提出少步生成蛋白骨架的新方法，速度显著提升。
SE(3)-MeanFlow: Few-Step Protein Backbone Generation on Lie Groups
- 在李代数空间直接建模旋转与平移的平均速度，无需数值积分。
- 仅用少数几步生成即达基线模型多步效果，且在少步时优势更明显。
- 适合需要高速设计蛋白骨架的研究者，尤其适用于高通量筛选场景。
蛋白质骨架生成的生成模型有望实现具有特定结构与功能属性的全新蛋白质设计。现有扩散与流匹配模型虽能在 SE(3)^N 空间生成高质量骨架，但推理需对常微分方程进行数百次网络评估，每次涉及李群指数映射，成为高通量设计的瓶颈。我们提出 SE(3)-MeanFlow，将 MeanFlow 从欧氏空间推广至蛋白构象的李群几何。在 so(3) 和 R^3 的李代数空间中，推导出旋转与平移的闭式平均速度表达式，实现无仿真训练目标。进一步引入 SE(3) alpha-Flow 目标，消除旋转分支中的雅可比向量积，作为预训练暖身阶段；随后切换至小 t 稳定化的 MeanFlow 损失，用于剩余预训练及基于修正的后训练。在蛋白骨架生成任务中，SE(3)-MeanFlow 在少步条件下表现超越或持平使用更多采样步数的基线模型，且优势随步数减少而扩大；修正机制使其在各预算下均领先，仅略降低多样性。
原文摘要 · Abstract (English)
Generative modeling of protein backbones promises the de novo design of proteins with prescribed structural and functional properties. Existing diffusion and flow-matching models produce high-quality backbones on SE(3)^N, but inference requires numerically integrating an ODE over hundreds of network evaluations, each involving a Lie group exponential map - a bottleneck for high-throughput design campaigns. We introduce SE(3)-MeanFlow, a few-step generative framework that extends MeanFlow from Euclidean space to the Lie group geometry of protein frames. Working natively in the Lie algebra so(3) and in R^3, we derive closed-form average-velocity identities for rotations and translations, giving simulation-free training targets. We further introduce an SE(3) alpha-Flow objective that removes the Jacobian-vector product from the rotation branch and serves as a warm-up stage, after which training switches to a small-t stabilized MeanFlow loss that is used for the remainder of pretraining and for rectification-based post-training. In protein backbone generation, SE(3)-MeanFlow matches or exceeds flow-matching baselines that use several times more sampling steps, and its advantage widens in the few-step regime, where rectification lets it lead at every matched budget - at a modest cost in diversity.
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