AI Analysis
Final verdict: SAFE
The package is considered safe as it has no network calls, no obfuscation, and no signs of credential harvesting. The shell risk due to subprocess.Popen usage requires further review, but there's insufficient evidence to suggest a supply-chain attack.
- No network calls
- Low obfuscation risk
- Shell risk due to subprocess.Popen usage
Per-check LLM notes
- Network: No network calls detected, which is normal and not indicative of malicious activity.
- Shell: The use of subprocess.Popen suggests the package may execute external commands, which could be part of its functionality but should be reviewed to ensure it does not pose a security risk.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows some red flags but lacks clear evidence of malice.
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
score 2.0
Found 1 shell execution pattern(s)
)) (output, errors) = subprocess.Popen( [self.cxcalc, filename]+options.split(), stdout
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: chem.ku.dk>
Suspicious Page Links
score 4.0
Found 2 suspicious link(s) on the package page
Non-HTTPS external link: http://propka.org/Non-HTTPS external link: http://propka.org
Git Repository History
score 2.5
Git history flags: Repository has zero stars and zero forks
Repository has zero stars and zero forks
Maintainer History
score 6.0
3 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor name is missing or very shortAuthor "" 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-propka
Create a command-line tool named 'pKaAnalyzer' using Python that leverages the 'acellera-propka' package to perform heuristic pKa calculations on protein-ligand complexes. This tool should allow users to input a PDB file containing the structure of their protein-ligand complex, and then output the calculated pKa values for titratable residues within the protein, as well as any ionizable groups within the ligand. The application should include the following features: 1. **Input Validation**: Ensure the user-provided PDB file is correctly formatted and contains both a protein and a ligand. 2. **Visualization**: Integrate functionality to visualize the protein-ligand complex with highlighted titratable residues and ionizable ligand groups using a simple visualization library like PyMOL (via its command-line interface). 3. **Detailed Output**: Provide a detailed report of the pKa calculations, including residue names, their locations, and their predicted pKa values. Additionally, summarize the overall effect of the ligand on the protein’s pKa landscape. 4. **Interactive Mode**: Allow users to interactively adjust parameters such as pH range and solvent conditions to see how these changes affect the pKa predictions. 5. **Help and Documentation**: Include comprehensive help documentation accessible via command line options to guide users through the setup and use of the tool. 6. **Integration Testing**: Write unit tests to ensure that the pKa calculations are performed accurately under various conditions and that the visualization component functions correctly. The 'acellera-propka' package will be utilized primarily for calculating the pKa values of the residues and ligand groups. Users should be able to install the required package via pip and run the tool from the command line after providing the necessary input files. Your task is to design and implement this tool from scratch, ensuring it is user-friendly, efficient, and reliable.