Antibody Design

Antibody design aims to create novel antibodies with desired properties, such as high binding affinity to a specific antigen, using computational methods to accelerate and optimize the process. Current research heavily utilizes machine learning, particularly deep learning models like diffusion models, variational autoencoders, and transformer-based architectures, often combined with physics-based energy functions and reinforcement learning algorithms to guide the design process. This field is crucial for advancing therapeutic antibody development, enabling the creation of more effective and targeted treatments for various diseases, and accelerating vaccine design.