BioMD首次实现蛋白-配体长时序动态模拟，显著降低计算成本。
BioMD: All-atom Generative Model for Biomolecular Dynamics Simulation
- 分层预测与插值框架生成全原子级分子轨迹。
- 在DD-13M和MISATO数据集上生成高保真构象，重建误差低。
- 97.1%系统可在十次尝试内生成配体解离路径，适合药物研发。
分子动力学（MD）模拟在计算化学与药物发现中至关重要，但受限于高昂的计算成本，难以覆盖生物相关过程的长时尺度。尽管现有机器学习方法表现良好，却因缺乏MD数据集及建模长历史轨迹的高算力需求，难以生成长时序分子系统轨迹。本文提出BioMD，首个基于分层预测与插值框架的全原子生成模型，用于模拟蛋白-配体长时序动态。在DD-13M（配体解离）与MISATO数据集上，BioMD生成高度真实的构象，具备良好的物理合理性与低重建误差。此外，其在十次尝试内成功生成97.1%蛋白-配体系统的配体解离路径，展现出探索关键解离路径的能力。结果表明，BioMD可有效模拟复杂生物分子过程，广泛适用于计算化学与药物发现。
原文摘要 · Abstract (English)
Molecular dynamics (MD) simulations are essential tools in computational chemistry and drug discovery, offering crucial insights into dynamic molecular behavior. However, their utility is significantly limited by substantial computational costs, which severely restrict accessible timescales for many biologically relevant processes. Despite the encouraging performance of existing machine learning (ML) methods, they struggle to generate extended biomolecular system trajectories, primarily due to the lack of MD datasets and the large computational demands of modeling long historical trajectories. Here, we introduce BioMD, the first all-atom generative model to simulate long-timescale protein-ligand dynamics using a hierarchical framework of forecasting and interpolation. We demonstrate the effectiveness and versatility of BioMD on the DD-13M (ligand unbinding) and MISATO datasets. For both datasets, BioMD generates highly realistic conformations, showing high physical plausibility and low reconstruction errors. Besides, BioMD successfully generates ligand unbinding paths for 97.1% of the protein-ligand systems within ten attempts, demonstrating its ability to explore critical unbinding pathways. Collectively, these results establish BioMD as a tool for simulating complex biomolecular processes, offering broad applicability for computational chemistry and drug discovery.
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