From Bits to Bonds: High-Throughput Virtual Screening of Ribonucleic Acid Nanocarriers Using a Combinatorial Approach of Machine Learning and Molecular Dynamics

The implementation of high-throughput methods for fuelling the design of effective nanocarriers for RNA delivery remains challenging. Traditional experimental screening is resource-intensive, while purely computational approaches face limitations, such as data scarcity for machine learning models and the high computational cost of molecular dynamics simulations. This work introduces a high-throughput virtual screening platform, ″Bits2Bonds,″ integrating coarse-grained molecular dynamics simulations with machine learning-driven optimization to design novel poly(β-amino ester) (PBAE) carriers for therapeutic siRNA delivery. The platform evaluates virtual polymers using MD-based ″challenges″ that simulate key hurdles in nucleic acid delivery, such as membrane and siRNA interaction (association/dissociation). The computational framework was calibrated and validated against experimental data, including synthesis and characterization of four distinct PBAEs, log<i>P</i> measurements, siRNA encapsulation assays, and cell culture knockdown experiments. This integrated approach provides a powerful tool for the <i>de novo</i> design and rapid virtual screening of optimized polymeric siRNA delivery systems.