Multimodal reasoning for broadly neutralizing antibody discovery from label-free human B cell repertoires across virus families
Read the original on arXiv AI →The paper introduces ImmuneAgent, a closed‑loop AI system that combines multimodal reasoning, continual meta‑learning, and wet‑lab feedback to identify broadly neutralizing antibodies (bnAbs) from human B cell repertoires. Applied to vaccinated or infected cohorts, ImmuneAgent achieved a 55% neutralization discovery rate and an 11% bnAb yield, outperforming existing sequence‑based predictors and co‑folding models. Five discovered antibodies provided full in vivo protection against lethal influenza, and the system uncovered conserved bnAb reservoirs and structural signatures that enabled cross‑viral antibody discovery without antigen‑specific sorting.
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