Documentation/Analysis/Machine Learning

Machine Learning Analysis

Comprehensive machine learning capabilities powered by scikit-learn, featuring 8+ algorithms for classification, regression, clustering, and dimensionality reduction.

Machine Learning Capabilities

Complete machine learning toolkit integrated into your workflows.

8+ ML algorithms from scikit-learn

Classification (Logistic Regression, Random Forest, SVM, KNN)

Regression (Linear, Random Forest, SVR)

Clustering (K-Means, Agglomerative)

Dimensionality reduction (PCA)

Model persistence (save/load)

Evaluation metrics and visualization

Cross-validation support

Available Algorithms

Comprehensive collection of machine learning algorithms from scikit-learn.

Classification

Logistic Regression

Linear model for binary and multi-class classification

Use Cases:
  • •Binary classification
  • •Probability estimation
  • •Feature importance
Key Parameters:
Regularization (L1/L2)C parameterSolver selection

Random Forest Classifier

Ensemble of decision trees with bagging

Use Cases:
  • •Non-linear relationships
  • •Feature importance
  • •Robust to overfitting
Key Parameters:
Number of treesMax depthMin samples split

Support Vector Machine

Maximum margin classifier with kernel methods

Use Cases:
  • •High-dimensional data
  • •Non-linear boundaries
  • •Binary classification
Key Parameters:
Kernel typeC parameterGamma

K-Nearest Neighbors

Instance-based learning algorithm

Use Cases:
  • •Non-parametric
  • •Local patterns
  • •Multi-class
Key Parameters:
Number of neighborsDistance metricWeights

Regression

Linear Regression

Simple linear model for continuous targets

Use Cases:
  • •Linear relationships
  • •Interpretability
  • •Baseline model
Key Parameters:
Fit interceptNormalization

Random Forest Regressor

Ensemble method for non-linear regression

Use Cases:
  • •Complex patterns
  • •Feature importance
  • •Robust predictions
Key Parameters:
Number of estimatorsMax featuresBootstrap

Support Vector Regression

Epsilon-insensitive loss regression

Use Cases:
  • •Non-linear regression
  • •Robust to outliers
  • •High dimensions
Key Parameters:
KernelEpsilonC parameter

Clustering

K-Means

Partition-based clustering algorithm

Use Cases:
  • •Spherical clusters
  • •Large datasets
  • •Known K
Key Parameters:
Number of clustersInitializationMax iterations

Agglomerative Clustering

Hierarchical bottom-up clustering

Use Cases:
  • •Dendrogram analysis
  • •Variable K
  • •Non-spherical clusters
Key Parameters:
Linkage criterionDistance metricNumber of clusters

Dimensionality Reduction

PCA

Principal Component Analysis for feature reduction

Use Cases:
  • •Visualization
  • •Noise reduction
  • •Feature extraction
Key Parameters:
Number of componentsWhitenSVD solver

Evaluation Metrics

Comprehensive metrics for model evaluation and validation.

Classification Metrics

Accuracy0-1

Overall correct predictions

Precision0-1

Positive predictive value

Recall0-1

True positive rate

F1 Score0-1

Harmonic mean of precision and recall

ROC AUC0-1

Area under ROC curve

Confusion MatrixMatrix

True vs predicted classes

Regression Metrics

MSE0-∞

Mean Squared Error

RMSE0-∞

Root Mean Squared Error

MAE0-∞

Mean Absolute Error

R²(-∞)-1

Coefficient of determination

Explained Variance0-1

Variance explained by model

Clustering Metrics

Silhouette Score-1 to 1

Cluster separation quality

Inertia0-∞

Within-cluster sum of squares

Davies-Bouldin0-∞

Cluster similarity measure

Calinski-Harabasz0-∞

Between/within cluster ratio

Common ML Workflows

Typical machine learning workflows in computational chemistry.

QSAR Model Development

Build predictive models for molecular properties

  1. 1Load molecular dataset (CSV/SDF)
  2. 2Calculate molecular descriptors
  3. 3Split data (80/20 train/test)
  4. 4Train Random Forest Regressor
  5. 5Evaluate with R² and RMSE
  6. 6Feature importance analysis
  7. 7Save model for deployment
  8. 8Generate predictions on new compounds

Activity Classification

Binary classification for bioactivity

  1. 1Import bioactivity data
  2. 2Balance dataset if needed
  3. 3Feature selection/engineering
  4. 4Train multiple classifiers
  5. 5Cross-validation
  6. 6Compare model performance
  7. 7Generate ROC curves
  8. 8Deploy best model

Compound Clustering

Group similar molecules

  1. 1Calculate molecular fingerprints
  2. 2Apply PCA for visualization
  3. 3Determine optimal K (elbow method)
  4. 4Run K-means clustering
  5. 5Analyze cluster profiles
  6. 6Visualize in 2D/3D
  7. 7Export cluster assignments
  8. 8Representative selection

Best Practices

Data Preparation

  • Handle missing values appropriately
  • Scale/normalize features when needed
  • Balance classes for classification
  • Remove highly correlated features

Model Training

  • Use cross-validation for robust evaluation
  • Perform hyperparameter tuning
  • Compare multiple algorithms
  • Check for overfitting

Feature Engineering

  • Calculate molecular descriptors
  • Generate fingerprints for similarity
  • Use domain knowledge for features
  • Apply dimensionality reduction

Model Deployment

  • Save models with proper versioning
  • Document model parameters
  • Test on hold-out dataset
  • Monitor model performance