UniSim统一模拟生物分子动态，跨系统迁移能力强。
UniSim: A Unified Simulator for Time-Coarsened Dynamics of Biomolecules
- 用多头预训练学习统一原子表示，融合多领域数据
- 基于随机插值框架，长时序状态转移预测准确率高
- 可快速适应新化学环境，适合小分子到蛋白质研究
分子动力学（MD）模拟对理解分子系统原子级行为至关重要，可揭示其构象变化与相互作用。但传统方法受限于精度与效率的权衡，而近期深度学习方法多集中于单一分子类型，缺乏对陌生体系的泛化能力。为此，我们提出统一模拟器UniSim，利用跨域知识增强对原子相互作用的理解。首先，采用多头预训练方法从大规模多样化分子数据中学习统一原子表示模型；其次，基于随机插值框架，从MD轨迹中学习长时步的状态转移模式，并引入力引导模块以快速适应不同化学环境。实验表明，UniSim在小分子、肽类和蛋白质上均达到具有竞争力的性能。
原文摘要 · Abstract (English)
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.
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