Multi-Objective-Guided Discrete Flow Matching for Controllable Biological Sequence Design

Tong Chen, Yinuo Zhang, Sophia Vincoff, Sophia Tang, Pranam Chatterjee

Keywords: multi-objective optimization, discrete flow matching, Pareto front, hypercone filtering, peptide binders, enhancer DNA, controllable generation

TL;DR: MOG-DFM introduces multi-objective guidance for discrete flow matching, steering peptide and DNA sequence generation toward Pareto-efficient trade-offs across competing biological properties.

Abstract: 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.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

Submission Number: 11825