🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some signs of potential risk, particularly due to subprocess calls and the lack of detailed maintainer information.

Shell risk due to subprocess calls
Metadata risk due to new or inactive maintainer account

Per-check LLM notes

Network: No network calls detected, which is normal for most packages.
Shell: Subprocess calls observed might be legitimate if related to package functionality, but could indicate potential risks if not properly documented.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer has a new or inactive account and lacks a proper author name, indicating potential unreliability.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

ame + "_conf_.xyz", "-m"] subprocess.run(command_run_1, stdout=subprocess.DEVNULL, stderr=subprocess.
t1}_xtb1.out" subprocess.call(comm_xtb, shell=False) os.rename(str(dat_dir
me_conf + ".sdf"] subprocess.run(command_xyz, stdout=subprocess.DEVNULL, stderr=subprocess.DE
eate a coord.ref file subprocess.run( ["crest", xyzin, "--constrain", "1"],
] subprocess.run( command_xyz,
] subprocess.run( command_pdb,

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: lsu.edu>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository hklem/QMzyme 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 QMzyme

Create a fully-functional mini-application that utilizes the QMzyme package to generate and validate models of enzymes based on quantum mechanics principles. This application will serve as a tool for researchers and students interested in understanding enzyme behavior at a molecular level. The app should include the following features:

1. **User Interface**: Develop a simple yet intuitive user interface where users can input basic information about the enzyme they wish to study, such as its name and known structure (if available).
2. **Model Generation**: Utilize QMzyme's capabilities to generate a quantum mechanics-based model of the enzyme. This step involves setting up the computational environment, specifying parameters, and running simulations to create the model.
3. **Validation Tools**: Implement tools within the application to validate the generated model against known experimental data or theoretical predictions. This could involve comparing calculated properties (such as reaction rates or energy levels) with published results.
4. **Visualization**: Provide visualization capabilities so users can view the enzyme structure and the dynamics of its reactions in a graphical format. This helps in better understanding the model and its implications.
5. **Report Generation**: Allow users to generate detailed reports summarizing their findings, including model accuracy, validation outcomes, and any insights gained from the analysis.
6. **Educational Content**: Include educational materials within the application to help users understand the basics of quantum mechanics as applied to enzyme modeling and why such models are important in biochemical research.

Throughout the development process, focus on integrating QMzyme effectively into each feature, ensuring that the application not only leverages its powerful modeling and validation functionalities but also makes these complex processes accessible to non-expert users.