AReUReDi让生物序列设计同时优化多个矛盾目标，且有理论保证。
AReUReDi: Annealed Rectified Updates for Refining Discrete Flows with Multi-Objective Guidance
- 用退火修正更新机制，在离散空间中逐步逼近最优解集。
- 能同时优化5种治疗属性，性能超越进化与扩散模型基线。
- 适合需要多指标平衡的药物分子生成任务。
在治疗和生物分子工程中，设计满足多个常冲突目标的序列是一项核心挑战。现有生成框架多在连续空间中进行单目标引导，而离散方法缺乏对多目标帕累托最优的保证。本文提出AReUReDi（Annealed Rectified Updates for Refining Discrete Flows），一种具有收敛到帕累托前沿理论保证的离散优化算法。基于修正离散流（ReDi），AReUReDi结合切比雪夫标量化、局部均衡提议和退火梅特罗波利斯更新，引导采样向帕累托最优状态偏移，同时保持分布不变性。应用于肽和SMILES序列设计，AReUReDi可同时优化多达五项治疗特性（包括亲和力、溶解度、溶血性、半衰期和非黏附性），并优于进化算法和扩散基线模型。结果表明，AReUReDi是一种强大的、基于序列的多属性生物分子生成框架。
原文摘要 · Abstract (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.
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