AI Analysis
Final verdict: SAFE
The package appears to be safe for use with no direct indicators of malicious intent. However, there are concerns regarding low maintainer activity which could indicate potential abandonment or other issues.
- No network calls, shell executions, obfuscation, or credential harvesting detected.
- Low maintainer activity raises some concern.
Per-check LLM notes
- Network: No network calls detected, which is normal for a package focused on chemical structure visualization.
- Shell: No shell execution detected, aligning with the expected behavior of a benign scientific tool.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows signs of low maintainer activity and effort, raising some suspicion but not conclusive evidence of malice.
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
No obfuscation patterns detected
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: unistra.fr>
Suspicious Page Links
All external links appear legitimate
Git Repository History
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 6.0
3 maintainer concern(s) found
Author name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)Package has no PyPI classifiers (low effort / metadata quality)
Known CVE Vulnerabilities
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Use this prompt to build a project with ChemographyKit
Create a web-based application that allows users to visualize and explore chemical space using the ChemographyKit Python library. This application should enable users to input molecular structures (in SMILES format) and visualize them on a Generative Topographic Map (GTM). Additionally, include features such as clustering of similar molecules, similarity search, and interactive exploration of the GTM space. Steps: 1. Set up a Flask backend to handle API requests and responses. 2. Integrate ChemographyKit into your project to perform GTM calculations. 3. Use Plotly or a similar JavaScript library to create interactive visualizations. 4. Implement a user interface where users can upload or input SMILES strings of molecules. 5. Utilize ChemographyKit to generate GTM embeddings for these molecules and display them interactively. 6. Add functionality for clustering molecules based on their GTM embeddings. 7. Allow users to perform similarity searches within the dataset by specifying a query molecule. 8. Ensure the application can handle multiple datasets and allow users to switch between them. 9. Provide documentation and examples for using the application effectively. Features: - User-friendly interface for uploading molecules in SMILES format. - Real-time visualization of GTM embeddings for uploaded molecules. - Clustering of molecules based on their chemical properties. - Interactive exploration of the GTM space with zooming and panning capabilities. - Similarity search feature to find molecules similar to a given one. - Support for multiple datasets, allowing users to switch between different sets of molecules easily. Utilization of ChemographyKit: - Use ChemographyKit to preprocess the molecular data before generating GTM embeddings. - Employ ChemographyKit's GTM algorithm to map molecular descriptors onto a 2D or 3D space. - Leverage ChemographyKit's clustering capabilities to group similar molecules together. - Apply ChemographyKit's similarity measures to facilitate the similarity search feature.