alphajudge

v1.1.0 safe
4.0
Medium Risk

Evaluate AlphaFold-predicted protein complexes using confidence metrics and interface biophysics.

🤖 AI Analysis

Final verdict: SAFE

The package has minimal risk indicators with no network calls, obfuscation, or credential harvesting. The slight increase in shell risk due to subprocess.run usage is noted but does not elevate the overall risk to a level indicating malicious intent or supply-chain attack.

  • Low network and shell risk
  • No signs of obfuscation or credential harvesting
  • Potential for command injection if inputs are not sanitized
Per-check LLM notes
  • Network: No network calls detected, which is normal and not indicative of malicious activity.
  • Shell: The use of subprocess.run to execute external commands with arguments might be legitimate for functionality but should be reviewed for potential risks like executing untrusted input.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.

📦 Package Quality Overall: Medium (5.8/10)

✦ High Test Suite 9.0

Test suite present — 5 test file(s) found

  • 5 test file(s) detected (e.g. test_biophysics.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (10668 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 175 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 4 unique contributor(s) across 97 commits in KosinskiLab/AlphaJudge
  • Small but multi-author team (3–4 contributors)

🔬 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)

  • -> bool: try: r = subprocess.run([exe, "--help"], capture_output=True, text=True, check=False
  • : list[str]) -> None: r = subprocess.run([exe, *args], capture_output=True, text=True, check=False)
✓ 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

Repository KosinskiLab/AlphaJudge appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Dima Molodenskiy" 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 alphajudge 
Create a mini-application named 'ProteinEvaluator' that leverages the 'alphajudge' Python package to assess the quality of predicted protein structures from AlphaFold. Your application should be designed for both research scientists and bioinformaticians who need to quickly evaluate the stability and reliability of their protein models. Here’s a step-by-step guide on how to develop this application:

1. **Setup**: Start by setting up a virtual environment and installing the necessary packages including 'alphajudge', 'biopython', and any other dependencies required for file handling and visualization.
2. **User Interface**: Design a simple yet intuitive command-line interface (CLI) where users can input the path to their AlphaFold prediction files (.pdb format).
3. **Data Processing**: Implement a function within your application to parse the .pdb files and extract relevant information about the protein structure such as amino acid sequences and atomic coordinates.
4. **Evaluation Metrics**: Use 'alphajudge' to calculate confidence metrics and interface biophysics for the protein complex. Display these metrics in a clear, formatted output to help users understand the structural integrity of their predictions.
5. **Visualization**: Integrate basic visualization capabilities allowing users to see graphical representations of their protein structures alongside the evaluation metrics. This could be done using matplotlib or similar libraries.
6. **Report Generation**: Create a feature that generates a detailed report summarizing all the evaluations performed. This report should include visualizations, numerical metrics, and textual descriptions of the protein's predicted stability.
7. **Interactive Mode**: Offer an interactive mode where users can adjust parameters and see how changes affect the evaluation results, enhancing the educational value of the tool.
8. **Documentation**: Ensure comprehensive documentation is provided, explaining how to install the application, use its features, and interpret the outputs.

By following these steps, you will create a valuable tool for anyone working with protein structures, providing them with quick insights into the quality and reliability of their predictions.

💬 Discussion Feed

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