Representations of lipid nanoparticles using large language models for transfection efficiency prediction.

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 pr

SUBMITTER: Moayedpour S 

PROVIDER: S-EPMC11629694 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature