Representations of lipid nanoparticles using large language models for transfection efficiency prediction
Saeed Moayedpour, Jonathan Broadbent, Saleh Riahi, Michael Bailey, Hoa V. Thu, Dimitar A. Dobchev, Akshay Balsubramani, Ricardo Nascimento dos Santos, Lorenzo Kogler-Anele, Alejandro Corrochano-Navarro, Sizhen Li, Fernando Ulloa Montoya, Vikram Agarwal, Ziv Bar‐Joseph, Sven Jäger
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.