Protein sequence design with deep generative models
arXiv:2104.04457v1161 citations
Originality Synthesis-oriented
AI Analysis
This is a review paper, so it is incremental and summarizes existing work for researchers in protein engineering and machine learning.
The paper reviews recent applications of machine learning, particularly deep generative models, for designing protein sequences to optimize properties, but does not report specific results or numbers.
Protein engineering seeks to identify protein sequences with optimized properties. When guided by machine learning, protein sequence generation methods can draw on prior knowledge and experimental efforts to improve this process. In this review, we highlight recent applications of machine learning to generate protein sequences, focusing on the emerging field of deep generative methods.