🤖 AI Analysis

Final verdict: SAFE

The package shows low risk across all key indicators with no signs of malicious intent. The slight increase in metadata and shell risks is noted but does not warrant concern.

No network calls detected
Low risk of shell execution
Maintainer's newness and package maintenance efforts raise minor concerns

Per-check LLM notes

Network: No network calls were detected.
Shell: Shell execution patterns observed are likely related to launching Streamlit applications and do not inherently indicate malicious activity.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer appears new or inactive, and the lack of classifiers suggests low effort in maintaining the package.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

_file__).parent / "ui.py" subprocess.run([sys.executable, "-m", "streamlit", "run", str(app_path)]) f
cript = Path(sys.argv[1]) subprocess.run([sys.executable, "-m", "streamlit", "run", str(user_script)]

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author "Théo Vienne, Théo Morales Crassier, Tolga Seckin, Noam Balter-Dejeux" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with OrganoMind

Develop a web-based application called 'Molecule Explorer' using Python and the OrganoMind cheminformatics package. This application will allow users to input chemical structures in SMILES format and analyze them through various features provided by OrganoMind. Here’s a detailed plan for the project:

1. **User Interface**: Create a clean, intuitive user interface where users can input SMILES strings of molecules and select from a list of analysis options.
2. **Molecular Properties Analysis**: Utilize OrganoMind to calculate basic properties such as molecular weight, logP, and hydrogen bond donors/receivers.
3. **Structural Visualization**: Implement functionality to visualize the 2D structure of the molecule using OrganoMind’s visualization tools.
4. **Toxicity Prediction**: Use OrganoMind to predict potential toxicity based on the molecular structure. Display the probability of toxicity along with any warnings.
5. **Drug-likeness Assessment**: Evaluate whether the molecule has drug-like characteristics using OrganoMind’s drug-likeness scoring algorithms. Provide a score and a brief explanation of the score.
6. **Integration with External Databases**: Optionally, integrate with external databases to fetch additional information about the molecule, such as known biological activities.
7. **Results Presentation**: Present all results in a clear, organized manner, including visual representations and textual descriptions.
8. **Documentation and Deployment**: Write comprehensive documentation explaining how to use the application and deploy it online for public access.

Use OrganoMind throughout the development process to handle the cheminformatics tasks, ensuring that the application is both powerful and user-friendly.