Multi-Objective-Guided Discrete Flow Matching for Controllable
  Biological Sequence Design
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.
6822c81dbda1314b4c967f53
Tong Chen
6822c81dbda1314b4c967f54
Yinuo Zhang
admin_assigned
2025-05-13T08:41:33.531Z
64ffdd2ae3201fff884c257f
https://cdn-avatars.huggingface.co/v1/production/uploads/64ffdd2ae3201fff884c257f/EZXWdkW33wfoGmYGZLdkh.png
z
user
yinuozhang
6822c81dbda1314b4c967f55
Sophia Tang
admin_assigned
2025-05-13T08:41:19.741Z
66d89392c81167fc5e1bdada
/avatars/4d06f2abec11e471a918353d687f77fe.svg
Sophia Tang
user
sophtang
6822c81dbda1314b4c967f56
Pranam Chatterjee
extracted_confirmed
2025-06-12T03:12:51.316Z
64cd5b3f0494187a9e8b7c69
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/eo9HiGbvCxHQZ-QJB1Y_o.jpeg
Pranam Chatterjee
user
pranamanam
Multi-Objective-Guided Discrete Flow Matching for Controllable
  Biological Sequence Design
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.
MOG-DFM, a general framework, steers pretrained discrete-time flow matching generators for Pareto-efficient multi-objective sequence design in biomolecule engineering.
flow matching models discrete-time flow matching hybrid rank-directional score adaptive hypercone filter discrete flow matching peptide generation enhancer DNA generation