多目标引导的离散流匹配用于可控生物序列设计

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

May 11, 2025

作者: Tong Chen, Yinuo Zhang, Sophia Tang, Pranam Chatterjee

cs.AI

摘要

设计满足多种、往往相互冲突的功能与生物物理标准的生物序列，仍然是生物分子工程中的核心挑战。尽管离散流匹配模型近期在高维序列空间的高效采样方面展现出潜力，但现有方法仅针对单一目标，或需要依赖可能扭曲离散分布的连续嵌入。我们提出了多目标引导的离散流匹配（MOG-DFM），这是一个通用框架，旨在引导任何预训练的离散时间流匹配生成器，实现跨多个标量目标的帕累托有效权衡。在每一步采样中，MOG-DFM计算候选转移的混合排名-方向得分，并应用自适应超锥过滤器以确保多目标进展的一致性。我们还训练了两个无条件离散流匹配模型——用于多样化肽生成的PepDFM和用于功能性增强子DNA生成的EnhancerDFM，作为MOG-DFM的基础生成模型。我们展示了MOG-DFM在生成跨五个属性（溶血性、抗污性、溶解性、半衰期和结合亲和力）优化的肽结合剂，以及设计具有特定增强子类别和DNA形状的DNA序列方面的有效性。总体而言，MOG-DFM被证明是多属性引导生物分子序列设计的有力工具。

English

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.