LANTERN: A Machine Learning Framework for Lipid Nanoparticle Transfection Efficiency Prediction
Journal:
arXiv
Published Date:
Jul 3, 2025
Abstract
The discovery of new ionizable lipids for efficient lipid nanoparticle
(LNP)-mediated RNA delivery remains a critical bottleneck for RNA-based
therapeutics development. Recent advances have highlighted the potential of
machine learning (ML) to predict transfection efficiency from molecular
structure, enabling high-throughput virtual screening and accelerating lead
identification. However, existing approaches are hindered by inadequate data
quality, ineffective feature representations, low predictive accuracy, and poor
generalizability. Here, we present LANTERN (Lipid nANoparticle Transfection
Efficiency pRedictioN), a robust ML framework for predicting transfection
efficiency based on ionizable lipid representation. We benchmarked a diverse
set of ML models against AGILE, a previously published model developed for
transfection prediction. Our results show that combining simpler models with
chemically informative features, particularly count-based Morgan fingerprints,
outperforms more complex models that rely on internally learned embeddings,
such as AGILE. We also show that a multi-layer perceptron trained on a
combination of Morgan fingerprints and Expert descriptors achieved the highest
performance ($\text{R}^2$ = 0.8161, r = 0.9053), significantly exceeding AGILE
($\text{R}^2$ = 0.2655, r = 0.5488). We show that the models in LANTERN
consistently have strong performance across multiple evaluation metrics. Thus,
LANTERN offers a robust benchmarking framework for LNP transfection prediction
and serves as a valuable tool for accelerating lipid-based RNA delivery systems
design.
