Multiscale Molecular Simulations in the Age of Exascale Computing

The interior of living cells is highly crowded with proteins, nucleic acids, and metabolites—a setting that simultaneously demands large system sizes and long simulation timescales. Exascale computing, now realized on modern supercomputers, offers a qualitatively new opportunity to address this challenge. In this review, we examine how multiscale molecular dynamics simulations—spanning all-atom, coarse-grained, and ultra-coarse-grained representations—are transforming our understanding of macromolecular crowding and liquid–liquid phase separation. We survey recent advances in force-field development, machine-learning-assisted system construction, and high-performance computing–optimized molecular dynamics engines, with particular attention to how these tools make large-scale simulations practically feasible. Key physical insights discussed include the interplay between excluded volume and nonspecific sticky interactions, crowding-induced slowdown of diffusion, metabolite-binding kinetics, and the regulatory mechanisms governing biomolecular condensate formation and maturation. We outline how exascale platforms, integrated with machine learning and experimental data, are bringing predictive whole-cell simulations within reach.