Fast Adversarial Generation of Molecular Dynamics Trajectories with Kinetic Fidelity

Molecular dynamics (MD) simulations yield atomic-level insights into molecular motion but struggle to reach the long timescales needed for rare events due to prohibitive computational costs. Generative machine-learning models (e.g., diffusion models and normalizing flows) offer a promising route to accelerate sampling, yet they generate independent equilibrium snapshots without temporal correlation or kinetic information. Autoregressive sequence models can learn time evolution by producing one frame at a time, but this stepwise generation often accumulates errors and drifts from true dynamics. Here, we propose a complementary approach inspired by advances in image and video generation: we treat finite MD trajectory segments as high-dimensional objects and learn their joint distribution using Generative Adversarial Networks (GANs). Using a Wasserstein GAN with gradient penalty, we directly generate entire time-series trajectories in one shot, that remain physically coherent over time without explicitly integrating the equations of motion. We demonstrate the generality of this method on molecular systems of increasing complexity: a 2D triple-well potential energy landscape, a protein-ligand binding process (cytochrome P450), the dynamics of an intrinsically disordered protein (-synuclein) in a latent coordinate space, and even the conditional generation of folding trajectories for the Trp-cage mini-protein. In all cases, the GAN-generated trajectories closely reproduce the true free-energy landscapes and kinetic signatures of the systems, while enabling efficient sampling of rare events that would ordinarily require months of conventional MD simulation.