原子的舞蹈——基于扩散模型的从头蛋白质设计
The Dance of Atoms-De Novo Protein Design with Diffusion Model
AI总结：
本综述系统考察扩散模型在从头蛋白质设计中的应用，分析RFDiffusion等模型在骨架和序列生成中的优势与局限，总结成功案例并展望未来方向。
摘要：
蛋白质的从头设计是指创建自然界中不存在但具有特定结构和功能的蛋白质。近年来，高质量蛋白质结构和序列数据的积累以及技术进步，为生成式人工智能（AI）模型在蛋白质设计中的成功应用铺平了道路。这些模型已超越依赖片段和生物信息学的传统方法。它们显著提高了从头蛋白质设计的成功率，并降低了实验成本，从而在该领域取得了突破。在各种生成式AI模型中，扩散模型在蛋白质设计中产生了最有希望的结果。在过去两三年中，出现了十多种基于扩散模型的蛋白质设计模型。其中，代表性模型RFDiffusion在25项蛋白质设计任务中的成功率远超传统方法以及其他基于AI的方法，如RFjoint和hallucination。本综述将系统考察扩散模型在生成蛋白质骨架和序列中的应用。我们将探讨不同模型的优势和局限性，总结使用扩散模型进行蛋白质设计的成功案例，并讨论未来的发展方向。
英文摘要：
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.
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。
