用等变随机插值直接连接蛋白构象，加速动态模拟。
EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic Interpolants
- 基于SO(3)等变性设计，直接跨时间步预测蛋白构象变化。
- 在快速折叠蛋白数据上达到当前最优动态模拟性能。
- 可迁移性强，适合需高效蛋白动力学建模的研究者。
解析蛋白质构象动态对理解其功能机制至关重要。虽然分子动力学（MD）能精确模拟蛋白运动，但计算成本过高限制了实际应用。为解决此问题，已有研究提出多种基于生成模型的加速方法，多依赖从先验分布采样进行传输，而这些分布常远离真实数据流形。近期提出的随机插值框架则可实现任意分布端点间的传输。在此基础上，我们提出EquiJump，一种可迁移的SO(3)-等变模型，可直接连接全原子蛋白动力学模拟的时间步。该方法统一了多种采样策略，并在快速折叠蛋白的轨迹数据上与现有模型对比验证。结果表明，EquiJump在所有快速折叠蛋白上均实现当前最优的动态模拟性能，且具备良好可迁移性。
原文摘要 · Abstract (English)
Mapping the conformational dynamics of proteins is crucial for elucidating their functional mechanisms. While Molecular Dynamics (MD) simulation enables detailed time evolution of protein motion, its computational toll hinders its use in practice. To address this challenge, multiple deep learning models for reproducing and accelerating MD have been proposed drawing on transport-based generative methods. However, existing work focuses on generation through transport of samples from prior distributions, that can often be distant from the data manifold. The recently proposed framework of stochastic interpolants, instead, enables transport between arbitrary distribution endpoints. Building upon this work, we introduce EquiJump, a transferable SO(3)-equivariant model that bridges all-atom protein dynamics simulation time steps directly. Our approach unifies diverse sampling methods and is benchmarked against existing models on trajectory data of fast folding proteins. EquiJump achieves state-of-the-art results on dynamics simulation with a transferable model on all of the fast folding proteins.
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