ArticleNature biotechnology2025
Artificial intelligence-guided design of lipid nanoparticles for pulmonary gene therapy.
Article in Nature biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 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
85 citing papers in PubMed.
- Delivering the blueprint: Advances and challenges in mRNA therapeutics for the respiratory system.International journal of pharmaceutics: X · 2026Review
- Structural evolution of ionizable lipids for nucleic acid delivery.Nature reviews. Chemistry · 2026Review
- 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
- Dual pKa Lipid Nanoparticles for Lung-tropic mRNA Delivery and pH-Programmed Endosomal Escape.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Cancer Vaccine Development: Toward Artificial Intelligence-Assisted Personalized Cell Membrane Nanovaccine.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Nanomaterial Strategies for Pulmonary Delivery of Immunotherapeutics in Lung Cancer Treatment.Advanced healthcare materials · 2026Review
- Precision nanomedicine for pulmonary diseases: from molecular targeting to clinical translation.Signal transduction and targeted therapy · 2026Review
- Elucidating lipid nanoparticle properties and structure through biophysical analyses.Nature biotechnology · 2026Article
- Review
- 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
- Living Inorganic Nanomaterials: Design, Preparation, and Biomedical Applications.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- High-throughput microfluidics and machine learning-assisted screening of lipid nanoparticle formulations for siRNA delivery.Materials today. Bio · 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
- Target-Product and Translational Design Principles for Inhalable RNA Nanomedicines.Pharmaceutics · 2026Review
- Epigenetic editing approaches maturity: AI-driven precision design, delivery innovation, and the road to clinical translation.Clinical epigenetics · 2026Review
- Artificial intelligence in biologic drug discovery: A review of methodological evolution and therapeutic applications.Acta pharmaceutica Sinica. B · 2026Review
- The Use of Deep Learning in RNA Therapeutic Development.ACS nano · 2026Review
- Liposomal multimodal theranostics for bone disorders: from rational responsive delivery and biomimetic strategies to clinical translation.Journal of nanobiotechnology · 2026Review
25 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
20 authors.
Funding
Abstract
Ionizable lipids are a key component of lipid nanoparticles, the leading nonviral messenger RNA delivery technology. Here, to advance the identification of ionizable lipids beyond current methods, which rely on experimental screening and/or rational design, we introduce lipid optimization using neural networks, a deep-learning strategy for ionizable lipid design. We created a dataset of >9,000 lipid nanoparticle activity measurements and used it to train a directed message-passing neural network for prediction of nucleic acid delivery with diverse lipid structures. Lipid optimization using neural networks predicted RNA delivery in vitro and in vivo and extrapolated to structures divergent from the training set. We evaluated 1.6 million lipids in silico and identified two structures, FO-32 and FO-35, with local mRNA delivery to the mouse muscle and nasal mucosa. FO-32 matched the state of the art for nebulized mRNA delivery to the mouse lung, and both FO-32 and FO-35 efficiently delivered mRNA to ferret lungs. Overall, this work shows the utility of deep learning for improving nanoparticle delivery.
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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.