用最小作用量方法生成稳定肽序列，避免无效中间态。
Minimal-Action Discrete Schrödinger Bridge Matching for Peptide Sequence Design
- 基于氨基酸编辑图构建连续时间马尔可夫过程，引导生成路径。
- 通过控制场优化转移率，使路径始终在高概率区域，减少采样步数。
- 首次将分类器引导用于离散薛定谔桥模型，适合药物肽设计。
肽序列生成需在离散且高度受限的空间中进行，许多中间状态化学上不成立或不稳定。现有离散扩散与流模型依赖固定破坏过程或预设概率路径，常迫使生成过程经过低概率区域，需大量采样步骤。本文提出最小作用量离散薛定谔桥匹配（MadSBM），一种基于速率的肽设计生成框架，将生成建模为氨基酸编辑图上的受控连续时间马尔可夫过程。为使概率轨迹始终贴近高概率序列邻域，MadSBM：1）以预训练蛋白语言模型的逻辑值为生物启发参考过程；2）学习时变控制场，调整转移率以生成从掩码先验到数据分布的低作用量传输路径。最后引入指导机制，使采样过程向特定功能目标收敛，拓展治疗性肽的设计空间；据我们所知，这是首个将离散分类器引导应用于薛定谔桥生成模型的工作。
原文摘要 · Abstract (English)
Generative modeling of peptide sequences requires navigating a discrete and highly constrained space in which many intermediate states are chemically implausible or unstable. Existing discrete diffusion and flow-based methods rely on reversing fixed corruption processes or following prescribed probability paths, which can force generation through low-likelihood regions and require countless sampling steps. We introduce Minimal-action discrete Schrödinger Bridge Matching (MadSBM), a rate-based generative framework for peptide design that formulates generation as a controlled continuous-time Markov process on the amino-acid edit graph. To yield probability trajectories that remain near high-likelihood sequence neighborhoods throughout generation, MadSBM 1) defines generation relative to a biologically informed reference process derived from pre-trained protein language model logits and 2) learns a time-dependent control field that biases transition rates to produce low-action transport paths from a masked prior to the data distribution. We finally introduce guidance to the MadSBM sampling procedure towards a specific functional objective, expanding the design space of therapeutic peptides; to our knowledge, this represents the first-ever application of discrete classifier guidance to Schrödinger bridge-based generative models.
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