The generative rearchitecture of antibody engineering shifts empirical discovery into intentional design

Generative AI is reshaping antibody engineering by transitioning from empirical screening toward more specification-driven design, potentially expanding the accessible target landscape. While front-loading developability criteria helps streamline discovery timelines, data constraints still introduce risks of in silico misprioritization. This Perspective discusses how integrating computational inference within disciplined engineering frameworks can complement traditional sampling methods, highlighting that coupling AI-driven predictions with rigorous empirical validation remains essential for achieving therapeutic intent.

This work was supported by grants from the National Research Foundation of Korea (NRF) funded by the Korea government (MSIT) (RS-2024-00338524 and RS-2024-00338397) and the KRIBB Research Initiative Program (KGM5192632 and KGM1322612) in the Republic of Korea. All figures were created with BioRender.com. Bionanotechnology Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, Republic of Korea Department of Nanobiotechnology, KRIBB School of Biotechnology, Korea National University of Science and Technology (UST), Daejeon, Republic of Korea Genomic Medicine Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, Republic of Korea Department of Biomolecular Science, KRIBB School of Bioscience, Korea National University of Science and Technology (UST), Daejeon, Republic of Korea Correspondence to Kyunghee Noh or Wonbeak Yoo.

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The generative rearchitecture of antibody engineering shifts empirical discovery into intentional design. npj Digit. Med. (2026). https://doi.org/10.1038/s41746-026-03266-1 DOI: https://doi.org/10.1038/s41746-026-03266-1