Biomolecular function arises from phenomena spanning several lengths and time scales, from electronic rearrangements and charge transfer to conformational dynamics and cellular-scale processes. To bridge these scales, multiscale modeling integrates quantum mechanical, classical atomistic, mesoscopic, and continuum descriptions into coherent frameworks. In this review, a brief overview of the state of the art methodologies used in multiscale approaches is provided, emphasizing how quantum-derived parameters inform force fields, how embedding techniques integrate active regions within complex environments, how atomistic molecular dynamics link to coarse-grained and continuum models, and how emerging machine-learning strategies accelerate or unify these techniques. Highlights of representative case studies of biomolecular electron transfers, radical-pair spin dynamics under magnetic fields, membrane phenomena, and molecular diffusion are discussed, showcasing how multiscale coupling delivers insights into real biophysical systems. Throughout, central challenges of transferability, sampling, error propagation, validation, and adaptive coupling are presented. Finally, an outline of future directions toward fully predictive, unified multiscale platforms for biomolecular physics is provided.
Gerhards et al. (Tue,) studied this question.