ReQFlow：用于高效高质量蛋白质骨架生成的正则化四元数流

ReQFlow: Rectified Quaternion Flow for Efficient and High-Quality Protein Backbone Generation

February 20, 2025

作者: Angxiao Yue, Zichong Wang, Hongteng Xu

cs.AI

摘要

蛋白质骨架生成在从头蛋白质设计中占据核心地位，对众多生物与医学应用具有重要意义。尽管基于扩散和流模型的生成方法为这一挑战性任务提供了潜在解决方案，但它们往往生成可设计性欠佳的蛋白质，且存在计算效率低下的问题。本研究提出了一种新颖的校正四元数流（ReQFlow）匹配方法，用于快速生成高质量的蛋白质骨架。具体而言，我们的方法为蛋白质链中的每个残基从随机噪声生成局部平移和三维旋转，将每个三维旋转表示为单位四元数，并通过指数形式的球面线性插值（SLERP）构建其流。我们采用四元数流（QFlow）匹配训练模型，确保数值稳定性，并对QFlow模型进行校正以加速推理过程并提升生成蛋白质骨架的可设计性，从而提出了ReQFlow模型。实验表明，ReQFlow在蛋白质骨架生成上达到了最先进的性能，同时所需采样步骤大幅减少，推理时间显著缩短（例如，在生成长度为300的骨架时，比RFDiffusion快37倍，比Genie2快62倍），充分证明了其有效性与高效性。代码已公开于https://github.com/AngxiaoYue/ReQFlow。

English

Protein backbone generation plays a central role in de novo protein design and is significant for many biological and medical applications. Although diffusion and flow-based generative models provide potential solutions to this challenging task, they often generate proteins with undesired designability and suffer computational inefficiency. In this study, we propose a novel rectified quaternion flow (ReQFlow) matching method for fast and high-quality protein backbone generation. In particular, our method generates a local translation and a 3D rotation from random noise for each residue in a protein chain, which represents each 3D rotation as a unit quaternion and constructs its flow by spherical linear interpolation (SLERP) in an exponential format. We train the model by quaternion flow (QFlow) matching with guaranteed numerical stability and rectify the QFlow model to accelerate its inference and improve the designability of generated protein backbones, leading to the proposed ReQFlow model. Experiments show that ReQFlow achieves state-of-the-art performance in protein backbone generation while requiring much fewer sampling steps and significantly less inference time (e.g., being 37x faster than RFDiffusion and 62x faster than Genie2 when generating a backbone of length 300), demonstrating its effectiveness and efficiency. The code is available at https://github.com/AngxiaoYue/ReQFlow.