Add comprehensive documentation for ProteinFlex

- Add main README with project overview
- Add architecture documentation
- Add advanced features documentation
- Add optimization documentation (memory, processing, monitoring)
- Add deployment documentation (setup, configuration, monitoring)

Documentation provides complete coverage of system components,
features, optimization techniques, and deployment procedures.

docs/README.md

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+# ProteinFlex: Advanced Protein Generation and Analysis Platform
+
+ProteinFlex is a cutting-edge platform for protein generation and analysis using state-of-the-art transformer architectures and advanced optimization techniques. The platform combines text-to-protein generation capabilities with comprehensive structural analysis and validation.
+
+## Features
+
+### Advanced Protein Generation
+- Text-to-protein sequence generation using transformer architectures
+- Structure prediction and validation
+- Binding site analysis and prediction
+- Fold recognition and classification
+
+### Optimization and Performance
+- Advanced memory management for efficient protein processing
+- Hardware-adaptive processing optimization
+- Real-time performance monitoring and adaptation
+- Support for various hardware configurations (CPU, GPU, etc.)
+
+### Visualization and Analysis
+- Interactive 3D protein structure visualization
+- Real-time structure analysis
+- Binding site visualization
+- Fold comparison tools
+
+## Architecture
+
+ProteinFlex uses a modular architecture with the following key components:
+
+- **Core Generation Engine**: Advanced transformer-based models for protein generation
+- **Optimization Layer**: Memory management and hardware optimization
+- **Analysis Pipeline**: Structure validation and analysis tools
+- **Visualization System**: Interactive 3D visualization components
+
+For detailed architecture information, see [Architecture Overview](architecture/overview.md).
+
+## Getting Started
+
+### Prerequisites
+- Python 3.8+
+- CUDA-capable GPU (recommended)
+- Required Python packages (see requirements.txt)
+
+### Installation
+```bash
+git clone https://github.com/VishwamAI/ProtienFlex.git
+cd ProtienFlex
+python -m venv venv
+source venv/bin/activate  # or `venv\Scripts\activate` on Windows
+pip install -r requirements.txt
+```
+
+### Basic Usage
+```python
+from proteinflex import ProteinGenerator
+
+# Initialize generator
+generator = ProteinGenerator()
+
+# Generate protein from description
+protein = generator.generate("A stable protein that binds to ACE2 receptor")
+
+# Analyze structure
+structure = protein.predict_structure()
+binding_sites = protein.predict_binding_sites()
+
+# Visualize results
+protein.visualize_structure()
+```
+
+## Advanced Features
+
+ProteinFlex includes numerous advanced features for protein analysis and optimization:
+
+- **Memory Optimization**: Advanced memory management for large protein structures
+- **Hardware Adaptation**: Automatic optimization for available hardware
+- **Performance Monitoring**: Real-time performance tracking and optimization
+
+For detailed information about advanced features, see [Advanced Features](features/advanced_features.md).
+
+## Optimization
+
+The platform includes sophisticated optimization techniques:
+
+- **Memory Management**: Efficient handling of large protein structures
+- **Adaptive Processing**: Hardware-specific optimizations
+- **Performance Monitoring**: Real-time performance tracking
+
+For detailed optimization information, see [Optimization Guide](optimization/memory_management.md).
+
+## Deployment
+
+For deployment instructions and configuration details, see:
+- [Setup Guide](deployment/setup.md)
+- [Configuration Guide](deployment/configuration.md)
+- [Monitoring Guide](deployment/monitoring.md)
+
+## Contributing
+
+We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.
+
+## License
+
+This project is licensed under the MIT License - see the LICENSE file for details.
+
+## Acknowledgments
+
+- DeepMind's AlphaFold project for inspiration and methodologies
+- The protein research community for valuable datasets and validation methods
+- Contributors and maintainers of key dependencies
+
+## Contact
+
+For questions and support, please open an issue in the GitHub repository.

docs/architecture/overview.md

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+# ProteinFlex Architecture Overview
+
+## System Architecture
+
+ProteinFlex uses a modular, layered architecture designed for scalability and extensibility. The system is composed of several key layers that work together to provide comprehensive protein generation and analysis capabilities.
+
+### Core Layers
+
+1. **Generation Layer**
+   - Transformer-based protein generation models
+   - Text-to-protein sequence conversion
+   - Structure prediction pipeline
+   - Validation mechanisms
+
+2. **Optimization Layer**
+   - Memory management system
+   - Hardware-adaptive processing
+   - Performance monitoring
+   - Resource optimization
+
+3. **Analysis Layer**
+   - Structure analysis tools
+   - Binding site prediction
+   - Fold recognition
+   - Validation pipeline
+
+4. **Visualization Layer**
+   - 3D structure visualization
+   - Interactive analysis tools
+   - Real-time updates
+   - Export capabilities
+
+## Component Interaction
+
+The layers interact through well-defined interfaces:
+
+1. **Generation → Optimization**
+   - Resource allocation requests
+   - Performance metrics
+   - Optimization feedback
+
+2. **Optimization → Analysis**
+   - Optimized data structures
+   - Performance boundaries
+   - Resource utilization data
+
+3. **Analysis → Visualization**
+   - Structure data
+   - Analysis results
+   - Interactive elements
+
+## Data Flow
+
+1. **Input Processing**
+   - Text description intake
+   - Parameter validation
+   - Resource assessment
+
+2. **Generation Pipeline**
+   - Sequence generation
+   - Structure prediction
+   - Initial validation
+
+3. **Analysis Pipeline**
+   - Structure analysis
+   - Feature detection
+   - Quality assessment
+
+4. **Output Generation**
+   - Structure visualization
+   - Analysis reports
+   - Performance metrics
+
+## System Requirements
+
+### Hardware Requirements
+- CPU: Multi-core processor
+- Memory: 16GB+ RAM
+- GPU: CUDA-capable (recommended)
+- Storage: 100GB+ available space
+
+### Software Requirements
+- Operating System: Linux/Windows/MacOS
+- Python 3.8+
+- CUDA Toolkit (for GPU support)
+- Required libraries and dependencies
+
+## Scalability
+
+The architecture supports scaling through:
+
+1. **Horizontal Scaling**
+   - Distributed processing
+   - Load balancing
+   - Resource pooling
+
+2. **Vertical Scaling**
+   - Memory optimization
+   - Processing optimization
+   - Resource management
+
+## Security
+
+The system implements several security measures:
+
+1. **Data Protection**
+   - Input validation
+   - Secure processing
+   - Output verification
+
+2. **Resource Protection**
+   - Access control
+   - Resource limits
+   - Monitoring systems
+
+## Monitoring
+
+The architecture includes comprehensive monitoring:
+
+1. **Performance Monitoring**
+   - Resource usage tracking
+   - Performance metrics
+   - Optimization feedback
+
+2. **Health Monitoring**
+   - System status
+   - Component health
+   - Error tracking
+
+## Future Extensibility
+
+The architecture is designed for future expansion:
+
+1. **New Models**
+   - Additional generation models
+   - Enhanced analysis tools
+   - Improved visualization
+
+2. **Enhanced Features**
+   - Advanced optimization
+   - Extended analysis
+   - Improved visualization
+
+## Integration Points
+
+The system provides integration capabilities:
+
+1. **External Tools**
+   - Analysis tools
+   - Visualization systems
+   - Data sources
+
+2. **APIs**
+   - REST API
+   - Python API
+   - Data exchange formats