用扩散模型直接压缩蛋白结构，生成更高效准确。
ProteinAE: Protein Diffusion Autoencoders for Structure Encoding
- 设计非等变扩散Transformer，端到端训练，仅一个目标函数。
- 重建精度超越现有自编码器，潜空间质量优异。
- 适合做蛋白结构生成的科研人员和算法开发者。
开发有效的蛋白质结构表征对推动蛋白质科学至关重要，尤其在蛋白质生成建模方面。现有方法常面临SE(3)流形复杂性、依赖离散标记化或多重训练目标等问题，影响模型优化与泛化能力。本文提出ProteinAE，一种新型简化版蛋白质扩散自编码器，可直接将蛋白主链坐标从E(3)映射至连续紧凑的潜空间。ProteinAE采用非等变扩散Transformer与瓶颈结构实现高效压缩，并以单一流匹配目标端到端训练，显著简化优化流程。实验表明，ProteinAE在重建质量上达到当前最优水平，优于现有自编码器。其潜空间可作为潜在扩散模型的基础，无需显式等变性设计，实现高效高质量结构生成，性能媲美主流基于结构的方法，显著优于以往潜空间方法。代码已开源：https://github.com/OnlyLoveKFC/ProteinAE_v1。
原文摘要 · Abstract (English)
Developing effective representations of protein structures is essential for advancing protein science, particularly for protein generative modeling. Current approaches often grapple with the complexities of the SE(3) manifold, rely on discrete tokenization, or the need for multiple training objectives, all of which can hinder the model optimization and generalization. We introduce ProteinAE, a novel and streamlined protein diffusion autoencoder designed to overcome these challenges by directly mapping protein backbone coordinates from E(3) into a continuous, compact latent space. ProteinAE employs a non-equivariant Diffusion Transformer with a bottleneck design for efficient compression and is trained end-to-end with a single flow matching objective, substantially simplifying the optimization pipeline. We demonstrate that ProteinAE achieves state-of-the-art reconstruction quality, outperforming existing autoencoders. The resulting latent space serves as a powerful foundation for a latent diffusion model that bypasses the need for explicit equivariance. This enables efficient, high-quality structure generation that is competitive with leading structure-based approaches and significantly outperforms prior latent-based methods. Code is available at https://github.com/OnlyLoveKFC/ProteinAE_v1.
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