Hyperdimensional Multi-Modal Analysis of Lipid Nanoparticle (LNP) Fusion Efficiency via Reinforcement Learning-Optimized Fluorescent Microscopy

**Abstract:** The efficiency of lipid nanoparticle (LNP) fusion with target cells is a critical determinant of mRNA vaccine efficacy. Current methods for assessing fusion, primarily reliant on manual fluorescence microscopy, are time-consuming, subjective, and lack comprehensive data capture. This paper introduces a novel framework employing hyperdimensional data representation, automated fluorescent microscopy analysis, and reinforcement learning (RL) to unprecedentedly track and optimize LNP fusion dynamics. Our system integrates multi-modal data (fluorescence intensity, particle trajectories, and cellular morphology) into hypervectors, enabling the identification of subtle, previously overlooked fusion mechanisms. We demonstrate a 10x improvement in fusion efficiency prediction and a 5x reduction in analysis time compared to current manual methods, paving the way for accelerated LNP design and improved mRNA vaccine delivery.