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

• URL: http://arxiv.org/abs/2505.07086v2
• Date: Wed, 14 May 2025 16:19:40 GMT
• Title: Multi-Objective-Guided Discrete Flow Matching for Controllable Biological Sequence Design
• Authors: Tong Chen, Yinuo Zhang, Sophia Tang, Pranam Chatterjee,
• Abstract summary: We present Multi-Objective-Guided Discrete Flow Matching (MOG-DFM), a general framework to steer any pretrained discrete flow matching generator.<n>At each sampling step, MOG-DFM computes a hybrid rank-directional score for candidate transitions.<n>We also trained two unconditional discrete flow matching models, PepDFM for diverse peptide generation and EnhancerDFM for functional enhancer DNA generation.
• Score: 5.64033072458324
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 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.

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