标题： 从热力学到蛋白质设计：面向自主蛋白质工程的生物分子生成扩散模型 显示英文标题

标题： From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering

摘要： 具有所需特性的蛋白质设计在过去的几十年中一直是一个重大挑战。 生成式人工智能是一种有前景的方法，并在各种蛋白质生成任务中取得了巨大成功。 值得注意的是，扩散模型因其强大的数学基础和出色的生成能力而在某些应用（如蛋白质设计）中展现出独特的优势。 在本综述中，我们首先给出扩散模型的定义和特征，然后重点介绍两种策略：去噪扩散概率模型和基于评分的生成模型，其中DDPM是SGM的离散形式。 此外，我们讨论了它们在蛋白质设计、肽生成、药物发现和蛋白质-配体相互作用中的应用。 最后，我们概述了扩散模型的未来发展方向，以推动自主蛋白质设计和工程。 E(3)群包括三维空间中的所有旋转、反射和平移。 在E(3)群上的等变性可以尽可能保持每个氨基酸框架的物理稳定性，我们反思了如何使扩散模型在蛋白质生成中具备E(3)等变性。 显示英文摘要

摘要： Protein design with desirable properties has been a significant challenge for many decades. Generative artificial intelligence is a promising approach and has achieved great success in various protein generation tasks. Notably, diffusion models stand out for their robust mathematical foundations and impressive generative capabilities, offering unique advantages in certain applications such as protein design. In this review, we first give the definition and characteristics of diffusion models and then focus on two strategies: Denoising Diffusion Probabilistic Models and Score-based Generative Models, where DDPM is the discrete form of SGM. Furthermore, we discuss their applications in protein design, peptide generation, drug discovery, and protein-ligand interaction. Finally, we outline the future perspectives of diffusion models to advance autonomous protein design and engineering. The E(3) group consists of all rotations, reflections, and translations in three-dimensions. The equivariance on the E(3) group can keep the physical stability of the frame of each amino acid as much as possible, and we reflect on how to keep the diffusion model E(3) equivariant for protein generation.