Documentation

Comprehensive guides, tutorials, and reference documentation for VERA - a visual workflow platform with 100+ specialized nodes for computational chemistry, bioinformatics, and data science.

Getting Started

Getting Started

Complete beginner's guide and learning paths

Getting Started

Step-by-step setup and licensing guide

Quick Start Tutorial

Your first workflow in 10 minutes

Interface Overview

Understanding the VERA workspace

Example Workflows

Ready-to-use workflow templates

Core Features

Node-Based Workflows

Creating and managing computational workflows

Canvas Operations

Working with the visual workflow editor

Data Management

Handling molecular and experimental data

File I/O Operations

Importing and exporting data files

Scientific Engines

Molecular Docking

AutoDock Vina and Vina GPU integration

Molecular Dynamics

GROMACS simulation workflows

Cheminformatics

RDKit and OpenBabel molecular processing

Advanced Analysis

Machine Learning

8+ ML algorithms and model training

Data Analysis

Data manipulation, statistics, and processing

Data Visualization

20+ chart types and molecular viewers

Developer Resources

Plugin development and API reference

Choose Your Learning Path

Different paths for different research interests. Pick the one that matches your goals.

Molecular Docking Path

Master AutoDock Vina for drug discovery and virtual screening

Learning Progression:
  1. 1
    Installation & Interface Overview
  2. 2
    AutoDock Vina CPU Docking Basics
  3. 3
    Receptor & Ligand Preparation (PDBQT conversion)
  4. 4
    Grid Search & Parameter Optimization
  5. 5
    Docking Results Analysis & RMSD clustering
  6. 6
    GPU Acceleration with AutoDock Vina GPU
  7. 7
    High-throughput Virtual Screening
  8. 8
    Protein-ligand Interaction Analysis
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Molecular Dynamics Path

Learn GROMACS for protein simulations and trajectory analysis

Learning Progression:
  1. 1
    Installation & System Requirements
  2. 2
    GROMACS Preparation (TPR generation)
  3. 3
    Energy Minimization with GPU acceleration
  4. 4
    NVT/NPT Equilibration for membrane proteins
  5. 5
    Production MD Runs with checkpoint support
  6. 6
    MDAnalysis: RMSD, RMSF, Radius of Gyration
  7. 7
    CHARMM-GUI Integration & Advanced Setup
  8. 8
    Trajectory visualization with NGL.js
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Machine Learning Path

Learn machine learning for molecular data analysis

Learning Progression:
  1. 1
    Data Preparation & Train-Test Split
  2. 2
    Classification Algorithms (8+ methods)
  3. 3
    Regression Algorithms (3+ methods)
  4. 4
    Clustering Analysis (K-means, Hierarchical)
  5. 5
    Dimensionality Reduction (PCA)
  6. 6
    Model Evaluation & Cross-validation
  7. 7
    Model Persistence (Save/Load)
  8. 8
    Feature Importance & Model Interpretation
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Data Science & Visualization

Master data analysis and scientific visualization

Learning Progression:
  1. 1
    DataFrame Operations (Select, Filter, Merge)
  2. 2
    File I/O for Molecular Data (20+ formats)
  3. 3
    Statistical Plotting (20+ chart types)
  4. 4
    Molecular Structure Visualization
  5. 5
    Protein-ligand Interaction Analysis (ProLIF)
  6. 6
    3D Molecular Viewer Integration
  7. 7
    2D Structure Drawing & Annotation
  8. 8
    Publication-ready Plot Generation
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