DnaFlow generates Molecular Ensembles by Multiscale Physics-Informed Flow Matching

Macromolecular function emerges from structures and their conformational ensembles, whose high-dimensional distributions remain difficult to learn from limited atomistic simulation data. Here, we introduce DnaFlow, a physics-informed flow matching framework and architecture that decomposes molecular fluctuations across spatial scales, focusing on the fast generation of structural ensembles of double-stranded DNA. Repeated forward passes generate atomistic ensembles that reproduce the sequence-dependent conformational distributions obtained from state-of-the-art atomistic molecular dynamics simulations. The central idea is to decompose the chain's local deformation field across spatial frequencies, assigning each scale a distinct generative pathway: slow bending modes are modeled with a latent prior derived from a polymer model, specifically the worm-like chain (WLC), high-frequency roughness by a calibrated noise field, and sequence-specific equilibrium structure with the flow matching objective. By learning only local, length-independent statistics while assigning global fluctuations to a physical prior, DnaFlow enables sequence-dependent extrapolation to chain lengths beyond those observed during training, extending the scope of existing generative models of DNA. More broadly, our approach provides a general framework for multiscale generative modeling that combines physical priors with learned, system-specific conformational distributions.