AReUReDi：多目标引导下离散流精炼的退火校正更新

摘要

設計滿足多重且往往相互衝突目標的序列，是治療與生物分子工程中的核心挑戰。現有的生成框架主要在連續空間中運作，並依循單一目標指導，而離散方法則缺乏對多目標帕累托最優性的保證。我們引入了AReUReDi（退火校正更新以精煉離散流），這是一種具有理論保證能收斂至帕累托前沿的離散優化算法。基於校正離散流（ReDi），AReUReDi結合了切比雪夫標量化、局部平衡提案以及退火Metropolis-Hastings更新，以偏向於帕累托最優狀態的採樣，同時保持分佈不變性。應用於肽和SMILES序列設計時，AReUReDi同時優化了多達五種治療特性（包括親和力、溶解度、溶血性、半衰期和非污損性），並超越了基於進化和擴散的基準方法。這些成果確立了AReUReDi作為一個強大的、基於序列的多屬性生物分子生成框架。

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：多目标引导下离散流精炼的退火校正更新

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

摘要

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