ReviewActa pharmaceutica Sinica. B2026
Artificial intelligence-guided design of lipid nanoparticles for mRNA delivery.
Review in Acta pharmaceutica Sinica. B, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Nanomedicine-based cancer immunotherapy: translational barriers, mechanistic strategies, and future perspectives.Drug delivery · 2026Review
- New insights into breast cancer therapy: application and mechanisms of novel targeted nano-formulation.International journal of pharmaceutics: X · 2026Review
- Review
- Nanomaterial-driven spatiotemporal autophagy modulation: The dual-edged sword in precision cancer therapy.Acta pharmaceutica Sinica. B · 2026Review
- Lipid Nanoparticles for Gene Therapy: Unresolved Challenges in Manufacturing, Transdermal Delivery, Machine Learning, Endosomal Escape, and the Protein Corona.Pharmaceutics · 2026Review
- Smart nanoparticle vaccines integrate nanotechnology artificial intelligence and immunoengineering for precision immunization.Discover nano · 2026Review
- Artificial Intelligence-Driven Nanomedicine: From Drug Formulation and Nanocarrier Design to Clinical Translation.Pharmaceutics · 2026Review
- Machine Learning-Driven QSAR Modeling for pKInternational journal of molecular sciences · 2026Article
- Functionalized Lipid Nanoparticles for Targeted RNA Delivery in Immune and Inflammatory Diseases.Biomedicines · 2026Review
- Industrial Perspective on the Manufacturing of Lipid Nanoparticles for Nucleic Acid Delivery.Pharmaceutics · 2026Review
- Artificial Intelligence and the Transformation of Cell and Gene Therapy Development.Pharmaceutics · 2026Review
- Designing intranasal formulations for Alzheimer's disease: A material-based perspective.Tzu chi medical journalReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lipid nanoparticles (LNPs) hold significant potential for mRNA-based therapeutics, as evidenced by their successful use in SARS-CoV-2 mRNA vaccines. LNPs effectively protect and transport mRNA to target sites, thereby ensuring its stability and efficient transfection. Despite the progress, some challenges remain in the development of mRNA-LNP delivery systems, such as limited targeting specificity, the complexity of formulations, and the time-consuming and high-throughput screening process. Artificial intelligence (AI) has emerged as a powerful tool to address these challenges, accelerating the design and optimization process of LNPs. AI-guided approaches can improve the efficiency of lipid structure and formulation screening by rapidly identifying key design parameters and employing predictive modeling to optimize LNP properties. The combination of AI and LNP technology offers significant advantages, including enabling the design of more personalized and precise delivery systems, streamlining the development process, and reducing the cost. This review discusses recent advancements in AI-guided mRNA-LNP delivery systems and highlights their potential to revolutionize mRNA therapeutics.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.