2025-11-27 ミュンヘン大学(LMU)
<関連情報>
- https://www.lmu.de/en/newsroom/news-overview/news/computational-framework-for-therapeutic-rna-carrier-design.html
- https://pubs.acs.org/doi/10.1021/jacs.5c12694
ビットから結合へ:機械学習と分子動力学のコンビナトリアルアプローチを用いたリボ核酸ナノキャリアの高スループット仮想スクリーニング From Bits to Bonds: High-Throughput Virtual Screening of Ribonucleic Acid Nanocarriers Using a Combinatorial Approach of Machine Learning and Molecular Dynamics
Felix Sieber-Schäfer,Jonas Binder,Tim Münchrath,Katharina M. Steinegger,Min Jiang,Benjamin Winkeljann,Wolfgang Friess,and Olivia M. Merkel
Journal of the American Chemical Society Published: November 26, 2025
DOI:https://doi.org/10.1021/jacs.5c12694
Abstract
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, logP measurements, siRNA encapsulation assays, and cell culture knockdown experiments. This integrated approach provides a powerful tool for the de novo design and rapid virtual screening of optimized polymeric siRNA delivery systems.
