Accurate, interpretable prediction of acute oral toxicity (LD₅₀) is challenged by severe dataset imbalance and complex structure-activity relationships. This study develops a transparent QSAR framework by integrating 2D topological and 3D conformational descriptors with hybrid SMOTE-RUS resampling to classify rat oral LD₅₀ into Very Toxic vs. Not Very Toxic. Using 8,396 compounds from NICEATM–EPA NCCT, features were refined via sequential filtering, correlation pruning, and Random Forest ranking. Among seven optimized machine learning models, XGBoost achieved superior external performance (F₁=0.62, accuracy = 0.87). The model’s interpretability was ensured via Explainable AI: Permutation Feature Importance highlighted global contributors like nP and TDB01m; SHAP analysis identified local determinants such as TPSA(Tot); and surrogate decision trees distilled the logic into rule-based thresholds with high fidelity (≥ 0.88). This pipeline aligns with OECD principles, offering a regulatory-grade, explainable QSAR model that balances predictive power with mechanistic transparency for chemical safety assessment.
Elsayad et al. (Mon,) studied this question.