title: ReQFlow: Rectified Quaternion Flow for Efficient and High-Quality
    Protein Backbone Generation
summary: 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.
paper_authors._id: 67bc3393057a4685851067c9
paper_authors.name: Angxiao Yue
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-24T09:06:54.271Z
paper_authors._id: 63021e665e305a35cb09cb35
paper_authors.avatarUrl: /avatars/442e61765cb755f55540192e9a80cf80.svg
paper_authors.fullname: AngxiaoYue
paper_authors.type: user
paper_authors.user: AngxiaoYue
paper_authors._id: 67bc3393057a4685851067ca
paper_authors.name: Zichong Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-24T11:54:58.392Z
paper_authors._id: 669a2fd15bd3f749a3eb7b65
paper_authors.avatarUrl: /avatars/7b23f892d2dc8fc87f62469dc02524ac.svg
paper_authors.fullname: ZiChong Wang
paper_authors.type: user
paper_authors.user: EatEatEatEat
paper_authors._id: 67bc3393057a4685851067cb
paper_authors.name: Hongteng Xu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-24T13:24:57.462Z
paper_authors._id: 67bc72956d5bfdc989e194dd
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/LB6fZyznoTrPKFUDJia2i.png
paper_authors.fullname: Hongteng Xu
paper_authors.type: user
paper_authors.user: Hongteng
paper_title: ReQFlow: Rectified Quaternion Flow for Efficient and High-Quality
    Protein Backbone Generation
paper_summary: 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.
paper_ai_keywords: geometric diffusion flow-based generative models sparse flow (ReQFlow) local translation 3D rotation unit quaternion spherical linear interpolation (SLERP) quaternion flow (QFlow) ReQFlow model