Protein Language Model

Protein language models (PLMs) leverage the power of natural language processing techniques to analyze and generate protein sequences, aiming to improve our understanding of protein structure, function, and evolution. Current research focuses on enhancing PLMs through techniques like instruction tuning, incorporating structural information (e.g., using graph neural networks or contact maps), and employing various training strategies such as contrastive learning and reinforcement learning to optimize for specific properties or tasks. These advancements are significantly impacting various fields, including drug discovery, protein engineering, and the broader understanding of biological processes by enabling more accurate predictions and efficient design of proteins with desired characteristics.