Align Your Structures: Generating Trajectories with Structure Pretraining for Molecular Dynamics
This work addresses the problem of limited data and high-dimensional modeling for molecular dynamics trajectory generation, offering a solution for computational chemistry and drug discovery, though it is incremental as it builds on existing diffusion models.
The authors tackled the challenge of generating molecular dynamics trajectories by proposing a framework that uses structure pretraining and an interpolator module, achieving remarkable improvements in accuracy for geometric, dynamical, and energetic measurements on datasets like QM9 and DRUGS.
Generating molecular dynamics (MD) trajectories using deep generative models has attracted increasing attention, yet remains inherently challenging due to the limited availability of MD data and the complexities involved in modeling high-dimensional MD distributions. To overcome these challenges, we propose a novel framework that leverages structure pretraining for MD trajectory generation. Specifically, we first train a diffusion-based structure generation model on a large-scale conformer dataset, on top of which we introduce an interpolator module trained on MD trajectory data, designed to enforce temporal consistency among generated structures. Our approach effectively harnesses abundant structural data to mitigate the scarcity of MD trajectory data and effectively decomposes the intricate MD modeling task into two manageable subproblems: structural generation and temporal alignment. We comprehensively evaluate our method on the QM9 and DRUGS small-molecule datasets across unconditional generation, forward simulation, and interpolation tasks, and further extend our framework and analysis to tetrapeptide and protein monomer systems. Experimental results confirm that our approach excels in generating chemically realistic MD trajectories, as evidenced by remarkable improvements of accuracy in geometric, dynamical, and energetic measurements.