SaDiT：通过潜在结构标记化和扩散变换器实现高效的蛋白质骨架设计
SaDiT: Efficient Protein Backbone Design via Latent Structural Tokenization and Diffusion Transformers
AI总结：
SaDiT通过结合潜在结构标记化和扩散变换器，实现了高效的蛋白质骨架设计，提升了生成速度和结构可行性。
摘要：
生成模型在从头设计蛋白质骨架方面取得了显著成功，能够创造出新颖的蛋白质结构。然而，这些基于扩散的方法仍然计算上耗费精力，并且在大规模结构探索中速度不够理想。尽管最近的努力如Proteina引入了流匹配以提高采样效率，但结构压缩和加速的标记化潜力在蛋白质领域仍 largely 未被探索。在本工作中，我们提出SaDiT，一种新的框架，通过整合SaProt标记化与扩散变换器（DiT）架构来加速蛋白质骨架生成。SaDiT利用离散的潜在空间来表示蛋白质几何，显著降低了生成过程的复杂性，同时保持理论上的SE(3)等价性。为了进一步提高效率，我们引入了IPA标记缓存机制，通过在迭代采样过程中重用计算的标记状态来优化不变点注意力（IPA）层。实验结果表明，SaDiT在计算速度和结构可行性方面均优于最先进的模型，包括RFDiffusion和Proteina。我们评估了我们的模型在无条件骨架生成和折叠类别条件生成任务中的表现，其中SaDiT在捕捉复杂拓扑特征和高设计性方面表现出优越的能力。
英文摘要：
Generative models for de novo protein backbone design have achieved remarkable success in creating novel protein structures. However, these diffusion-based approaches remain computationally intensive and slower than desired for large-scale structural exploration. While recent efforts like Proteina have introduced flow-matching to improve sampling efficiency, the potential of tokenization for structural compression and acceleration remains largely unexplored in the protein domain. In this work, we present SaDiT, a novel framework that accelerates protein backbone generation by integrating SaProt Tokenization with a Diffusion Transformer (DiT) architecture. SaDiT leverages a discrete latent space to represent protein geometry, significantly reducing the complexity of the generation process while maintaining theoretical SE(3) equivalence. To further enhance efficiency, we introduce an IPA Token Cache mechanism that optimizes the Invariant Point Attention (IPA) layers by reusing computed token states during iterative sampling. Experimental results demonstrate that SaDiT outperforms state-of-the-art models, including RFDiffusion and Proteina, in both computational speed and structural viability. We evaluate our model across unconditional backbone generation and fold-class conditional generation tasks, where SaDiT shows superior ability to capture complex topological features with high designability.
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