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.