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

• URL: http://arxiv.org/abs/2510.00352v2
• Date: Fri, 03 Oct 2025 00:49:30 GMT
• Title: AReUReDi: Annealed Rectified Updates for Refining Discrete Flows with Multi-Objective Guidance
• Authors: Tong Chen, Yinuo Zhang, Pranam Chatterjee,
• Abstract summary: AReUReDi is a sequence-based framework for multi-property biomolecule generation.<n>It is applied to peptide and SMILES sequence design.<n>Results establish AReUReDi as a powerful, sequence-based framework for multi-property biomolecule generation.
• Score: 9.647706155737287
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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