UniSim: A Unified Simulator for Time-Coarsened Dynamics of Biomolecules
This addresses the problem of limited transferability in deep learning-based MD simulations for researchers in computational biology and chemistry, though it appears incremental as it builds on existing frameworks.
The paper tackles the trade-off between accuracy and efficiency in Molecular Dynamics simulations by proposing UniSim, a unified simulator that leverages cross-domain knowledge, achieving highly competitive performance across small molecules, peptides, and proteins.
Molecular Dynamics (MD) simulations are essential for understanding the atomic-level behavior of molecular systems, giving insights into their transitions and interactions. However, classical MD techniques are limited by the trade-off between accuracy and efficiency, while recent deep learning-based improvements have mostly focused on single-domain molecules, lacking transferability to unfamiliar molecular systems. Therefore, we propose \textbf{Uni}fied \textbf{Sim}ulator (UniSim), which leverages cross-domain knowledge to enhance the understanding of atomic interactions. First, we employ a multi-head pretraining approach to learn a unified atomic representation model from a large and diverse set of molecular data. Then, based on the stochastic interpolant framework, we learn the state transition patterns over long timesteps from MD trajectories, and introduce a force guidance module for rapidly adapting to different chemical environments. Our experiments demonstrate that UniSim achieves highly competitive performance across small molecules, peptides, and proteins.