PI-Mamba：通过谱初始化流匹配实现线性时间的蛋白质主链生成
PI-Mamba: Linear-Time Protein Backbone Generation via Spectrally Initialized Flow Matching
AI总结：
PI-Mamba通过谱初始化和流匹配框架，在保证局部共价几何精确性的同时实现线性时间推断，实现了主链生成的高效与高保真。
摘要：
动机：蛋白质主链设计的生成模型必须同时确保几何有效性、采样效率和长序列的可扩展性。然而，大多数现有方法依赖于迭代细化、二次注意力机制或事后几何修正，导致计算效率与结构保真度之间存在持续的权衡。结果：我们提出物理指导的Mamba（PI-Mamba），一种生成模型，通过构造确保精确的局部共价几何，同时实现线性时间推断。PI-Mamba将可微约束执行操作符整合到流匹配框架中，并与基于Mamba的状态空间架构耦合。为了提高优化稳定性和主链真实性，我们引入了源自Rouse聚合物模型的谱初始化和辅助的顺式脯氨酸意识头。在基准任务中，PI-Mamba实现了0.0%的局部几何违规率和高设计性（scTM = $0.91\pm 0.03$，n = 100），并且在单个A5000 GPU（24 GB）上可扩展到超过2,000个残基的蛋白质。
英文摘要：
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).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
