ArticleNature communications2024
Artificial intelligence-driven rational design of ionizable lipids 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 95 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
95 citing papers in PubMed.
- Towards mRNA therapeutics 2.0.Nature reviews. Drug discovery · 2026Review
- Structural evolution of ionizable lipids for nucleic acid delivery.Nature reviews. Chemistry · 2026Review
- In silico discovery of natural and synthetic inhibitors targeting AKT1 in prostate cancer.Molecular diversity · 2026Article
- mRNA lipid nanoparticle vaccines: current status, challenges and future prospects.Molecular biomedicine · 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
- Review
- Multifunctional Nanomaterials for Precision Diagnostics and Drug Delivery: AI-Assisted Biosensing, Barrier-Directed Transport, Stimuli-Responsive Release, and Theranostic Integration.Molecules (Basel, Switzerland) · 2026Review
- Skin Relevant Biomaterials from Wound Healing, Medical Aesthetics, Flexible Electronics to Artificial Intelligence and Beyond.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Precision chemical engineering of dendrimers for nucleic acid delivery.Nature reviews. Chemistry · 2026Review
- Review
- Cancer Drug Delivery with Nanoparticles and Biomolecules: Stimuli-Responsive, Theranostic, and AI-Guided Approaches.Materials (Basel, Switzerland) · 2026Review
- 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
- Nontoxic mRNA vehicles based on electrically neutral self-assembling peptides.Nature communications · 2026Article
- RNA Size and Structure Modulate the Apparent pKa of Ionizable Lipid Nanoparticles.Bioconjugate chemistry · 2026Article
- Machine-Learning-Driven Optimization of Functional Excipients and Their Biointeractions in Drug Formulations.ACS pharmacology & translational science · 2026Review
- Two Classes of Protein Therapeutics: Why Dose-Response Architecture Defines the Boundary of mRNA Medicines.Pharmaceutics · 2026Review
- Next-Generation Nanocarrier Platforms for RNA Vaccines: Advances in Formulation, Stability Engineering, and Translational Manufacturing Challenges.Pharmaceutics · 2026Review
- AI meets high-throughput screening of LNPs.Nature biomedical engineering · 2026Article
- Tutorial: strategies for targeting organelles using nanoparticles.Nature protocols · 2026Review
35 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
10 authors.
Funding
Abstract
Lipid nanoparticles (LNPs) have proven effective in mRNA delivery, as evidenced by COVID-19 vaccines. Its key ingredient, ionizable lipids, is traditionally optimized by inefficient and costly experimental screening. This study leverages artificial intelligence (AI) and virtual screening to facilitate the rational design of ionizable lipids by predicting two key properties of LNPs, apparent pKa and mRNA delivery efficiency. Nearly 20 million ionizable lipids were evaluated through two iterations of AI-driven generation and screening, yielding three and six new molecules, respectively. In mouse test validation, one lipid from the initial iteration, featuring a benzene ring, demonstrated performance comparable to the control DLin-MC3-DMA (MC3). Notably, all six lipids from the second iteration equaled or outperformed MC3, with one exhibiting efficacy akin to a superior control lipid SM-102. Furthermore, the AI model is interpretable in structure-activity relationships.
Indexed as
Identifiers
What 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.