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ImmuneAgent, a closed-loop AI system, integrates multimodal reasoning with continual meta-learning and wet-lab feedback to address the challenges of discovering rare broadly neutralizing antibodies (bnAbs) from human B cell repertoires. The system achieved a ~55% neutralization antibody discovery rate and ~11% bnAb yield, significantly outperforming existing computational methods. Notably, five discovered antibodies conferred 100% in vivo protection against lethal influenza challenge, and the system identified conserved cellular and structural determinants of bnAb activity, generalizing to unseen antigens.
A single AI system discovers broadly neutralizing antibodies with clinical-stage therapeutic efficacy against influenza and generalizes to novel viruses like hMPV and HPV, identifying conserved bnAb reservoirs and structural signatures.
Discovering broadly neutralizing antibodies (bnAbs) from human natural immune repertoires remains a fundamental challenge in immunology, hindered by: the extreme rarity of bnAb, incomplete understanding of their cellular origins across pathogens, and the inability of existing computational tools to generalize across emerging viral threats. Here we present ImmuneAgent, a closed-loop AI system that integrates multimodal reasoning with continual meta-learning and wet-lab feedback to overcome these barriers. Applied to screen the natural BCR repertoires from vaccinated or infected cohorts, the system achieves a ~55% neutralization antibody discovery rate (60 of 110 cloned candidates) and a ~11% bnAb yield (12 of 110), substantially outperforming a state-of-the-art sequence-based neutralization predictor or cofolding models evaluated at the same cloning budget. Five ImmuneAgent-discovered antibodies conferred 100% in vivo protection against lethal influenza challenge, comparable to the clinical-stage therapeutic MEDI8852. The system recovered the cellular and structural determinants of bnAb activity and identified FCRL5+CD27+ atypical memory B cells as a conserved bnAb reservoir and hydrophobic interface enrichment as a cross-viral structural signature, which generalized to unseen antigens, discovering human metapneumovirus (hMPV) cross-neutralizing and human papillomavirus (HPV)-neutralizing antibodies without antigen-specific sorting. These results validate that ImmuneAgent is a generalizable framework for rapid therapeutic antibody discovery against emerging viral threats.