Machine Learning and Ranking-Based Evaluation for Prioritizing High-Potency Ionizable Lipids in LNP-Mediated RNA Delivery

The application of machine learning (ML) models to accelerate the discovery of high-transfection-potency ionizable lipids has gained significant momentum in advancing lipid nanoparticle (LNP)-mediated RNA delivery. In the present study, we adopt a screening-oriented evaluation framework based on early-recognition ranking metrics tailored to high-throughput discovery. Model performance was assessed using the enrichment factor (EF), normalized discounted cumulative gain (NDCG), and HitRate at the top 10% of the ranked list, with uncertainty quantified via 1000 nonparametric bootstrap resamples. To assess robustness of conclusions, additional analyses were conducted at the top 1% and top 5% thresholds, reflecting increasingly stringent prioritization scenarios. Four predictive models—XGBoost, Random Forest, Elastic Net, and Quantile Regression Forest—were evaluated across three molecular feature representations, circular Morgan fingerprints, expert-crafted descriptors, and Grover graph embeddings, using a held-out test set. Across all models and thresholds, Morgan fingerprints consistently yielded superior early-recognition performance. The best-performing configuration—XGBoost with Morgan fingerprints—achieved EF@10% = 4.850 (95% CI [3.182, 6.818]), NDCG@10% = 0.628 (95% CI [0.234, 0.909]), and HitRate@10% = 0.493 (95% CI [0.318, 0.683]), corresponding to nearly fivefold enrichment over random selection and identification of highly potent lipids in approximately half of the prioritized candidates. Threshold-sensitivity analyses revealed that although stricter cutoffs (top 1% and top 5%) exhibit greater variability, the relative performance ordering of molecular representations remains stable. Bootstrap distributional comparisons further demonstrate that Morgan fingerprints provide not only higher but also more consistent screening performance than expert descriptors and Grover embeddings. Collectively, these results indicate that molecular representation—rather than model architecture—is the primary determinant of early-recognition performance in ionizable lipid discovery and that this conclusion is robust across multiple screening depths.