Protein Graph

Protein graph research focuses on representing proteins as graphs to leverage their structural and sequential information for various biological tasks. Current efforts concentrate on developing and improving graph neural networks (GNNs), often incorporating self-supervised learning and attention mechanisms, to analyze protein structures and predict properties like binding affinity or function. These advancements are significantly impacting drug discovery, particularly structure-based drug design, by enabling faster and more accurate prediction of drug-target interactions and accelerating the identification of potential drug candidates. The development of standardized benchmarks and datasets is also a key area of focus, facilitating more robust model evaluation and comparison.