LANTERN: A Machine Learning Framework for Lipid Nanoparticle Transfection Efficiency Prediction

TL;DR

LANTERN is a machine learning framework that accurately predicts lipid nanoparticle transfection efficiency, facilitating faster design of RNA delivery systems by leveraging chemically informative features and robust modeling techniques.

Contribution

The paper introduces LANTERN, a new ML framework that outperforms existing models in predicting transfection efficiency using simple, interpretable features and robust algorithms.

Findings

Multi-layer perceptron with Morgan fingerprints achieved R^2=0.8161.

LANTERN models outperform AGILE in predictive accuracy.

Chemically informative features improve model performance.

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.