How This Workflow Works
This workflow demonstrates how to train and evaluate several machine learning models to classify chemical compounds as active or inactive based on their molecular structure and activity data. It processes molecular data, generates numeric fingerprints, applies three different classification algorithms, and compares their performance using robust validation and visualization tools.
Key Features:
- Automate the classification of molecules into active or inactive categories
- Compare multiple machine learning models to identify the most robust approach
- Use cross-validation to ensure reliable performance estimates
- Visualize model results with ROC curves and confusion matrices
Step-by-step:
1. Transform Molecular Data for Analysis:
The workflow labels each compound as active or inactive based on its activity value, then converts molecular structure information into a numeric fingerprint. This fingerprint serves as a compact, machine-readable representation of each molecule, enabling effective input for machine learning algorithms.
2. Train Multiple Classification Models:
The processed data is split into training and test sets using cross-validation. Three different machine learning models—Random Forest, Neural Network (RProp), and Support Vector Machine—are trained in parallel. Each model learns to distinguish between active and inactive compounds based on the molecular fingerprints.
3. Evaluate Model Performance:
Each trained model is tested on unseen data to estimate its classification accuracy. The workflow aggregates performance metrics across validation folds, providing a robust assessment of each model’s ability to generalize to new compounds.
4. Visualize and Compare Results:
The workflow presents key evaluation metrics, including ROC curves and confusion matrices, in a dashboard. This allows users to directly compare the strengths and weaknesses of each model and select the most suitable one for further development.