从噪声中筛选：扩散概率模型及其在生物分子中的应用综述
Sifting through the Noise: A Survey of Diffusion Probabilistic Models and Their Applications to Biomolecules
AI总结：
本文综述了扩散概率模型的理论及其在生物分子结构与序列预测和设计中的应用，介绍了常见应用模式及在生成与预测任务中的显著成果，帮助研究人员全面理解其关键作用。
摘要：
扩散概率模型自问世以来，已应用于多个备受瞩目的领域。特别是，在利用扩散模型预测和设计生物分子结构与序列方面掀起了一股研究热潮。其日益普及使得相关领域的研究人员必须理解这些模型。本文旨在概述这些模型背后的理论及当前的研究现状。我们首先介绍扩散模型，并讨论将其应用于生物分子时常用的模式。随后，我们展示这些模型在生成和预测任务中应用所取得的显著成果。本综述旨在为读者提供对扩散模型日益关键作用的全面理解。
英文摘要：
Diffusion probabilistic models have made their way into a number of high-profile applications since their inception. In particular, there has been a wave of research into using diffusion models in the prediction and design of biomolecular structures and sequences. Their growing ubiquity makes it imperative for researchers in these fields to understand them. This paper serves as a general overview for the theory behind these models and the current state of research. We first introduce diffusion models and discuss common motifs used when applying them to biomolecules. We then present the significant outcomes achieved through the application of these models in generative and predictive tasks. This survey aims to provide readers with a comprehensive understanding of the increasingly critical role of diffusion models.
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。
