Virtual Lab Description Card

May 19, 2025

Executive Summary: A multi-agent AI system where specialized AI researchers collaborate through structured meetings to conduct sophisticated interdisciplinary science research, demonstrated through successful nanobody design with experimental validation
Key Goal of the System: Enable human AI collaboration to perform complex, interdisciplinary scientific research that translates to validated real-world results across multiple scientific domains
System Architecture: Multi-agent architecture with specialized scientist agents, operating through structured team and individual meetings
Base Model(s): GPT-4o (with flexibility to use other LLMs)
General Tools Used: Natural language processing for meeting orchestration, parallel processing capabilities
Domain Specific Tools: ESM (protein language model), AlphaFold-Multimer (protein complex prediction), Rosetta (binding energy calculation), LocalColabFold

Number of Agents: Variable (3-5 scientist agents + PI agent + Scientific Critic agent)
Agent Types: Principal Investigator, Immunologist, Computational Biologist, Machine Learning Specialist, Scientific Critic (customizable based on project needs)
Agent Hierarchy: Principal Investigator leads team meetings and makes strategic decisions; Scientific Critic provides oversight and quality control; scientist agents operate as collaborative peers
Communication Protocol: Structured meeting protocols with defined speaking order, synthesis phases, and feedback loops; agents build on each other's contributions
Memory Architecture: Meeting summaries and context preservation across sessions; agents can reference previous meeting outcomes and decisions

Interaction Model: Human researcher provides high-level guidance through meeting agendas, agenda questions, and agenda rules; minimal input required (1.3% of total content)
Expertise Required: Domain knowledge needed to set appropriate research directions and validate final outputs; no technical AI expertise required
Feedback Mechanisms: Agenda setting, meeting rule specification, and review of final recommendations; human can iterate on meetings if outputs are unsatisfactory
Output Formats: Meeting summaries, research recommendations, complete code implementations, experimental protocols, strategic decisions with justifications

Response Time: 5-10 minutes per meeting session; entire research project completed in days versus months
Computational Requirements: Standard LLM inference requirements; parallel meeting capability increases resource needs
Scaling Properties: Performance improves with parallel meetings and merging; flexible temperature settings (0.8 for creativity, 0.2 for consistency)
Benchmark Results: Systems reasoning performance was not benchmarked using available datasets.
Real-world Validation: Complete wet-lab validation of nanobody designs with functional binding assays across multiple SARS-CoV-2 variants. 92 nanobodies designed with >90% experimental expression success rate; 2 candidates showed novel binding profiles

Literature Coverage: Relies on LLM training data; may miss most recent publications or paywalled content
Validation Status: Full experimental validation including protein expression, purification, and binding assays. Laboratory experiments are not tight to system.
Target Identification Accuracy: Successfully designed functional nanobodies with 90%+ expression rate and novel binding properties
Hypothesis Novelty Rate: Generated novel computational pipeline combining ESM, AlphaFold-Multimer, and Rosetta with custom scoring function
Domain Expertise Breadth: Demonstrated in nanobody design; architecture adaptable to other interdisciplinary biological research areas

Known Limitations: LLM knowledge cutoffs limit awareness of latest tools; requires prompt engineering for optimal performance; can give vague answers without specific guidance
Safety Mechanisms: Scientific Critic agent provides error checking and quality control; human oversight required for experimental validation
Edge Cases: Performance may degrade when agents are not given specific enough directions or when consensus building fails
Ethical Considerations: Requires attribution frameworks for AI contributions to scientific discovery; maintains human responsibility for final research decisions and experimental validation