Building an Autonomous Wet-Lab Protocol Planner with Salesforce CodeGen for Agentic Experiment Design and Safety Optimization

Autonomous Wet-Lab Protocol Planner and Validator Using Salesforce CodeGen

This tutorial outlines the development of an intelligent agentic system for automating experimental design, safety validation, and resource optimization in wet-lab environments. The system integrates Salesforce CodeGen-350M-mono, a lightweight language model, with modular Python components to parse protocols, verify reagents, schedule experiments, and identify safety risks. The solution closes the loop between perception, planning, validation, and refinement, enabling self-contained, data-secure lab operations.

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Key Components and Functionality

1. ProtocolParser: Extracting Structured Experimental Data

• Purpose: Parses unstructured protocol text into structured data (steps, durations, temperatures, safety flags).
• Implementation:
  • Uses regex to identify step numbers, durations (e.g., “overnight” → 720 minutes), temperatures (e.g., “4°C” → “4C”), and safety markers (e.g., “BSL-2” → “BSL-2/3”).
  • Contextual analysis ensures accurate extraction of details from adjacent lines.
• Impact: Enables precise scheduling and safety validation by transforming free-form text into actionable data.

2. InventoryManager: Reagent Availability and Expiry Checks

• Purpose: Validates reagent stock levels, expiry dates, and availability using fuzzy matching.
• Implementation:
  • Loads reagent data from CSV files and checks against protocol text.
  • Flags issues like expired reagents, low stock (<10 units), or missing items.
  • Example: check_availability(["capture antibody"]) would return warnings if the reagent is expired or out of stock.
• Impact: Prevents experimental delays due to missing or expired materials.

3. SchedulePlanner: Optimizing Experimental Timelines

• Purpose: Generates schedules, identifies parallelizable steps, and reduces total experiment time.
• Implementation:
  • Maps steps to days and times (e.g., starting at 09:00).
  • Detects steps with duration_min > 60 for parallel execution.
  • Example: Steps at “37°C” can be grouped to save time.
• Impact: Reduces total time by up to 30% through parallelization (e.g., saving 15 minutes in the sample ELISA protocol).

4. SafetyValidator: Identifying and Mitigating Risks

• Purpose: Enforces lab safety standards by detecting hazardous conditions.
• Implementation:
  • Checks for unsafe pH levels (5.0–11.0), temperature ranges, and safety flags (e.g., “HAZARD” → PPE required).
  • Example: If a step mentions “corrosive” chemicals, it triggers a “Full PPE + chemical hood” alert.
• Impact: Ensures compliance with biosafety protocols and reduces accident risks.

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AI Integration: CodeGen for Optimization and Reasoning

LLM-Based Optimization

• Purpose: Uses Salesforce CodeGen-350M-mono to suggest refinements (e.g., batch similar steps, pre-warm instruments).
• Implementation:
  • The llm_call() function generates natural language suggestions based on protocol metadata.
  • Example: For a 12-step ELISA protocol, the AI might suggest, “Batch temperature steps to reduce instrument switching.”
• Impact: Enhances efficiency by proposing human-like optimizations.

Agent Loop: End-to-End Workflow

• Purpose: Integrates all components into a single workflow.
• Implementation:
  • Parses protocols, validates inventory, schedules steps, and checks safety.
  • Outputs include a Markdown checklist, Gantt-compatible CSV, and AI suggestions.
• Example Output:
  • Time Saved: 15 minutes via parallelization.
  • Safety Alerts: “Step 6: Work in dark/amber tubes” due to light-sensitive reagents.

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Working Example: ELISA Protocol Test Case

# Sample ELISA Protocol and Inventory
SAMPLE_PROTOCOL = """ELISA Protocol for Cytokine Detection
1. Coating (Day 1, 4°C overnight)
- Dilute capture antibody to 2 μg/mL in coating buffer (pH 9.6)
- Add 100 μL per well to 96-well plate
- Incubate at 4°C overnight (12-16 hours)
- BSL-2 cabinet required
...
"""
SAMPLE_INVENTORY = """reagent,quantity,unit,expiry,lot
capture antibody,500,μg,2025-12-31,AB123
blocking buffer,500,mL,2025-11-30,BB456
...
"""

# Execute the agent loop
results = agent_loop(SAMPLE_PROTOCOL, SAMPLE_INVENTORY, start_time="09:00")
print(generate_checklist(results))
print(generate_gantt_csv(results['schedule'])[:400])

Output:

• Markdown Checklist: Lists reagents, safety alerts, and optimized steps.
• Gantt CSV: Schedules steps across days with start/end times.
• AI Suggestion: “Batch similar temperature steps together.”

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Recommendations and Best Practices

• Model Optimization:
  • Use float16 precision and automatic device mapping for faster inference on GPUs.
  • Set temperature=0.7 and top_p=0.9 in llm_call() for balanced creativity and accuracy.
• Inventory Management:
  • Regularly update CSV files with reagent expiry dates and quantities.
  • Use fuzzy matching (str.contains) to handle typos or alternative reagent names.
• Safety Checks:
  • Expand regex patterns in SafetyValidator to cover additional hazards (e.g., “radioactive”).
  • Integrate with lab equipment APIs for real-time instrument availability checks.
• Pitfalls to Avoid:
  • Over-reliance on regex for duration extraction may fail for ambiguous text (e.g., “overnight” vs. “12 hours”).
  • Ensure CodeGen is fine-tuned for scientific domains to avoid irrelevant optimizations.

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Conclusion

This system demonstrates how agentic AI can transform wet-lab workflows by automating protocol validation, resource planning, and safety checks. By leveraging Salesforce CodeGen and modular Python code, researchers can reduce manual errors, optimize time, and ensure compliance with lab standards. The provided code and test case offer a scalable foundation for deploying autonomous lab assistants.

For full code and tutorials, visit MarkTechPost’s GitHub page.