This short review explores recent strategies aimed at enhancing the reliability of biomolecular simulations. It is designed for a broad biophysical audience and provides an overview of key methodologies that advance biomolecular modeling toward larger length scales and longer timescales while maintaining high accuracy (see Figure ). The first section discusses the Figure : A cartoon illustrating the approaches for biomolecular simulations discussed in this review: (1) high-resolution models (all-atom MD), which are limited to small length and time scales but enhanced by machine learning potentials (MLPs); and (2) low-resolution models (coarse-grained MD), which enable significantly larger length and time scales, with backmapping to higher resolutions facilitated by ML. development of QM-based ML potentials for biomolecules and emphasizes successful application and limitations. The second section highlights several pragmatic CG potentials that have demonstrated versatility in describing biomolecular complexes. These include models for lipids, nucleic acids, polysaccharides, and their post-translational modifications, which have gained increasing support from the biophysical and physicochemical communities. The third section examines ML-driven approaches for backmapping CG trajectories to higher-resolution atomistic representations. Finally, we conclude by discussing the immediate challenges facing ML potentials, CG potentials, and backmapping methods in their pursuit of more transferable, accurate, and thermodynamically consistent biomolecular potentials.