An advanced web application built with ASP.NET Core 8.0 and Blazor Server that simulates AI-driven drug discovery processes. The application features random molecule generation, AI-based therapeutic scoring, comprehensive data management, and detailed analytics.
- Random Molecule Generation: Generate realistic chemical compounds with customizable parameters
- AI Therapeutic Scoring: Advanced scoring algorithm based on Lipinski's Rule of Five and drug-likeness criteria
- Molecule Database: Complete CRUD operations with SQLite storage
- Search & Filtering: Advanced search capabilities with real-time filtering
- Data Analytics: Comprehensive statistics and data visualization dashboard
- CSV Export: Export molecule data for external analysis
- RESTful API: Complete API with Swagger documentation
- Real-time Updates: Blazor Server for dynamic user interface
- Responsive Design: Bootstrap-based UI optimized for all devices
- Performance Optimized: Database indexing and pagination support
- Comprehensive Logging: Structured logging with Serilog
The application generates realistic molecules with the following properties:
- Molecular Weight: 150-600 Da range
- LogP (Lipophilicity): -2 to 6 range for drug-like properties
- Hydrogen Bond Donors/Acceptors: Realistic counts for drug compounds
- Atomic Composition: Carbon, hydrogen, nitrogen, oxygen atoms
- Rotatable Bonds: Flexibility indicators
- Polar Surface Area: Membrane permeability predictions
- Generates chemically valid SMILES notation
- Includes various functional groups (alcohols, amines, carboxylic acids, etc.)
- Supports aromatic and aliphatic structures
The therapeutic scoring system evaluates molecules based on multiple criteria:
- Molecular Weight: ≤ 500 Da
- LogP: ≤ 5
- Hydrogen Bond Donors: ≤ 5
- Hydrogen Bond Acceptors: ≤ 10
- Toxicity Prediction: Based on structural alerts and properties
- Solubility Assessment: Aqueous solubility predictions
- Synthetic Accessibility: Ease of chemical synthesis
- Overall Therapeutic Potential: Weighted combination of all factors
- Total molecules generated
- Average therapeutic scores
- Distribution of molecular properties
- Top-performing compounds
- Score distribution histograms
- Molecular weight distributions
- LogP vs. score correlations
- Property correlation matrices
- ASP.NET Core 8.0: Modern web framework
- Entity Framework Core: ORM for database operations
- SQLite: Lightweight, file-based database
- Serilog: Structured logging framework
- Blazor Server: Server-side rendering with real-time updates
- Bootstrap 5: Responsive CSS framework
- Chart.js: Interactive data visualizations
- Custom CSS: Pharmaceutical-themed styling
- Swagger/OpenAPI: Comprehensive API documentation
- RESTful Endpoints: Standard HTTP operations
- JSON Responses: Structured data exchange
GET /api/molecules - Get paginated molecules
POST /api/molecules - Create new molecule
GET /api/molecules/{id} - Get specific molecule
PUT /api/molecules/{id} - Update molecule
DELETE /api/molecules/{id} - Delete molecule
POST /api/molecules/generate - Generate random molecules
GET /api/molecules/search - Search molecules
GET /api/molecules/statistics - Get analytics data
GET /api/molecules/export - Export molecules as CSV
- .NET 8.0 SDK
- Visual Studio 2022 or VS Code
- Modern web browser
-
Clone the repository:
git clone <repository-url> cd MoleculeSimulator
-
Restore dependencies:
dotnet restore
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Build the application:
dotnet build
-
Run the application:
dotnet run
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Open your browser and navigate to:
http://localhost:5142
The application uses SQLite with automatic database creation. The database file (molecules.db) will be created in the project root on first run.
- Welcome message and feature overview
- Navigation to key sections
- Quick access to generation and analysis tools
- Customizable generation parameters
- Batch generation capabilities
- Real-time preview of generated molecules
- Export functionality
- Searchable table of all molecules
- Detailed molecule information modals
- Sorting and filtering options
- Score-based categorization
- Comprehensive analytics
- Interactive charts and graphs
- Export capabilities
- Data insights and trends
The application implements established pharmaceutical principles:
Industry-standard criteria for drug-like properties, ensuring generated molecules have realistic pharmaceutical characteristics.
Consideration of Absorption, Distribution, Metabolism, Excretion, and Toxicity factors in the scoring algorithm.
SMILES generation follows chemical rules and produces valid molecular structures.
- Indexed columns for fast querying
- Pagination for large datasets
- Efficient search algorithms
- In-memory caching for frequently accessed data
- Optimized database queries
- Minimal network overhead
- Mobile-friendly interface
- Progressive loading
- Optimized asset delivery
Configure database connection in appsettings.json:
{
"ConnectionStrings": {
"DefaultConnection": "Data Source=molecules.db"
}
}Serilog is configured for comprehensive logging with different levels for development and production environments.
- Machine learning integration with ML.NET
- 3D molecular visualization
- Advanced chemical property calculations
- Integration with chemical databases
- Multi-user support with authentication
- Advanced analytics and reporting
The modular architecture allows for easy extension with additional scoring algorithms, data sources, and visualization components.
This project is created for educational and research purposes. Please ensure compliance with relevant regulations when working with pharmaceutical data.
Contributions are welcome! Please follow the established coding standards and include appropriate tests for new features.
For questions, issues, or feature requests, please refer to the project documentation or create an issue in the repository.
AI-Based Drug Discovery Simulator - Advancing pharmaceutical research through intelligent molecular design and analysis.