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

Abstract

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 (TE). To optimize LNPs we developed and tested models that enable the virtual screening of LNPs with high TE. Our best method uses the lipid SMILES as inputs to a Large Language Model (LLM). LLM 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. Code and data links available at: https://github.com/Sanofi-Public/LipoBART.