Enhancing Antibody Antigen Interaction Efficiency Through AI Based Computational Approaches

Improving antibody-antigen interactions is critical for therapeutic antibody development. The artificial intelligence-powered method improves binding affinity and specificity by leveraging deep learning and structural bioinformatics. Despite these advances, significant hurdles remain, including the difficulty of simulating dynamic interactions under physiological settings, the scarcity of data for uncommon antigens, and the computational demands of structural predictions. To address these issues, this paper combines Variational Auto Encoders (VAE), transformers, and graph-based models into a single pipeline, resulting in enhanced structure prediction and binding affinity estimation on benchmark datasets. Specifically, transformer-based models such as Alphafold and RoseTTAFold are employed to predict antibody structures, focusing particularly on variable regions like the CDR-H3 loop, while Graph Neural Networks (GNN) and Graph Transformer Networks (GTN) are used to model complex binding interfaces. The proposed method achieves the RMSE of 0.15 and MAE of 0.10, indicating the low error rate. These findings demonstrate the system potential to produce structurally stable, high-affinity antibodies while also greatly speeding up rational therapeutic antibody design