Protein Sequence Design

Protein sequence design aims to create novel protein sequences with desired functionalities, a crucial task in fields like drug discovery and biotechnology. Current research heavily utilizes machine learning, employing generative models like diffusion models and language models (including those specifically trained on protein sequences), often coupled with reinforcement learning or Bayesian optimization algorithms, to efficiently explore the vast sequence space and design proteins with improved stability and specific functions. These advancements are significantly accelerating the protein engineering process, enabling the creation of proteins with tailored properties for various applications, and reducing reliance on time-consuming and expensive experimental methods.