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Artificial intelligence-driven rational design of ionizable lipids for mRNA delivery

Author

Listed:
  • Wei Wang

    (University of Macau
    University of Macau)

  • Kepan Chen

    (Fudan University
    Fudan University)

  • Ting Jiang

    (Fudan University
    Ltd)

  • Yiyang Wu

    (University of Macau
    University of Macau)

  • Zheng Wu

    (University of Macau
    University of Macau)

  • Hang Ying

    (Fudan University
    Ltd)

  • Hang Yu

    (Fudan University
    Ltd)

  • Jing Lu

    (Fudan University
    Ltd)

  • Jinzhong Lin

    (Fudan University
    Fudan University)

  • Defang Ouyang

    (University of Macau
    University of Macau)

Abstract

Lipid nanoparticles (LNPs) have proven effective in mRNA delivery, as evidenced by COVID-19 vaccines. Its key ingredient, ionizable lipids, is traditionally optimized by inefficient and costly experimental screening. This study leverages artificial intelligence (AI) and virtual screening to facilitate the rational design of ionizable lipids by predicting two key properties of LNPs, apparent pKa and mRNA delivery efficiency. Nearly 20 million ionizable lipids were evaluated through two iterations of AI-driven generation and screening, yielding three and six new molecules, respectively. In mouse test validation, one lipid from the initial iteration, featuring a benzene ring, demonstrated performance comparable to the control DLin-MC3-DMA (MC3). Notably, all six lipids from the second iteration equaled or outperformed MC3, with one exhibiting efficacy akin to a superior control lipid SM-102. Furthermore, the AI model is interpretable in structure-activity relationships.

Suggested Citation

  • Wei Wang & Kepan Chen & Ting Jiang & Yiyang Wu & Zheng Wu & Hang Ying & Hang Yu & Jing Lu & Jinzhong Lin & Defang Ouyang, 2024. "Artificial intelligence-driven rational design of ionizable lipids for mRNA delivery," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-55072-6
    DOI: 10.1038/s41467-024-55072-6
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