Node-Based Workflows
Build complex computational workflows by connecting specialized nodes. Visual programming for computational chemistry and bioinformatics.
Key Features
Powerful features that make workflow creation intuitive and efficient.
Node-Based Architecture
Build complex workflows by connecting computational nodes
- •110+ specialized nodes across 13 categories
- •Drag-and-drop workflow creation
- •Visual connection system
- •Reusable workflow components
- •Modular design for easy customization
Data Flow Management
Automatic data type checking and conversion between nodes
- •Automatic type compatibility checking
- •Real-time connection validation
- •Data format conversion
- •Error handling and debugging
- •Memory-efficient data streaming
Execution Control
Full control over workflow execution with advanced features
- •Run, pause, resume, and stop workflows
- •Step-by-step execution for debugging
- •Parallel execution for independent nodes
- •Real-time progress monitoring
- •Execution history and logging
Workflow Validation
Ensure workflow integrity before execution
- •Pre-execution validation
- •Node dependency checking
- •Data flow verification
- •Resource requirement assessment
- •Performance optimization suggestions
Workflow Types
Different workflow patterns for different computational needs.
Linear Workflows
BeginnerSimple sequential processing pipelines
Example Flow:
File Input → Data Processing → Analysis → Visualization
Use Case:
Basic data analysis and molecular property calculations
Branching Workflows
IntermediateConditional logic and decision trees
Example Flow:
Data Input → Condition Check → Branch A/B → Merge Results
Use Case:
Quality control, filtering, and conditional processing
Iterative Workflows
AdvancedLoops and repeated processing
Example Flow:
Initialize → Process Batch → Check Completion → Loop Back
Use Case:
Batch processing, optimization loops, and parameter sweeps
Parallel Workflows
AdvancedIndependent processing streams
Example Flow:
Split Data → Process A + Process B → Combine Results
Use Case:
High-throughput screening and comparative analysis
Common Workflow Patterns
Reusable patterns that form the basis of many computational workflows.
Data Pipeline
Process raw data into insights
Typical Nodes:
Load experimental data, clean missing values, calculate descriptors, run ML model, plot results
Molecular Screening
High-throughput compound evaluation
Typical Nodes:
Load compound library, prepare molecules, dock against target, score results, filter hits
Simulation Workflow
Molecular dynamics preparation and analysis
Typical Nodes:
Load protein structure, prepare simulation system, minimize energy, equilibrate, run production MD
Comparative Analysis
Compare multiple datasets or methods
Typical Nodes:
Load different datasets, process with different methods, compare results, generate comparison report
Best Practices
Tips and techniques for building robust and efficient workflows.
Organize Your Nodes
Keep related nodes together and use clear naming
- ✓Group nodes by function (input, processing, analysis, output)
- ✓Use descriptive node titles
- ✓Leave space for connections and modifications
- ✓Use comments for complex workflows
Validate Early and Often
Check your workflow before running expensive computations
- ✓Use the Validate button before running
- ✓Test with small datasets first
- ✓Check node connections for errors
- ✓Monitor memory usage for large datasets
Plan for Errors
Handle potential issues gracefully
- ✓Add error handling nodes where appropriate
- ✓Use checkpoint nodes for long-running workflows
- ✓Implement fallback options for failed nodes
- ✓Save intermediate results regularly
Optimize Performance
Make your workflows run faster and more efficiently
- ✓Use GPU-accelerated nodes when available
- ✓Process data in batches for large datasets
- ✓Minimize data conversion between nodes
- ✓Cache intermediate results when possible
Ready to Build Your First Workflow?
Learn how to create workflows with the visual canvas and explore node connections.