Title: Unified Biomolecular Trajectory Generation via Pretrained Variational Bridge
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: deep generative model, molecular dynamics, trajectory generation, augmented bridge matching, adjoint matching

Abstract: Molecular Dynamics (MD) simulations provide a fundamental tool for characterizing molecular behavior at full atomic resolution, but their applicability is severely constrained by computational inefficiency. To address this, a surge of deep generative models has recently emerged to learn dynamics at coarsened timesteps for efficient trajectory generation. Nevertheless, most of these methods suffer from two main issues: (i) Non-pretrained models are limited to single-domain simulation; (ii) Pretrained approaches, while tailored for cross-domain scenarios, fail to leverage the structural information learned during pretraining in the generative process due to misaligned training objectives. Here, we propose the Pretrained Variational Bridge (PVB), which first maps the initial state into a noised latent space and then projects it to stage-specific target states using a decoder based on augmented bridge matching. This unifies training for both single-structure and paired trajectory data, ensuring the consistent utilization of extensive cross-domain structural knowledge across stages. Moreover, we incorporate RL optimization for protein-ligand complexes using adjoint matching, which enables the model to rapidly evolve toward the holo state within short simulations, showcasing the potential for efficient post-optimization of docking poses. Experiments on proteins and protein-ligand complexes demonstrate that PVB accurately reproduces thermodynamic and kinetic observables measured in MD simulations, while achieving remarkable generative stability compared with baselines.