Toxicity Structure Alert

Source: https://github.com/aipoch/medical-research-skills

Toxicity Structure Alert (Skill ID: 141)

Identify potential toxic structural alerts in drug molecules.

When to Use

• Use this skill when the task is to Identify potential toxic structural alerts in drug molecules by scanning.
• Use this skill for data analysis tasks that require explicit assumptions, bounded scope, and a reproducible output format.
• Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.

Key Features

See ## Features above for related details.

• Scope-focused workflow aligned to: Analyze data with toxicity-structure-alert using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.
• Packaged executable path(s): scripts/main.py.
• Reference material available in references/ for task-specific guidance.
• Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

• Python 3.8+
• RDKit

Example Usage

See ## Usage above for related details.

cd "20260318/scientific-skills/Data Analytics/toxicity-structure-alert"
python -m py_compile scripts/main.py
python scripts/main.py --help

Example run plan:

1. Confirm the user input, output path, and any required config values.
2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
3. Run python scripts/main.py with the validated inputs.
4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

See ## Workflow above for related details.

• Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
• Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
• Primary implementation surface: scripts/main.py.
• Reference guidance: references/ contains supporting rules, prompts, or checklists.
• Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
• Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

python -m py_compile scripts/main.py
python scripts/main.py --help
python scripts/main.py --input "Audit validation sample with explicit symptoms, history, assessment, and next-step plan." --format json

Workflow

1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

Features

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

Supported Alert Structures

Usage

python -m py_compile scripts/main.py

# Example invocation: 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