Understanding the molecular structure, dynamics, and reactivity requires bridging processes that occur across widely separated timescales. Conventional molecular dynamics simulations provide an atomistic resolution, but their femtosecond time steps limit access to the slow conformational changes and relaxation processes that govern chemical function. Here, we introduce a deep generative modeling framework that accelerates sampling of molecular dynamics by four orders of magnitude while retaining physical realism. Applied to small organic molecules and peptides, the approach enables quantitative characterization of equilibrium ensembles and dynamical relaxation processes that were previously only accessible by costly brute-force simulation. The method generalizes across chemical composition and system size, extrapolating to peptides larger than those used for training, and captures chemically meaningful transitions on extended timescales. By expanding the accessible range of molecular motions without sacrificing the atomistic detail, this approach opens opportunities for probing conformational landscapes, thermodynamics, and kinetics in systems central to chemistry and biophysics.