Structure Prediction

Structure prediction aims to computationally determine the three-dimensional arrangement of molecules, particularly proteins and RNA, from their constituent sequences. Current research heavily utilizes deep learning, employing transformer-based models, graph neural networks, and diffusion models to capture complex interactions and predict structures, including distance matrices as intermediate representations. These advancements significantly improve accuracy in various applications, such as drug discovery (protein-ligand interactions), materials science (crystal structure prediction), and understanding biological processes (protein-protein interactions and RNA folding). The resulting structural information is crucial for accelerating scientific discovery and technological innovation across multiple fields.