Antibody Sequence

Antibody sequence analysis focuses on understanding the relationship between antibody amino acid sequences and their properties, primarily to accelerate therapeutic antibody design and discovery. Current research heavily utilizes deep learning models, including various transformer-based architectures (like BERT and T5 adaptations) and graph neural networks, often incorporating pre-trained models and domain generalization techniques to improve prediction accuracy and robustness across diverse datasets. These advancements are significantly impacting drug development by enabling more efficient prediction of antibody properties like immunogenicity and binding affinity, leading to faster and more targeted therapeutic antibody design.