AI Article Synopsis
- Lipid nanoparticles (LNPs) are key for mRNA vaccine delivery, and optimizing their lipid composition can enhance effectiveness by improving properties such as biodegradability and transfection efficiency.
- The study presents a novel method that utilizes Simplified Molecular-Input Line-Entry System (SMILES) to virtually screen LNPs, using large language models for better prediction of lipid properties.
- Results demonstrate this approach can increase transfection efficiency while saving time and costs in testing, with resources available online for further implementation.
Article Abstract
Motivation: Lipid 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.
Results: To 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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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11629694 | PMC |
| http://dx.doi.org/10.1093/bioinformatics/btae342 | DOI Listing |
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