title: Gumbel-Softmax Flow Matching with Straight-Through Guidance for
    Controllable Biological Sequence Generation
summary: Flow matching in the continuous simplex has emerged as a promising strategy
  for DNA sequence design, but struggles to scale to higher simplex dimensions
  required for peptide and protein generation. We introduce Gumbel-Softmax Flow
  and Score Matching, a generative framework on the simplex based on a novel
  Gumbel-Softmax interpolant with a time-dependent temperature. Using this
  interpolant, we introduce Gumbel-Softmax Flow Matching by deriving a
  parameterized velocity field that transports from smooth categorical
  distributions to distributions concentrated at a single vertex of the simplex.
  We alternatively present Gumbel-Softmax Score Matching which learns to regress
  the gradient of the probability density. Our framework enables high-quality,
  diverse generation and scales efficiently to higher-dimensional simplices. To
  enable training-free guidance, we propose Straight-Through Guided Flows
  (STGFlow), a classifier-based guidance method that leverages straight-through
  estimators to steer the unconditional velocity field toward optimal vertices of
  the simplex. STGFlow enables efficient inference-time guidance using
  classifiers pre-trained on clean sequences, and can be used with any discrete
  flow method. Together, these components form a robust framework for
  controllable de novo sequence generation. We demonstrate state-of-the-art
  performance in conditional DNA promoter design, sequence-only protein
  generation, and target-binding peptide design for rare disease treatment.
paper_authors._id: 67e35ca7363374850440d91d
paper_authors.name: Sophia Tang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-13T10:27:28.822Z
paper_authors._id: 66d89392c81167fc5e1bdada
paper_authors.avatarUrl: /avatars/4d06f2abec11e471a918353d687f77fe.svg
paper_authors.fullname: Sophia Tang
paper_authors.type: user
paper_authors.user: sophtang
paper_authors._id: 67e35ca7363374850440d91e
paper_authors.name: Yinuo Zhang
paper_authors._id: 67e35ca7363374850440d91f
paper_authors.name: Alexander Tong
paper_authors._id: 67e35ca7363374850440d920
paper_authors.name: Pranam Chatterjee
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-03-26T01:57:51.167Z
paper_authors._id: 64cd5b3f0494187a9e8b7c69
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/eo9HiGbvCxHQZ-QJB1Y_o.jpeg
paper_authors.fullname: Pranam Chatterjee
paper_authors.type: user
paper_authors.user: pranamanam
paper_title: Gumbel-Softmax Flow Matching with Straight-Through Guidance for
    Controllable Biological Sequence Generation
paper_summary: Flow matching in the continuous simplex has emerged as a promising strategy
  for DNA sequence design, but struggles to scale to higher simplex dimensions
  required for peptide and protein generation. We introduce Gumbel-Softmax Flow
  and Score Matching, a generative framework on the simplex based on a novel
  Gumbel-Softmax interpolant with a time-dependent temperature. Using this
  interpolant, we introduce Gumbel-Softmax Flow Matching by deriving a
  parameterized velocity field that transports from smooth categorical
  distributions to distributions concentrated at a single vertex of the simplex.
  We alternatively present Gumbel-Softmax Score Matching which learns to regress
  the gradient of the probability density. Our framework enables high-quality,
  diverse generation and scales efficiently to higher-dimensional simplices. To
  enable training-free guidance, we propose Straight-Through Guided Flows
  (STGFlow), a classifier-based guidance method that leverages straight-through
  estimators to steer the unconditional velocity field toward optimal vertices of
  the simplex. STGFlow enables efficient inference-time guidance using
  classifiers pre-trained on clean sequences, and can be used with any discrete
  flow method. Together, these components form a robust framework for
  controllable de novo sequence generation. We demonstrate state-of-the-art
  performance in conditional DNA promoter design, sequence-only protein
  generation, and target-binding peptide design for rare disease treatment.
paper_ai_summary: A generative framework using Gumbel-Softmax Flow Matching and Score Matching enables high-quality, diverse sequence generation and scales to higher dimensions, supported by a training-free guidance method.
paper_ai_keywords: Gumbel-Softmax Flow Score Matching time-dependent temperature parameterized velocity field categorical distributions gradient of the probability density Straight-Through Guided Flows STGFlow straight-through estimators discrete flow method sequence generation DNA promoter design protein generation target-binding peptide design