Scale-Recursive Generation of Long-Horizon Molecular Dynamics Trajectories

Molecular dynamics (MD) simulations are a standard atomistic tool for studying protein folding, ligand binding, and other biomolecular processes, but their reliance on femtosecond-scale integration steps makes the simulation of millisecond-scale events prohibitively expensive. Generative surrogates offer a promising acceleration route, yet long-horizon trajectory generation poses a memory–accuracy trade-off: autoregressive models accumulate errors across successive predictions, while full-horizon temporal attention evaluates quadratically many frame pairs. Generating shorter segments reduces the jointly modeled sequence length but can affect agreement with reference trajectory distributions. We introduce ReScale, a scale-recursive factorization and sampling procedure for conditional molecular trajectories. ReScale jointly denoises separately maintained temporal states through recursive cross-scale conditioning, using a common generator architecture with level-specific parameters. This enables longer jointly generated sequences under practical memory constraints without sequential short-segment rollouts. We evaluate forward simulation on tetrapeptide and fast-folding protein systems, and transition path sampling on tetrapeptides. ReScale improves agreement with reference torsional distributions, populations along TICA coordinates, and MSM state occupations. Memory-capacity tests demonstrate 4.5× longer jointly generated sequences than MDGen at a 27.5 GiB peak-memory budget, while forward-simulation benchmarks show up to a 6× inference speedup over MDGen. Results on held-out proteins further support scale-recursive generation as a promising approach to scalable MD trajectory modeling.