Gumbel-Softmax Flow Matching with Straight-Through Guidance for
  Controllable Biological Sequence Generation
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.
67e35ca7363374850440d91d
Sophia Tang
claimed_verified
2025-05-13T10:27:28.822Z
66d89392c81167fc5e1bdada
/avatars/4d06f2abec11e471a918353d687f77fe.svg
Sophia Tang
user
sophtang
67e35ca7363374850440d91e
Yinuo Zhang
67e35ca7363374850440d91f
Alexander Tong
67e35ca7363374850440d920
Pranam Chatterjee
extracted_confirmed
2025-03-26T01:57:51.167Z
64cd5b3f0494187a9e8b7c69
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/eo9HiGbvCxHQZ-QJB1Y_o.jpeg
Pranam Chatterjee
user
pranamanam
Gumbel-Softmax Flow Matching with Straight-Through Guidance for
  Controllable Biological Sequence Generation
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.
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.
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