the path forward

  • 1.

    Department of Hematology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China

  • 2.

    Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China

  • 3.

    Department of Pathology, College of Basic Medical Sciences, Chongqing Medical University, Chongqing 400016, China

  • 4.

    Molecular Medicine Diagnostic and Testing Center, Chongqing Medical University, Chongqing 400016, China

  • 5.

    Department of Pathology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China

  • 6.

    Institute for Immunology and Pathogenesis (IIP), Chongqing Medical University, Chongqing 400016, China

  • 7.

    Center for Immune-Related Diseases at Shanghai Institute of Immunology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China

  • 8.

    Department of Immunobiology, Yale University School of Medicine, New Haven CT 06520-8055, USA

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  • Corresponding author: huabing.li@shsmu.edu.cn
  • Received Date:  February 26, 2026
    Accepted Date:  September 29, 2026
    Published Online:  September 30, 2026
The Innovation Life  https://doi.org/10.59717/j.xinn-life.2026.100261  |  Cite this article
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    • REFERENCES
  • [1] Bunne C., Roohani Y., Rosen Y., et al. (2024). How to build the virtual cell with artificial intelligence: Priorities and opportunities. Cell 187:7045−7063. DOI:10.1016/j.cell.2024.11.015

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    [2] Rafei H., Upadhyay R. and Sharma P. (2026). A guide to CAR T cell therapies: Development, current status and future prospects. Nat. Rev. Immunol. DOI:10.1038/s41577-026-01322-1

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    [3] Arkfeld E., Wang Z., Hakozaki H., et al. (2026). Whole-cell particle-based digital twin simulations from 4D lattice light-sheet microscopy data. Cell 189:4581−4593.e6. DOI:10.1016/j.cell.2026.06.010

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    [5] Ahlmann-Eltze C., Huber W. and Anders S. (2025). Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines. Nat. Methods 22:1657−1661. DOI:10.1038/s41592-025-02772-6

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  • Cite this article:

    Liang X., Luo B., Jiang N., et al. (2027). Catalyzing CAR-T innovation with AI virtual cells: Breakthroughs, bottlenecks, and the path forward. The Innovation Life 5:100261. https://doi.org/10.59717/j.xinn-life.2026.100261
    Liang X., Luo B., Jiang N., et al. (2027). Catalyzing CAR-T innovation with AI virtual cells: Breakthroughs, bottlenecks, and the path forward. The Innovation Life 5:100261. https://doi.org/10.59717/j.xinn-life.2026.100261

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