The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodies

Kyle Swanson, Wesley Wu, Nash L. Bulaong, John E. Pak () and James Zou ()
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Kyle Swanson: Stanford University
Wesley Wu: Chan Zuckerberg Biohub
Nash L. Bulaong: Chan Zuckerberg Biohub
John E. Pak: Chan Zuckerberg Biohub
James Zou: Stanford University

Nature, 2025, vol. 646, issue 8085, 716-723

Abstract: Abstract Science frequently benefits from teams of interdisciplinary researchers1–3, but many scientists do not have easy access to experts from multiple fields4,5. Although large language models (LLMs) have shown an impressive ability to aid researchers across diverse domains, their uses have been largely limited to answering specific scientific questions rather than performing open-ended research6–11. Here we expand the capabilities of LLMs for science by introducing the Virtual Lab, an artificial intelligence (AI)–human research collaboration to perform sophisticated, interdisciplinary science research. The Virtual Lab consists of an LLM Principal Investigator agent guiding a team of LLM scientist agents through a series of research meetings, with a human researcher providing high-level feedback. We applied the Virtual Lab to design nanobody binders to recent variants of SARS-CoV-2. The Virtual Lab created a novel computational nanobody design pipeline that incorporates the protein language model ESM, the protein folding model AlphaFold-Multimer and the computational biology software Rosetta and designed 92 new nanobodies. Experimental validation reveals a range of functional nanobodies with promising binding profiles across SARS-CoV-2 variants. In particular, two new nanobodies exhibit improved binding to the recent JN.1 or KP.3 variants12,13 while maintaining strong binding to the ancestral viral spike protein, suggesting that these are suitable candidates for further investigation. This work demonstrates how the Virtual Lab can rapidly make an impactful, real-world scientific discovery.

Date: 2025
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DOI: 10.1038/s41586-025-09442-9

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