Science frequently benefits from teams of interdisciplinary researchers1–3, but many scientists do not have easy access to experts from multiple fields4,5. While 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 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 apply the Virtual Lab to design nanobody binders to recent variants of SARS-CoV-2. The Virtual Lab creates a novel computational nanobody design pipeline that incorporates ESM, AlphaFold-Multimer, and Rosetta and designs 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 exciting candidates for further investigation. This demonstrates how the Virtual Lab can rapidly make an impactful, real-world scientific discovery.
