alphafold3-tools

v0.4.0 safe
4.0
Medium Risk

Toolkit for alphafold3 input and output files

🤖 AI Analysis

Final verdict: SAFE

The package is generally safe with a moderate network and shell execution risk, but no signs of obfuscation or credential harvesting. The low score is due to incomplete metadata, suggesting the developer might be less established.

  • Moderate network risk due to CIF file retrieval
  • Potential shell execution risks need careful monitoring
Per-check LLM notes
  • Network: Network calls to retrieve CIF files are likely part of the package's functionality for accessing structural biology data.
  • Shell: Subprocess execution may be necessary for running external tools like HMMER, but requires careful review to ensure it does not introduce security vulnerabilities.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author's details are incomplete and the author has only one package, which may indicate a less established or potentially suspicious account.

📦 Package Quality Overall: Medium (5.4/10)

✦ High Test Suite 9.0

Test suite present — 9 test file(s) found

  • Test runner config found: pyproject.toml
  • 9 test file(s) detected (e.g. test_cli.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (14879 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

  • 213 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 56 commits in cddlab/alphafold3_tools
  • Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • b_id}.cif" response = requests.get(full_url) if response.status_code == 200:
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • join(hmmbuild_cmd)}") subprocess.run( hmmbuild_cmd, check=True,
  • abase_path, ] subprocess.run(hmmsearch_cmd, check=True, capture_output=True) # co
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: tmd.ac.jp>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository cddlab/alphafold3_tools 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 alphafold3-tools 
Develop a comprehensive protein structure prediction web application using the 'alphafold3-tools' Python package. This application will allow users to upload their amino acid sequences and receive predictions of the protein structures based on AlphaFold3's advanced algorithms. Here’s a detailed breakdown of the application's functionalities:

1. **User Interface**: Design an intuitive web interface where users can input amino acid sequences (FASTA format). The UI should include fields for sequence submission, options to select computational resources (CPU/GPU), and a progress bar indicating the status of the prediction.

2. **Sequence Validation**: Implement a feature to validate the uploaded sequences before processing. This includes checking for correct FASTA format and ensuring the sequences are within the acceptable length range for AlphaFold3.

3. **Prediction Processing**: Utilize the 'alphafold3-tools' package to preprocess the input sequences, run the AlphaFold3 model for structure prediction, and post-process the results. Ensure efficient handling of computational resources to provide timely predictions.

4. **Visualization**: Integrate a visualization tool within the application to display the predicted 3D protein structures. Users should be able to rotate, zoom, and view the structure from different angles.

5. **Result Export**: Allow users to download the predicted protein structures in various formats such as PDB, CIF, or MOL2. Additionally, provide an option to save the results directly to a cloud storage service like Google Drive or Dropbox.

6. **Documentation and Help**: Include comprehensive documentation and a FAQ section to guide users through the process and answer common questions about the application and AlphaFold3 predictions.

7. **Security Measures**: Ensure user data privacy by implementing secure data handling practices, including encryption of sensitive information and secure server-side storage.

The 'alphafold3-tools' package will be crucial for handling all aspects related to the AlphaFold3 model, from preparing the input data to interpreting and presenting the output predictions. This project aims to make advanced protein structure prediction accessible to researchers and students alike.

💬 Discussion Feed

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