(308) Machine Learning-derived Insights in Design of Small Polymeric Nanoparticles for Gene Delivery

Introduction: Polymer nanoparticles are promising gene delivery vehicles due to their high chemical diversity and tunability. A major challenge in the field is identifying novel compositions with properties of interest. One key property is small particle size ( < 200 nm), which is important for cellular uptake and transfection (1). However, loading nanoparticles with a genetic payload can drastically change particle size. Predictive methods are needed to model which compositions remain small after introducing the payload.