Antibody Structure

Antibody structure research focuses on understanding the relationship between antibody sequence, 3D structure, and binding affinity to antigens, aiming to improve antibody design for therapeutic and research applications. Current research heavily utilizes deep learning, employing graph neural networks and language models to predict antibody-antigen interactions, design novel antibody sequences, and refine structural models, often incorporating physics-based force fields for enhanced accuracy. These advancements are significantly impacting drug discovery by enabling more efficient and precise antibody engineering, reducing reliance on costly experimental screening. Improved prediction methods also enhance our understanding of the immune system's complex interactions.