ArticleBioinformatics (Oxford, England)2024
Representations of lipid nanoparticles using large language models for transfection efficiency prediction.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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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
11 citing papers in PubMed.
- Structural evolution of ionizable lipids for nucleic acid delivery.Nature reviews. Chemistry · 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
- Transformer-based multidimensional feature fusion for accurate prediction of lipid nanoparticles transfection efficiency.Briefings in bioinformatics · 2026Article
- Article
- mRNA delivery systems 2.0: Engineering extrahepatic delivery for non-vaccine therapeutics.Materials today. Bio · 2025Review
- ProtoMol: enhancing molecular property prediction via prototype-guided multimodal learning.Briefings in bioinformatics · 2025Article
- Artificial Intelligence-Driven Nanoarchitectonics for Smart Targeted Drug Delivery.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Emerging Approaches for the Discovery of Lipid-Based RNA Delivery Systems.Pharmaceutics · 2025Review
- Artificial Intelligence-Driven Strategies for Targeted Delivery and Enhanced Stability of RNA-Based Lipid Nanoparticle Cancer Vaccines.Pharmaceutics · 2025Review
- Computational Methods for Modeling Lipid-Mediated Active Pharmaceutical Ingredient Delivery.Molecular pharmaceutics · 2025Review
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
motivationLipid nanoparticles (LNPs) are the most widely used vehicles for mRNA vaccine delivery. The structure of the lipids composing the LNPs can have a major impact on the effectiveness of the mRNA payload. Several properties should be optimized to improve delivery and expression including biodegradability, synthetic accessibility, and transfection efficiency.
resultsTo optimize LNPs, we developed and tested models that enable the virtual screening of LNPs with high transfection efficiency. Our best method uses the lipid Simplified Molecular-Input Line-Entry System (SMILES) as inputs to a large language model. Large language model-generated embeddings are then used by a downstream gradient-boosting classifier. As we show, our method can more accurately predict lipid properties, which could lead to higher efficiency and reduced experimental time and costs. AVAILABILITY AND IMPLEMENTATION: Code and data links available at: https://github.com/Sanofi-Public/LipoBART.
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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.