Precision at Every Scale: Efficiency in AI-Driven De Novo Antibody Design

Cha, H.; Cho, K.; Gu, J.; Gwak, D.; Ham, S. W.; Hong, M.; Kwon, S.; Lee, C.; Lee, D. K.; Lee, D.; Lim, J.; Noh, J.; Oh, S.; Park, E.; Park, S.; Park, T.; Ryu, E.; Ryu, S.; Sa, D. H.; Seok, C.; Song, M. Y.; Won, J.; Woo, H.; Yang, J.

doi:10.1101/2025.11.21.689414

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bioRxiv · 10.1101/2025.11.21.689414

Precision at Every Scale: Efficiency in AI-Driven De Novo Antibody Design

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Abstract

The precise de novo design of antibodies remains a therapeutic challenge. The AI platform, GaluxDesign, was evaluated in a high-efficiency Precision-Scale Workflow by synthesizing and testing only 50 full-length IgG candidates per epitope across eight distinct epitopes from six therapeutic targets. This campaign yielded a 10.5% binder rate (estimated EC50 < 100 nM), identifying target-specific binders for seven of eight epitopes, with multiple candidates exhibiting sub-nanomolar to single-digit nanomolar dissociation constants (Kd). We further assessed the same workflow on nine shared benchmark targets selected for external comparison, where GaluxDesign identified target-specific binders for eight of nine targets, demonstrating strong target-level performance relative to previously reported de novo antibody design approaches. Together, these results establish a high-efficiency, precision-scale workflow for generating novel, high-affinity therapeutic antibodies.

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Cha, H., Cho, K., Gu, J., Gwak, D., Ham, S. W., Hong, M., Kwon, S., Lee, C., Lee, D. K., Lee, D., Lim, J., Noh, J., Oh, S., Park, E., Park, S., Park, T., Ryu, E., Ryu, S., Sa, D. H., Seok, C., Song, M. Y., Won, J., Woo, H., Yang, J.. 2025-11-22. Precision at Every Scale: Efficiency in AI-Driven De Novo Antibody Design. https://doi.org/10.1101/2025.11.21.689414

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