用迭代优化提升扩散模型生成蛋白质和DNA的性能
Reward-Guided Iterative Refinement in Diffusion Models at Test-Time with Applications to Protein and DNA Design
- 每轮生成先加噪再按奖励引导去噪，逐步修正错误
- 在蛋白质和细胞特异性DNA设计任务上优于现有方法
- 适合需要高精度序列设计的研究者使用
为充分挖掘扩散模型在推理阶段的潜力，我们关注如何优化下游奖励函数。尽管已有多种奖励引导生成算法，但多数方法仅采用单次生成，直接从完全噪声状态过渡到去噪状态。本文提出一种受进化算法启发的推理时奖励优化新框架，通过迭代过程实现逐步修正：每轮包含加噪与奖励引导去噪两个步骤。该机制能有效缓解奖励优化带来的误差累积。我们还提供了理论保证。实验表明，该方法在蛋白质及细胞类型特异性调控DNA设计任务中表现更优。代码已公开于 https://github.com/masa-ue/ProDifEvo-Refinement。
原文摘要 · Abstract (English)
To fully leverage the capabilities of diffusion models, we are often interested in optimizing downstream reward functions during inference. While numerous algorithms for reward-guided generation have been recently proposed due to their significance, current approaches predominantly focus on single-shot generation, transitioning from fully noised to denoised states. We propose a novel framework for inference-time reward optimization with diffusion models inspired by evolutionary algorithms. Our approach employs an iterative refinement process consisting of two steps in each iteration: noising and reward-guided denoising. This sequential refinement allows for the gradual correction of errors introduced during reward optimization. Besides, we provide a theoretical guarantee for our framework. Finally, we demonstrate its superior empirical performance in protein and cell-type-specific regulatory DNA design. The code is available at \href{https://github.com/masa-ue/ProDifEvo-Refinement}{https://github.com/masa-ue/ProDifEvo-Refinement}.
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