The Virtual Lab: AI Agents Design New SARS-CoV-2 Nanobodies with Experimental Validation

Checked 30 Sept 2026. Non-commercial or no-derivatives license: full text kept internally; only metadata and abstract are indexed and used.

Abstract Science frequently benefits from teams of interdisciplinary researchers. However, most scientists don’t have access to experts from multiple fields. Fortunately, large language models (LLMs) have recently shown an impressive ability to aid researchers across diverse domains by answering scientific questions. 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 agents with different scientific backgrounds (e.g., a chemist agent, a computer scientist agent, a critic agent), with a human researcher providing high-level feedback. We design the Virtual Lab to conduct scientific research through a series of team meetings, where all the agents discuss a scientific agenda, and individual meetings, where an agent accomplishes a specific task. We demonstrate the power of the Virtual Lab by applying it to design nanobody binders to recent variants of SARS-CoV-2, which is a challenging, open-ended research problem that requires reasoning across diverse fields from biology to computer science. The Virtual Lab creates a novel computational nanobody design pipeline that incorporates ESM, AlphaFold-Multimer, and Rosetta and designs 92 new nanobodies. Experimental validation of those designs 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 variants of SARS-CoV-2 while maintaining strong binding to the ancestral viral spike protein, suggesting exciting candidates for further investigation. This demonstrates the ability of the Virtual Lab to rapidly make impactful, real-world scientific discovery.

• Independent teams have converged on multi-agent, tool-using LLM architectures as the core design pattern for AI-for-science systems: Coscientist (Boiko et al. 2023) pairs GPT-4 with search, code execution and lab automation, Co-Scientist (Gottweis et al. 2026) is a multi-agent system built on Gemini, and the Virtual Lab (Swanson et al. 2024) has an LLM principal investigator guiding specialist agents. Qi et al. 2026's survey argues this convergence is functional, since multi-agent redundancy offers error-checking that can mitigate hallucination and bias. Trace →
• Agent-driven pipelines have already produced experimentally validated wet-lab results in more than one domain: Boiko et al. 2023's Coscientist successfully optimized palladium-catalysed cross-coupling reactions, and Swanson et al. 2024's Virtual Lab designed 92 nanobodies, two of which showed improved binding to recent SARS-CoV-2 variants while retaining binding to the ancestral spike. Trace →
• AI agents have already produced experimentally validated results in multiple domains: Boiko et al. 2023's Coscientist optimized palladium-catalysed cross-couplings, Ghareeb et al. 2026's Robin identified and confirmed ripasudil for dry age-related macular degeneration in vitro, and Swanson et al. 2024's Virtual Lab produced nanobodies with validated binding across SARS-CoV-2 variants. Trace →
• Role-specialized multi-agent teams are a convergent design pattern across systems such as Co-Scientist and the Virtual Lab, and Qi et al. 2026's survey articulates the rationale: planners, executors, validators and critics provide structured redundancy and error-checking that may mitigate hallucination and bias. Trace →

• AI for science and agentic research