alphadynamics

v0.4.3 safe
3.0
Low Risk

Compact sequence-only neural propagator for protein torsion dynamics — 2.39× lower JSD than Microsoft Timewarp at 3000× fewer parameters.

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risks with no evidence of malicious activities. The network calls and obfuscation patterns are likely benign.

  • Low network and obfuscation risk
  • No signs of credential handling or malicious intent
Per-check LLM notes
  • Network: The network calls seem to be related to fetching data from a URL, possibly for package updates or other legitimate purposes.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: The code snippets show typical patterns of model evaluation and tensor operations which could be obfuscated but appear to be part of normal machine learning processing.
  • Credentials: No clear signs of credential harvesting or secret handling detected.
  • Metadata: The maintainer has an incomplete profile and a new/inactive account, which could indicate a low-risk but suspicious activity.

📦 Package Quality Overall: Medium (6.2/10)

✦ High Test Suite 9.0

Test suite present — 4 test file(s) found

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

Some documentation present

  • Detailed PyPI description (24911 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

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

Active multi-contributor project

  • 3 unique contributor(s) across 47 commits in krisss0mecom/AlphaDynamics
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

  • None: try: req = urllib.request.Request( PYPI_URL, headers={"User-Ag
  • eck"}, ) with urllib.request.urlopen(req, timeout=timeout) as r: data = json.
  • err.flush() try: urllib.request.urlretrieve(url, tmp, _hook) if show_progress:
⚠ Code Obfuscation score 10.0

Found 5 obfuscation pattern(s)

  • unchanged. """ model.eval() state = as_tensor(seed_frame[None], device) res_id
  • mup_frames.shape}") model.eval() W, N, _ = warmup_frames.shape B = ensemble_size
  • eed_frames.shape}") model.eval() B, N, _ = seed_frames.shape state = as_tensor(seed
  • l_mode : bool Call ``.eval()`` on the loaded model. Default: ``True``. """ if n
  • if eval_mode: model.eval() return model [build-system] requires = ["setuptools>=
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository krisss0mecom/AlphaDynamics 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 alphadynamics 
Develop a Python-based mini-application that leverages the 'alphadynamics' package to predict torsional dynamics of proteins based on their amino acid sequences. This application should serve as a user-friendly tool for researchers and biochemists to quickly analyze and visualize protein dynamics without needing extensive computational resources.

Steps to complete the project:
1. Install the necessary packages including 'alphadynamics', 'numpy', 'matplotlib', and 'biopython'.
2. Create a function to read amino acid sequences from a FASTA file or input string.
3. Implement a method using 'alphadynamics' to predict torsional angles over time for the given protein sequences.
4. Develop a visualization component to plot these predicted torsional angles against time.
5. Add functionality to save the predictions and plots to files.
6. Include a simple command-line interface for users to interact with the application.
7. Write comprehensive documentation explaining how to use the application and interpret its output.

Suggested Features:
- Support for multiple protein sequences in a single analysis session.
- Option to choose between different prediction models available in 'alphadynamics'.
- Real-time plotting of torsional angle predictions during computation.
- Error handling and informative messages for common issues like invalid input sequences.
- Compatibility with various operating systems.

How 'alphadynamics' is Utilized:
- The core functionality of 'alphadynamics' will be used to compute the dynamic behavior of the torsional angles in the protein sequences. Users will provide the sequence data, and your application will process it through the 'alphadynamics' package to generate predictions. These predictions will then be visualized and saved according to user specifications.

💬 Discussion Feed

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