title: Multi-Objective-Guided Discrete Flow Matching for Controllable
    Biological Sequence Design
summary: Designing biological sequences that satisfy multiple, often conflicting,
  functional and biophysical criteria remains a central challenge in biomolecule
  engineering. While discrete flow matching models have recently shown promise
  for efficient sampling in high-dimensional sequence spaces, existing approaches
  address only single objectives or require continuous embeddings that can
  distort discrete distributions. We present Multi-Objective-Guided Discrete Flow
  Matching (MOG-DFM), a general framework to steer any pretrained discrete-time
  flow matching generator toward Pareto-efficient trade-offs across multiple
  scalar objectives. At each sampling step, MOG-DFM computes a hybrid
  rank-directional score for candidate transitions and applies an adaptive
  hypercone filter to enforce consistent multi-objective progression. We also
  trained two unconditional discrete flow matching models, PepDFM for diverse
  peptide generation and EnhancerDFM for functional enhancer DNA generation, as
  base generation models for MOG-DFM. We demonstrate MOG-DFM's effectiveness in
  generating peptide binders optimized across five properties (hemolysis,
  non-fouling, solubility, half-life, and binding affinity), and in designing DNA
  sequences with specific enhancer classes and DNA shapes. In total, MOG-DFM
  proves to be a powerful tool for multi-property-guided biomolecule sequence
  design.
paper_authors._id: 6822c81dbda1314b4c967f53
paper_authors.name: Tong Chen
paper_authors._id: 6822c81dbda1314b4c967f54
paper_authors.name: Yinuo Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-13T08:41:33.531Z
paper_authors._id: 64ffdd2ae3201fff884c257f
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64ffdd2ae3201fff884c257f/EZXWdkW33wfoGmYGZLdkh.png
paper_authors.fullname: z
paper_authors.type: user
paper_authors.user: yinuozhang
paper_authors._id: 6822c81dbda1314b4c967f55
paper_authors.name: Sophia Tang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-13T08:41:19.741Z
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: 6822c81dbda1314b4c967f56
paper_authors.name: Pranam Chatterjee
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-05-13T04:18:39.835Z
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: Multi-Objective-Guided Discrete Flow Matching for Controllable
    Biological Sequence Design
paper_summary: Designing biological sequences that satisfy multiple, often conflicting,
  functional and biophysical criteria remains a central challenge in biomolecule
  engineering. While discrete flow matching models have recently shown promise
  for efficient sampling in high-dimensional sequence spaces, existing approaches
  address only single objectives or require continuous embeddings that can
  distort discrete distributions. We present Multi-Objective-Guided Discrete Flow
  Matching (MOG-DFM), a general framework to steer any pretrained discrete-time
  flow matching generator toward Pareto-efficient trade-offs across multiple
  scalar objectives. At each sampling step, MOG-DFM computes a hybrid
  rank-directional score for candidate transitions and applies an adaptive
  hypercone filter to enforce consistent multi-objective progression. We also
  trained two unconditional discrete flow matching models, PepDFM for diverse
  peptide generation and EnhancerDFM for functional enhancer DNA generation, as
  base generation models for MOG-DFM. We demonstrate MOG-DFM's effectiveness in
  generating peptide binders optimized across five properties (hemolysis,
  non-fouling, solubility, half-life, and binding affinity), and in designing DNA
  sequences with specific enhancer classes and DNA shapes. In total, MOG-DFM
  proves to be a powerful tool for multi-property-guided biomolecule sequence
  design.
paper_ai_summary: MOG-DFM, a general framework, steers pretrained discrete-time flow matching generators for Pareto-efficient multi-objective sequence design in biomolecule engineering.
paper_ai_keywords: flow matching models discrete-time flow matching hybrid rank-directional score adaptive hypercone filter discrete flow matching peptide generation enhancer DNA generation