Therapeutic Antibody

Therapeutic antibody development aims to create highly specific and effective antibody-based drugs, often focusing on improving binding affinity and reducing immunogenicity. Current research heavily utilizes machine learning, employing various architectures like deep learning models (including normalizing flows and transformers), and reinforcement learning algorithms to optimize antibody design, predict binding affinity, and assess properties like humanness and solubility. These computational approaches aim to accelerate and improve the efficiency of antibody discovery and development, reducing reliance on costly and time-consuming experimental methods, ultimately leading to better and more readily available therapeutics.