🤯 Virtual Labs Are Here, and They’re Doing Real Science

When I heard prof. James Zou from Stanford present this work during his ISMB/ECCB 2025 keynote in Liverpool several days ago, I was stunned. Not because it was speculative—but because it was already working. A team of AI agents, orchestrated through what they call the Virtual Lab, had successfully designed a nanobody vaccine candidate for a recent SARS-CoV-2 variant, and it was experimentally validated.

Now published in Nature, this work reimagines how research can be done—with a hybrid team of LLM-based scientists and minimal human supervision.

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🧠 What is the Virtual Lab?

The Virtual Lab is a system where a human researcher provides high-level guidance, and a team of large language model (LLM) agents—each with a defined scientific role—carry out the actual research. It includes:

• A central AI Principal Investigator (PI) who assigns tasks
• Domain-specific agents (e.g., immunologist, ML expert, protein modeler)
• A Scientific Critic agent (the famous Reviewer 2) tasked with evaluating and poking holes in others’ proposals
• Structured team meetings and 1:1 sessions, where agents debate, design, and iterate—captured as full transcripts (And they can be parallelized!🤯)

The PI agent dynamically assembles the research team and drives the workflow autonomously. Meetings run in parallel. Results are aggregated. And progress is fast.

🔬 Case Study: Nanobody Design for COVID-19

To demonstrate its capability, the Virtual Lab was tasked with designing binders for the KP.3 SARS-CoV-2 variant. Rather than conventional antibodies, the agents chose to focus on nanobodies—smaller, more stable antibody fragments that are easier to model computationally.

Over several rounds of team and individual meetings, agents used reasoning, structural modeling (via tools like AlphaFold), and even requested new tool access mid-process. The final nanobody designs were passed to wet-lab collaborators, who:

✅ Synthesized and validated the structures
✅ Confirmed high binding affinity to KP.3 spike protein
✅ Showed minimal off-target effects
✅ Demonstrated cross-reactivity with earlier strains like Wuhan

The AI-generated solution was novel, biologically plausible, and effective.

🚀 What This Means

This work signals a shift from LLMs as static assistants to agentic collaborators that:

• Form interdisciplinary research teams
• Debate and critique hypotheses
• Drive scientific projects with limited human intervention
• Produce new, experimentally testable insights

It’s not just about automating tasks. It’s about scaling scientific reasoning.

🔄 From Proof of Concept to Platform

The architecture is modular and extensible. The team has already begun adapting the Virtual Lab to:

• Re-analyze existing biomedical datasets
• Reinterpret findings from previous publications
• Explore new domains beyond COVID-19, with agents acting as self-updating analysts

And the feedback loop is real: wet-lab results feed back into the Virtual Lab for iterative refinement.

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🧭 Final Thoughts

This work left a strong impression on me. As someone focused on AI for drug discovery and biomedicine, it was a clear signal: We’re entering an era where AI agents may collaborate in real discovery, not just accelerate it.

And the Virtual Lab might be the blueprint for how.