ReQFlow: Rectified Quaternion Flow for Efficient and High-Quality
  Protein Backbone Generation
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.
67bc3393057a4685851067c9
Angxiao Yue
claimed_verified
2025-02-24T09:06:54.271Z
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/avatars/442e61765cb755f55540192e9a80cf80.svg
AngxiaoYue
user
AngxiaoYue
67bc3393057a4685851067ca
Zichong Wang
claimed_verified
2025-02-24T11:54:58.392Z
669a2fd15bd3f749a3eb7b65
/avatars/7b23f892d2dc8fc87f62469dc02524ac.svg
ZiChong Wang
user
EatEatEatEat
67bc3393057a4685851067cb
Hongteng Xu
claimed_verified
2025-02-24T13:24:57.462Z
67bc72956d5bfdc989e194dd
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/LB6fZyznoTrPKFUDJia2i.png
Hongteng Xu
user
Hongteng
ReQFlow: Rectified Quaternion Flow for Efficient and High-Quality
  Protein Backbone Generation
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.
A novel rectified quaternion flow method for generating high-quality protein backbones with fewer sampling steps and reduced inference time is proposed.
diffusion models flow-based generative models protein backbone generation quaternions spherical linear interpolation SLERP quaternion flow numerical stability ReQFlow