  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.
