Outline

Navigate the Orange3 visual programming interface for interactive data science and machine learning • Import, clean, and preprocess chemical datasets for QSAR model development • Construct automated data pipelines connecting descriptor generation to model training workflows

Outline

Implement Random Forest algorithms for robust prediction of biological activity from molecular features • Apply Support Vector Machine (SVM) models to capture complex non-linear structure-activity relationships • Compare algorithm performance characteristics and select optimal methods for specific datasets

Outline

Execute leave-one-out (LOO) validation to assess model predictivity on individual compounds • Design random sampling and k-fold cross-validation protocols for reliable performance estimation • Evaluate statistical metrics including R², Q², RMSE, and external test set predictions

Outline

Interpret model outputs to identify structural features driving biological activity • Generate publication-quality visualizations including scatter plots, regression lines, and feature importance charts • Communicate QSAR findings effectively to interdisciplinary stakeholders and decision-makers

Outline

Apply validated QSAR models to prioritize compounds in virtual screening campaigns • Examine real-world case studies demonstrating ML-driven QSAR in lead optimization • Integrate predictive modeling into contemporary pharmaceutical development pipelines

Outline

Investigate deep learning approaches and ensemble methods for enhanced QSAR prediction accuracy • Address challenges of model applicability domain and extrapolation beyond training data • Explore regulatory perspectives on QSAR models for toxicity and environmental fate prediction