引导生成模型进行蛋白质设计：对齐与条件化策略
Steering Generative Models for Protein Design: Aligning and Conditioning Strategies
AI总结：
本文综述了引导生成模型进行蛋白质设计的策略，涵盖修改模型参数和保持参数不变的方法，旨在提升生成蛋白质的特性。
摘要：
生成式人工智能模型通过学习数据的概率分布来生成新的样本，这些样本能够捕捉训练集中的显著特性。蛋白质特别适合此类方法，因为它们有丰富的数据和多样的表示形式，从序列到结构和功能。这种多样性推动了生成模型在蛋白质设计中的快速发展，使生成具有功能的蛋白质和酶取得前所未有的成功。然而，由于这些模型模仿其训练分布，它们倾向于采样最可能的模式，而低概率区域，往往编码有价值的特性，仍被低估。为了解决这一挑战，近期工作提出了引导生成模型向用户指定属性的策略。在本文综述中，我们调查并分类这些策略，区分修改模型参数的方法，如强化学习或监督微调，以及保持模型参数不变的方法，包括条件生成、检索增强策略、贝叶斯引导和定制采样方法。这些发展开始使生成模型能够引导到具有所需特性的蛋白质。
英文摘要：
Generative artificial intelligence models learn probability distributions from data and produce novel samples that capture the salient properties of their training sets. Proteins are particularly attractive for such approaches given their abundant data and the versatility of their representations, ranging from sequences to structures and functions. This versatility has motivated the rapid development of generative models for protein design, enabling the generation of functional proteins and enzymes with unprecedented success. However, because these models mirror their training distribution, they tend to sample from its most probable modes, while low-probability regions, often encoding valuable properties, remain underexplored. To address this challenge, recent work has proposed strategies for steering generative models toward user-specified properties. In this review, we survey and categorize these strategies, distinguishing approaches that modify model parameters, such as reinforcement learning or supervised fine-tuning, from those that keep the model's parameters fixed, including conditional generation, retrieval-augmented strategies, Bayesian guidance, and tailored sampling methods. Together, these developments are beginning to enable the steering of generative models toward proteins with desired properties.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
