用碎片化扩散模型精准预测药物分子结合位点，效果远超传统方法。
SigmaDock: Untwisting Molecular Docking With Fragment-Based SE(3) Diffusion
- 将分子拆成刚性片段，基于SE(3)几何先验生成结合姿态
- 在PoseBusters数据集上命中率超79.9%，显著优于此前深度学习方法
- 首次在经典分割下超越物理方法，适合新靶点药物设计
确定配体与蛋白质的结合构象（即分子对接）是药物发现中的核心任务。生成式方法相比物理模型可实现更快、更优且更多样化的构象采样，但常受限于化学不合理输出、泛化能力差和高计算开销。为此，我们提出一种基于结构化学归纳偏置的新型分子分段方案，将配体分解为刚性片段。在此基础上，构建了SigmaDock——一种SE(3)黎曼扩散模型，通过学习在结合口袋内重组装这些刚性片段来生成结合姿态。在SE(3)空间中以片段为单位操作，既利用了成熟的几何先验，又避免了复杂扩散过程和训练不稳定性。实验表明，SigmaDock达到顶尖性能，在PoseBusters数据集上的Top-1成功率（RMSD<2 且PB有效）超过79.9%，显著高于近期深度学习方法的12.7–30.8%；同时对未见蛋白表现出一致泛化能力。SigmaDock是首个在经典PB训练-测试划分下超越传统物理对接的方法，标志着深度学习在分子建模中的可靠性与可行性实现重大突破。
原文摘要 · Abstract (English)
Determining the binding pose of a ligand to a protein, known as molecular docking, is a fundamental task in drug discovery. Generative approaches promise faster, improved, and more diverse pose sampling than physics-based methods, but are often hindered by chemically implausible outputs, poor generalisability, and high computational cost. To address these challenges, we introduce a novel fragmentation scheme, leveraging inductive biases from structural chemistry, to decompose ligands into rigid-body fragments. Building on this decomposition, we present SigmaDock, an SE(3) Riemannian diffusion model that generates poses by learning to reassemble these rigid bodies within the binding pocket. By operating at the level of fragments in SE(3), SigmaDock exploits well-established geometric priors while avoiding overly complex diffusion processes and unstable training dynamics. Experimentally, we show SigmaDock achieves state-of-the-art performance, reaching Top-1 success rates (RMSD<2 & PB-valid) above 79.9% on the PoseBusters set, compared to 12.7-30.8% reported by recent deep learning approaches, whilst demonstrating consistent generalisation to unseen proteins. SigmaDock is the first deep learning approach to surpass classical physics-based docking under the PB train-test split, marking a significant leap forward in the reliability and feasibility of deep learning for molecular modelling.
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