用生成模型模拟大分子蛋白-配体动态，支持超1万原子系统。
HemePLM-Diffuse: A Scalable Generative Framework for Protein-Ligand Dynamics in Large Biomolecular System
- 基于SE(3)不变的分词与时间感知注意力，生成精准轨迹。
- 在超过1万原子系统中实现高精度路径采样与配体补全。
- 适合药物设计与大分子动力学研究者使用。
理解蛋白-配体复合物的长时间尺度动态对药物发现和结构生物学至关重要，但对大型生物分子系统仍面临计算挑战。本文提出HemePLM-Diffuse，一种创新的生成式Transformer模型，可准确模拟蛋白-配体轨迹，补全缺失的配体片段，并在超过10,000个原子的系统中采样过渡路径。该模型采用SE(3)-不变的分词方法处理蛋白与配体，并利用时间感知交叉注意力扩散机制有效捕捉原子运动。通过3CQV HEME系统的验证，其在准确性与可扩展性上均优于TorchMD-Net、MDGEN和Uni-Mol等先进模型。
原文摘要 · Abstract (English)
Comprehending the long-timescale dynamics of protein-ligand complexes is very important for drug discovery and structural biology, but it continues to be computationally challenging for large biomolecular systems. We introduce HemePLM-Diffuse, an innovative generative transformer model that is designed for accurate simulation of protein-ligand trajectories, inpaints the missing ligand fragments, and sample transition paths in systems with more than 10,000 atoms. HemePLM-Diffuse has features of SE(3)-Invariant tokenization approach for proteins and ligands, that utilizes time-aware cross-attentional diffusion to effectively capture atomic motion. We also demonstrate its capabilities using the 3CQV HEME system, showing enhanced accuracy and scalability compared to leading models such as TorchMD-Net, MDGEN, and Uni-Mol.
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