AI Analysis
Final verdict: SAFE
The package appears to be a legitimate tool for drug activity prediction with no indications of malicious intent or harmful actions. The metadata risk is slightly elevated due to its newness and lack of maintainer history, but there are no other significant risks identified.
- Low network, shell, obfuscation, and credential risks
- Elevated metadata risk due to newness of the package
Per-check LLM notes
- Network: No network calls detected, which is normal for an EDA-focused package.
- Shell: No shell executions detected, indicating the package does not attempt to execute external commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package is new and lacks maintainer history, but there are no immediate red flags like typosquatting or suspicious links.
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: gmil.com
Suspicious Page Links
All external links appear legitimate
Git Repository History
score 3.0
Repository not found (deleted or private)
Repository not found (deleted or private)
Maintainer History
score 4.0
2 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor "Ishan" 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 Drug-EDA
Create a drug activity prediction mini-application using the 'Drug-EDA' package. This application will serve as a tool for researchers and pharmaceutical scientists to predict the efficacy of new drugs based on their chemical structure and other relevant features. The application should include the following key functionalities: 1. **Data Input**: Users should be able to input data about the drug candidates, including but not limited to molecular structures, biological activities, and any other relevant descriptors. 2. **Exploratory Data Analysis (EDA)**: Utilize the EDA tools within 'Drug-EDA' to perform initial analysis on the dataset. This includes visualizing distributions of drug activities, identifying correlations between different descriptors, and detecting outliers. 3. **Model Training & Evaluation**: Implement a model training pipeline using 'Drug-EDA'. This pipeline should allow users to choose from various machine learning models (e.g., Random Forest, SVM, Neural Networks) and evaluate the performance of these models on a validation set. 4. **Prediction Interface**: Once a satisfactory model is selected, provide an interface where users can input new drug candidate data and receive predictions on their potential efficacy. 5. **Report Generation**: Finally, the application should generate a comprehensive report summarizing the EDA findings, model selection process, and prediction results. This report can be in PDF format and should be easily shareable. Throughout the development process, leverage the 'Drug-EDA' package for its utilities in handling drug-related datasets, performing efficient EDA, and facilitating the model training and evaluation processes.