Antibody Structure Prediction

Predicting the 3D structure of antibodies from their amino acid sequence is crucial for accelerating antibody drug development. Current research focuses on improving the accuracy and efficiency of prediction models, particularly for the highly variable regions (CDRs) that determine antigen binding, using techniques like deep learning and transformer-based architectures, often incorporating pre-trained language models for antibody sequences. These advancements enable faster and more cost-effective design of novel antibodies with desired properties, impacting therapeutic antibody development and our understanding of the immune system.