Project Theme
Virtual AI Co-scientist
Project Summary
Scientific discovery often requires brainstorming multiple hypotheses, evaluating their merits, and prioritizing research directions. This project creates an advanced multi-agent AI system that acts as a virtual research collaborator, capable of generating novel scientific hypotheses, engaging in simulated debate and critique, gathering supporting evidence from literature and databases, and ranking hypotheses by feasibility and potential impact. By automating the hypothesis generation and evaluation process, we can accelerate scientific discovery and explore novel research directions.
Goals and Objectives
- Generate scientific hypotheses from user-defined research questions and goals
- Implement multi-agent debate with specialized AI agents (Generator, Critic, Evaluator)
- Gather supporting evidence from literature and BRC databases
- Rank and prioritize hypotheses based on novelty, feasibility, and impact
Approach
Methods and AI/ML Approaches:
- Multi-agent LLM framework (AutoGen, CrewAI, LangGraph, or custom)
- Specialized agents with distinct roles and personas
- Chain-of-thought reasoning and debate protocols
- Literature search and evidence retrieval (RAG)
- Integration with BRC data for hypothesis validation
- Consensus mechanisms and voting systems
- Experimental design suggestion algorithms
Implementation Steps:
- Design multi-agent architecture and agent roles
- Implement agent communication protocols
- Create prompts for hypothesis generation
- Build critique and debate mechanisms
- Integrate evidence gathering from literature and databases
- Develop ranking and scoring algorithms
- Test with infectious disease research questions
- Create interface for user interaction and feedback
Data and Resources Required
| Resource Type | Source / Link | Description / Purpose |
|---|---|---|
| Data | PubMed, PubMed Central | Literature for evidence gathering |
| Data | BV-BRC databases | Pathogen data for validation |
| LLMs / AI Models | GPT-4, Claude 3, Gemini | Multi-agent reasoning |
| Tools / Services | AutoGen, CrewAI, or LangGraph | Multi-agent frameworks |
| Tools / Services | LangChain for RAG | Evidence retrieval |
| Data | ArXiv, bioRxiv preprints | Cutting-edge research |
| Compute / Storage | High token limits for LLM calls | Extended reasoning chains |
Expected Outcomes / Deliverables
- Functional multi-agent prototype with hypothesis generation and debate
- Generated hypotheses with supporting evidence for test questions
- Evaluation framework for hypothesis quality, novelty, and feasibility
- Case studies on infectious disease research questions
- User interface for interacting with the AI co-scientist
- Public repository with code and documentation
- Demo video showing multi-agent debate in action
Potential Impact and Next Steps
Impact on:
- Infectious disease research: Accelerates hypothesis generation and research planning
- AI/ML automation: Advances multi-agent reasoning and collaboration
- Public health preparedness: Enables rapid hypothesis generation for emerging threats
Next Steps After Codeathon:
- Add experimental design and protocol suggestions
- Integrate with grant writing assistance
- Implement learning from user feedback
- Create domain-specific agents for different pathogen classes
- Deploy as a research planning tool for BRC users
Technical Support Needed
- High LLM API rate limits
- Access to recent literature databases
- BRC database schemas and documentation
- Mentor support from research scientists
- Test research questions from BRC community
Team Information
Teams will be formed during the Codeathon. Ideal team composition:
- AI/ML Researcher: Multi-agent system design
- LLM Engineer: Prompt engineering and agent development
- Research Scientist: Hypothesis validation and evaluation
- Software Engineer: System architecture and integration
- UX Designer: Interface for human-AI collaboration