Protein Representation Learning

Protein representation learning aims to create computational models that effectively capture the complex relationships between a protein's amino acid sequence, 3D structure, and function. Current research heavily utilizes graph neural networks and transformer-based language models, often incorporating knowledge graphs and integrating both sequence and structural information to generate robust representations. These advancements are significantly impacting various fields, including drug discovery and protein engineering, by enabling more accurate predictions of protein properties and facilitating the design of novel proteins. The development of comprehensive benchmark datasets and efficient pretraining strategies are key focuses in ongoing efforts to improve the accuracy and generalizability of these models.