ArticleNature biomedical engineering2026
Artificial intelligence-guided design of LNPs for in vivo targeted mRNA delivery via analysis of the spatial conformation of ionizable lipids.
Article in Nature biomedical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Artificial intelligence-assisted design of self-assembling peptide hydrogels for neural regeneration: Principles and opportunities.Bioactive materials · 2027Review
- Novel EG95-based mRNA vaccines induce long-term protective immunity against echinococcosis.PLoS pathogens · 2026Article
- Review
- Cancer Drug Delivery with Nanoparticles and Biomolecules: Stimuli-Responsive, Theranostic, and AI-Guided Approaches.Materials (Basel, Switzerland) · 2026Review
- Nuciferine-Loaded Lipid Nanoparticle Microneedles Alleviate Intervertebral Disc Degeneration by Restoring Nucleus Pulposus Cell Homeostasis.Pharmaceutics · 2026Article
- Recent Advances in Lipid Nanoparticle-Mediated Respiratory and Gastrointestinal Mucosal Delivery of Nucleic Acids.Bioengineering (Basel, Switzerland) · 2026Review
- Advances in improving cancer immunotherapy with nanotechnology: from smart nanoparticles to synergistic combination strategies.Molecular cancer · 2026Review
- Review
- Decoding Undesirable Inflammatory Responses of Nucleic Acid-Delivering Lipid Nanoparticles.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors.
Funding
Abstract
Efficient mRNA delivery to specific tissues requires optimized ionizable lipids, yet the role of lipid spatial conformation in organ targeting and endosomal escape remains underexplored. Here we developed a library of lipids with diverse amino heads, degradable linkers and hydrophobic tails, generating distinct three-dimensional conformations. Molecular dynamics simulations revealed the dynamic conformations of these lipids during organic-aqueous phase transitions, and experimental validation confirmed that head and tail arrangements are key determinants of delivery efficiency and organ specificity. To accelerate lipid discovery, dynamic conformation data were converted into 2D density images to train machine learning models for lipid selection. AI-guided candidates, notably lipid P1, adopted stable three-tail cone-shaped conformations that promoted IgM protein corona formation and enabled spleen-targeted mRNA delivery. In preclinical models, P1-based mRNA vaccines triggered strong antibody and T-cell responses, leading to marked tumour suppression. These results highlight the pivotal role of lipid spatial conformation and the potential of AI-driven strategies to optimize lipid nanoparticles for organ-specific mRNA delivery.
Identifiers
41851281What OpenQuestion 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 OpenQuestion graph.