AReUReDi：基于多目标引导的退火校正更新优化离散流模型

摘要

设计满足多重且常相互冲突目标的序列，是治疗与生物分子工程中的核心挑战。现有的生成框架大多在连续空间内运作，依赖单一目标指导，而离散方法则缺乏对多目标帕累托最优性的保证。我们提出了AReUReDi（用于精炼离散流的退火校正更新），这是一种具有理论保证能收敛至帕累托前沿的离散优化算法。基于校正离散流（ReDi），AReUReDi结合了切比雪夫标量化、局部平衡提议以及退火Metropolis-Hastings更新，以偏向于帕累托最优状态进行采样，同时保持分布不变性。应用于肽和SMILES序列设计时，AReUReDi能同时优化多达五种治疗特性（包括亲和力、溶解性、溶血性、半衰期及抗污性），并超越了基于进化和扩散的基线方法。这些成果确立了AReUReDi作为一个强大的、基于序列的多属性生物分子生成框架的地位。

English

Designing sequences that satisfy multiple, often conflicting, objectives is a central challenge in therapeutic and biomolecular engineering. Existing generative frameworks largely operate in continuous spaces with single-objective guidance, while discrete approaches lack guarantees for multi-objective Pareto optimality. We introduce AReUReDi (Annealed Rectified Updates for Refining Discrete Flows), a discrete optimization algorithm with theoretical guarantees of convergence to the Pareto front. Building on Rectified Discrete Flows (ReDi), AReUReDi combines Tchebycheff scalarization, locally balanced proposals, and annealed Metropolis-Hastings updates to bias sampling toward Pareto-optimal states while preserving distributional invariance. Applied to peptide and SMILES sequence design, AReUReDi simultaneously optimizes up to five therapeutic properties (including affinity, solubility, hemolysis, half-life, and non-fouling) and outperforms both evolutionary and diffusion-based baselines. These results establish AReUReDi as a powerful, sequence-based framework for multi-property biomolecule generation.

AReUReDi：基于多目标引导的退火校正更新优化离散流模型

AReUReDi: Annealed Rectified Updates for Refining Discrete Flows with Multi-Objective Guidance

摘要

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