用线性时间生成无几何错误的蛋白质骨架，速度快且结构准确。
PI-Mamba: Linear-Time Protein Backbone Generation via Spectrally Initialized Flow Matching
- 通过可微约束模块和Mamba架构，直接构建符合化学键规则的骨架
- 零局部几何违规，设计能力高达scTM 0.91，支持超2000残基蛋白生成
- 适合需要高效、高保真蛋白质设计的研究者或药物研发团队
蛋白质骨架生成的生成模型需兼顾几何合理性、采样效率与长序列可扩展性。现有方法多依赖迭代优化、二次注意力机制或事后几何修正，导致计算效率与结构精度难以兼得。我们提出物理信息引导的Mamba模型（PI-Mamba），通过构造性方式确保局部共价几何精确，并实现线性时间推理。该模型将可微约束增强算子嵌入流匹配框架，结合Mamba状态空间架构。为提升优化稳定性与骨架真实性，引入基于Rouse聚合物模型的谱初始化及辅助顺式脯氨酸感知头。在基准任务中，PI-Mamba实现0.0%局部几何违规，设计能力达scTM = 0.91±0.03（n=100），并在单块A5000 GPU（24 GB）上成功生成超过2000残基的蛋白质。
原文摘要 · Abstract (English)
Motivation: Generative models for protein backbone design have to simultaneously ensure geometric validity, sampling efficiency, and scalability to long sequences. However, most existing approaches rely on iterative refinement, quadratic attention mechanisms, or post-hoc geometry correction, leading to a persistent trade-off between computational efficiency and structural fidelity. Results: We present Physics-Informed Mamba (PI-Mamba), a generative model that enforces exact local covalent geometry by construction while enabling linear-time inference. PI-Mamba integrates a differentiable constraint-enforcement operator into a flow-matching framework and couples it with a Mamba-based state-space architecture. To improve optimisation stability and backbone realism, we introduce a spectral initialization derived from the Rouse polymer model and an auxiliary cis-proline awareness head. Across benchmark tasks, PI-Mamba achieves 0.0\% local geometry violations and high designability (scTM = $0.91\pm 0.03$, n = 100), while scaling to proteins exceeding 2,000 residues on a single A5000 GPU (24 GB).
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