Toxicity Structure Alert (Skill ID: 141)

Identify potential toxic structural alerts in drug molecules.

Features

• Scan molecular structures (SMILES/SMARTS)
• Identify known toxic structural alerts
• Assess potential toxicity risk levels
• Generate detailed reports

Supported Alert Structures

Dependencies

• Python 3.8+
• RDKit

Usage

python scripts/main.py --input <smiles_string> [--format json|text]

Parameters

• --input, -i: Input SMILES string (required)
• --format, -f: Output format, optional json or text (default: text)
• --detail, -d: Detail level, optional basic, standard, full (default: standard)

Examples

# Basic text output
python scripts/main.py -i "O=[N+]([O-])c1ccccc1"

# JSON format output
python scripts/main.py -i "O=C1OC1c1ccccc1" -f json

# Detailed report
python scripts/main.py -i "c1ccc2c(c1)ccc1c3ccccc3ccc21" -d full

Python API

from scripts.main import ToxicityAlertScanner

scanner = ToxicityAlertScanner()
result = scanner.scan("O=[N+]([O-])c1ccccc1")
print(result.alerts)

Output Format

JSON Output

{
  "input": "O=[N+]([O-])c1ccccc1",
  "mol_weight": 123.11,
  "alert_count": 1,
  "risk_score": 0.85,
  "risk_level": "HIGH",
  "alerts": [
    {
      "name": "Aromatic Nitro",
      "type": "mutagenic",
      "smarts": "[N+](=O)[O-]",
      "risk_level": "HIGH",
      "description": "May cause DNA damage and mutagenicity"
    }
  ],
  "recommendations": [
    "Recommend Ames test validation",
    "Consider structural optimization to reduce toxicity"
  ]
}

Risk Levels

• HIGH: Known significant toxicity, strongly recommended to avoid
• MEDIUM: Potential toxicity, further evaluation recommended
• LOW: Minor concern, can be considered based on specific circumstances

Notes

1. This tool is based on known alert structures and cannot replace comprehensive toxicological assessment
2. False positives and false negatives may both exist
3. Recommended to use with other ADMET prediction tools

References

• Ashby J., Tennant R.W. (1988) Chemical structure, Salmonella mutagenicity...
• Kazius J., McGuire R., Bursi R. (2005) Derivation and validation of toxicophores...
• Enoch S.J., Cronin M.T.D. (2010) A review of the electrophilic reaction chemistry...

Risk Assessment

Security Checklist

• No hardcoded credentials or API keys
• No unauthorized file system access (../)
• Output does not expose sensitive information
• Prompt injection protections in place
• Input file paths validated (no ../ traversal)
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no stack traces exposed)
• Dependencies audited

Prerequisites

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

• Successfully executes main functionality
• Output meets quality standards
• Handles edge cases gracefully
• Performance is acceptable

Test Cases

1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:
  • Performance optimization
  • Additional feature support