🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of potential misuse with git commands and obfuscated code, but there is no clear evidence of malicious intent. The sparse metadata adds to the suspicion.

Shell risk due to git command execution
Obfuscation risk from use of 'eval()' and 'pickle.loads()'
Sparse and potentially inactive author metadata

Per-check LLM notes

Network: No network calls detected, which is normal and not indicative of any risk.
Shell: Git command execution may be legitimate if the package involves version control operations, but requires further investigation to ensure it's not being used maliciously.
Obfuscation: The use of 'eval()' and 'pickle.loads()' could indicate obfuscation or potential code injection risks, but without further context, it's unclear if this is malicious or part of legitimate functionality.
Credentials: No patterns indicative of credential harvesting were detected.
Metadata: The author's information is sparse and the account seems new or inactive, which raises some suspicion but not enough to conclusively identify it as malicious.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

eval_mode: model.eval() return model def save(self, path: str):
"pickled"][0] entry = pickle.loads(row) return entry @requires_package("pyarro

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

ust git process = subprocess.Popen([command] + args, cwd=cwd, env=env,

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: openforcefield.org>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository openforcefield/openff-nagl appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with acellera-openff-nagl

Develop a molecular property prediction tool using the 'acellera-openff-nagl' Python package. This application will allow users to input a molecule in SMILES format and predict its properties based on graph convolutional network models trained on molecular graphs. Here are the steps and features you should include:

1. **User Input Interface**: Design a simple command-line interface where users can enter a molecule's SMILES string.
2. **Molecule Parsing**: Use the Open Force Field (OFF) toolkit to parse the input SMILES string into a molecular graph.
3. **Graph Convolutional Network Model Application**: Apply the graph convolutional network models provided by 'acellera-openff-nagl' to predict molecular properties such as solubility, boiling point, or toxicity.
4. **Output Prediction**: Display the predicted molecular properties in a user-friendly format.
5. **Optional Features**:
- Allow users to choose which specific property they want to predict from a predefined list.
- Implement a feature to save the predicted results to a file.
- Include error handling for invalid SMILES inputs and model loading failures.
6. **Documentation**: Provide clear documentation explaining how to use the tool and any dependencies required.

Utilize the 'acellera-openff-nagl' package to perform the graph convolutional network operations necessary for predicting molecular properties. Ensure that your application is well-structured and modular, allowing for easy updates or integration of additional models or properties in the future.