Generative Diffusion Models for Antibody Design, Docking, and Optimization

Abstract In recent years, optimizing antibody binding affinity for biomedical applications has become increasingly important. However, traditional wet-experiment-based approaches are time-consuming and inefficient. To address this issue, we propose a diffusion model-based antibody optimization pipeline to improve binding affinity. Our approach involves two key models: AbDesign for designing antibody sequences and structures, and AbDock, a paratope-epitope docking model, used for screening designed CDRs. On an independent test set, our AbDesign demonstrates the exceptional performance of an RMSD of 2.56Å in structure design and an amino acid recovery of 36.47% in sequence design. In a paratope-epitope docking test set, our AbDock achieves a state-of-the-art performance of DockQ 0.44, irms 2.71Å, fnat 0.40, and Lrms 6.29Å. The effectiveness of the optimization pipeline is further experimentally validated by optimizing a flaviviruse antibody 1G5.3, resulting in a broad-spectrum antibody that demonstrates improved binding to 6 out of the nine tested flaviviruses. This research offers a general-purpose methodology to enhance antibody functionality without training on data from specific antigens.