Protein Model

Protein modeling aims to predict and understand the three-dimensional structures and interactions of proteins, crucial for drug discovery and understanding biological processes. Current research heavily utilizes deep learning, employing architectures like graph neural networks, transformers, and autoencoders to analyze protein sequences and structures, often incorporating multi-modal data (sequence, structure, function) for improved accuracy. These advancements enable improved prediction of protein-protein interactions, protein design (including antibodies), and compound-protein interactions, ultimately accelerating progress in fields like drug development and biomaterial engineering.