Recent advancements in specialized large-scale architectures for training image and language have profoundly impacted the field of computer vision and natural language processing (NLP). Language models, such as the recent ChatGPT and GPT4 have demonstrated exceptional capabilities in processing, translating, and generating human languages. These breakthroughs have also been reflected in protein research, leading to the rapid development of numerous new methods in a short time, with unprecedented performance. Language models, in particular, have seen widespread use in protein research, as they have been utilized to embed proteins, generate novel ones, and predict tertiary structures. In this book chapter, we provide an overview of the use of protein generative models, reviewing 1) language models for the design of novel artificial proteins, 2) works that use non-Transformer architectures, and 3) applications in directed evolution approaches.

翻译：近期，用于图像和语言训练的专业大规模架构的进步已深刻影响了计算机视觉和自然语言处理领域。语言模型，例如最近的ChatGPT和GPT4，在处理、翻译和生成人类语言方面展现出卓越能力。这些突破同样反映在蛋白质研究中，在短时间内催生了大量性能空前的新方法。特别是语言模型已在蛋白质研究中得到广泛应用，被用于蛋白质嵌入、新蛋白质生成及三级结构预测。在本章中，我们概述了蛋白质生成模型的应用，回顾了：1）用于设计新型人工蛋白质的语言模型，2）采用非Transformer架构的研究，以及3）在定向进化方法中的应用。