Building RNA coarse-grained force fields: Design principles and training strategies

The development of accurate and efficient computational models is essential for understanding RNA structures and biological pathways; however, the application of all-atom models to large RNA systems is often limited by their vast degrees of freedom and high computational cost. Consequently, various coarse-grained (CG) models have been developed to enhance computational efficiency while maintaining structural accuracy. Here, we discuss the major considerations in the force field development for current RNA CG models, as well as the design principles and training strategies employed in building RNA CG force fields. Additionally, we explore the integration of structural information such as sequence-dependent effects, secondary structures, and tertiary motifs into these models. Finally, we provide an outlook on the emerging role of machine learning as a promising direction for developing more versatile and accurate coarse-grained models for RNA molecules.