ArticleNature communications2024
AGILE platform: a deep learning powered approach to accelerate LNP development for mRNA delivery.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 121 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
121 citing papers in PubMed.
- Delivering the blueprint: Advances and challenges in mRNA therapeutics for the respiratory system.International journal of pharmaceutics: X · 2026Review
- Cancer nanomedicine and RNA/Lipid nanoparticle platforms in oncology: developmental evolution, translational barriers, and precision-therapy opportunities.Journal of the Egyptian National Cancer Institute · 2026Review
- The advances and challenges of mRNA-LNP application in fields of hematology and oncology therapeutics.Journal of hematology & oncology · 2026Review
- Artificial Intelligence in Photodynamic Therapy: From Molecular Design of Photosensitizers to Intelligent Delivery, Adaptive Dosimetry, and Personalized Prognostication of Results.International journal of molecular sciences · 2026Review
- Overcoming hepatic tropism: Precision engineering of lipid nanoparticles for extrahepatic RNA delivery.Materials today. Bio · 2026Review
- Structural evolution of ionizable lipids for nucleic acid delivery.Nature reviews. Chemistry · 2026Review
- Towards mRNA therapeutics 2.0.Nature reviews. Drug discovery · 2026Review
- mRNA lipid nanoparticle vaccines: current status, challenges and future prospects.Molecular biomedicine · 2026Review
- Lipid nanoparticles optimized for large RNA cargo and tissue targeting enhance in vivo genome editing.Nature biotechnology · 2026Article
- Advances in Nanomedicine for Brain Tumors: Overcoming Biological Barriers, Targeting Strategies, and Future Directions.Current neurology and neuroscience reports · 2026Review
- New approach methodologies (NAMs) for preclinical and translational evaluation of mRNA-lipid nanoparticle (LNP) therapeutics.Journal of controlled release : official journal of the Controlled Release Society · 2026Review
- Cancer Vaccine Development: Toward Artificial Intelligence-Assisted Personalized Cell Membrane Nanovaccine.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Review
- Hydrophobic Tail and Linker Diversification of PEG-Lipids Unlocks Precise Muscle Tropism of mRNA-Lipid Nanoparticles.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Artificial intelligence for translational personalized neoantigen cancer vaccine development.Journal of biomedical science · 2026Review
- Integration of Artificial Intelligence and Microfluidics for Drug Delivery Applications.Micromachines · 2026Review
- Lipid Nanoparticles for Gene Therapy: Unresolved Challenges in Manufacturing, Transdermal Delivery, Machine Learning, Endosomal Escape, and the Protein Corona.Pharmaceutics · 2026Review
- A novel machine learning framework-based rapid screening of ionizable lipids in LNPs for highly-efficient mRNA expression.Acta pharmaceutica Sinica. B · 2026Article
- Delivering the future of immunotherapy: A state-of-the-art review of gene editing in immune cells with lipid nanoparticles.Materials today. Bio · 2026Review
- From serendipity to rational design: Accelerating ionizable lipid discovery with LipidAI.Acta pharmaceutica Sinica. B · 2026Article
61 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ionizable lipid nanoparticles (LNPs) are seeing widespread use in mRNA delivery, notably in SARS-CoV-2 mRNA vaccines. However, the expansion of mRNA therapies beyond COVID-19 is impeded by the absence of LNPs tailored for diverse cell types. In this study, we present the AI-Guided Ionizable Lipid Engineering (AGILE) platform, a synergistic combination of deep learning and combinatorial chemistry. AGILE streamlines ionizable lipid development with efficient library design, in silico lipid screening via deep neural networks, and adaptability to diverse cell lines. Using AGILE, we rapidly design, synthesize, and evaluate ionizable lipids for mRNA delivery, selecting from a vast library. Intriguingly, AGILE reveals cell-specific preferences for ionizable lipids, indicating tailoring for optimal delivery to varying cell types. These highlight AGILE's potential in expediting the development of customized LNPs, addressing the complex needs of mRNA delivery in clinical practice, thereby broadening the scope and efficacy of mRNA therapies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.