  While RNA technologies hold immense therapeutic potential in a range of
applications from vaccination to gene editing, the broad implementation of
these technologies is hindered by the challenge of delivering these agents
effectively. Lipid nanoparticles have emerged as one of the most widely used
delivery agents, but their design optimization relies on laborious and costly
experimental methods. We propose to in silico optimize LNP design with machine
learning models. On a curated dataset of 622 LNPs from published studies, we
demonstrate the effectiveness of our model in predicting the transfection
efficiency of unseen LNPs, with the multilayer perceptron achieving a
classification accuracy of 98% on the test set. Our work represents a
pioneering effort in combining ML and LNP design, offering significant
potential for improving screening efficiency by computationally prioritizing
LNP candidates for experimental validation and accelerating the development of
effective mRNA delivery systems.
