Artificial intelligence in the rational design of lipid nanoparticles for mRNA therapeutics

Heyu Zhao; Junchao Xu; Xia Gao; Jingcheng Zhu; Zhongshan He

doi:10.59717/j.xinn-drugdisc.2026.100006

The Innovation Drug Discovery > 2026 Vol. 1 > No. 1 > 100006

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Artificial intelligence in the rational design of lipid nanoparticles for mRNA therapeutics

1.
Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China
2.
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
3.
Department of Experimental Surgery, Tangdu Hospital, The Fourth Military Medical University, Shaanxi 710038, China
4.
Departments of Molecular Physiology and Biological Physics and Biomedical Engineering, University of Virginia, Charlottesville, VA 22903, USA
5.
These authors contributed equally

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Corresponding author: udr5kr@virginia.edu
Received Date: January 12, 2026

Accepted Date: April 06, 2026

Published Online: April 08, 2026

The Innovation Drug Discovery 1(1), https://doi.org/10.59717/j.xinn-drugdisc.2026.100006 | Cite this article

GRAPHICAL ABSTRACT

GRAPHICAL ABSTRACT

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PUBLIC SUMMARY

PUBLIC SUMMARY
1. Lipid nanoparticles (LNPs) are not passive carriers; they shape where messenger RNA (mRNA) reaches.
  
  This review explains how lipid chemistry, formulation, and manufacturing jointly control delivery.
  
  Artificial intelligence (AI) can learn these linked factors and accelerate safer, more targeted LNP design.
  
  Progress now depends on shared data, better assays, and models built for real-world manufacturing.

ABSTRACT

ABSTRACT

Lipid nanoparticles (LNPs) have made RNA therapeutics clinically viable, yet delivery remains the dominant constraint on efficacy and safety as applications expand beyond the liver. Performance is emergent: it reflects coupled choices in ionizable-lipid chemistry, multi-component formulation, and process history, and is further reshaped by biological interfaces including protein corona remodeling, tissue transport barriers, endocytic trafficking, and low-probability endosomal escape. This review surveys how artificial intelligence is converting this nonlinear design space from empirical iteration into data-efficient, multi-objective optimization. We highlight (i) structure-activity learning and synthesis-aware generative modeling for ionizable lipid discovery; (ii) formulation- and process-conditioned architectures that treat LNPs as composite, process-defined materials; and (iii) pooled in vivo barcoding and single-cell readouts that enable prediction and tuning of organ and cell-type tropism. We conclude by outlining three priorities for translation: standardized data and metadata, mechanistic endpoints for escape and immunogenicity, and CMC-aware optimization. Progress on these fronts will be necessary for potent, safe, and manufacturable mRNA-LNP medicines.
- artificial intelligence /
- machine learning /
- lipid nanoparticles /
- mRNA therapeutics /
- ionizable lipids /
- biodistribution /
- endosomal escape /
- chemistry, manufacturing, and controls

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Zhao H., Xu J., Gao X., et al. (2026). Artificial intelligence in the rational design of lipid nanoparticles for mRNA therapeutics. The Innovation Drug Discovery 1:100006. https://doi.org/10.59717/j.xinn-drugdisc.2026.100006

Zhao H., Xu J., Gao X., et al. (2026). Artificial intelligence in the rational design of lipid nanoparticles for mRNA therapeutics. The Innovation Drug Discovery 1:100006. https://doi.org/10.59717/j.xinn-drugdisc.2026.100006

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