The de novo design of proteins refers to creating proteins with specific structures and functions that do not naturally exist. In recent years, the accumulation of high-quality protein structure and sequence data and technological advancements have paved the way for the successful application of generative artificial intelligence (AI) models in protein design. These models have surpassed traditional approaches that rely on fragments and bioinformatics. They have significantly enhanced the success rate of de novo protein design, and reduced experimental costs, leading to breakthroughs in the field. Among various generative AI models, diffusion models have yielded the most promising results in protein design. In the past two to three years, more than ten protein design models based on diffusion models have emerged. Among them, the representative model, RFDiffusion, has demonstrated success rates in 25 protein design tasks that far exceed those of traditional methods, and other AI-based approaches like RFjoint and hallucination. This review will systematically examine the application of diffusion models in generating protein backbones and sequences. We will explore the strengths and limitations of different models, summarize successful cases of protein design using diffusion models, and discuss future development directions.

本研究解决了当前蛋白质设计中的有效性和成本问题，通过介绍扩散模型在新蛋白质设计中的应用，显示其优于传统方法的成功率和成本效益。该研究的关键发现是RFDiffusion模型在25个蛋白质设计任务中的表现显著优于以往的设计方法，预示着未来蛋白质设计的巨大潜力。

原子之舞——基于扩散模型的新颖蛋白质设计