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.

本研究解决了设计具有理想特性的蛋白质这一长期挑战。论文提出了采用扩散模型的方法，这些模型具有坚实的数学基础和出色的生成能力，特别适用于蛋白质设计等领域。研究表明，这些模型在自动化蛋白质设计与工程方面具有重要的前景。

从热力学到蛋白质设计：用于生物分子生成的扩散模型朝向自主蛋白质工程