Machine learning prediction of bile salt export pump inhibition with Boruta feature selection in imbalanced data

Bile salt export pump (BSEP) inhibition is an important mechanistic risk factor for cholestatic drug-induced liver injury. Quantitative structure-activity relationship (QSAR) models may support early safety screening, but practical BSEP datasets are often limited in size and imbalanced.

We evaluated a machine-learning workflow combining molecular descriptors and fingerprints, Boruta feature selection, and class-imbalance training. Multiple algorithms were assessed using cross-validation, a held-out test set, and an independent external validation set composed of compounds synthesized in an industrial research setting.

Tree-based models generally showed the strongest external performance. In particular, models using RDKit2D descriptors achieved Matthews correlation coefficients (MCC) above 0.65 and a ROC-AUC of 0.87, while several other tree-based model representation combinations achieved MCC values around 0.45-0.50. Boruta markedly reduced feature dimensionality, but its effect on predictive performance depended on both the molecular representation and learning algorithm; it was most beneficial for selected high-dimensional fingerprints and tree-based models, while offering limited or unfavorable effects in some support vector machine settings. Applicability-domain and SHAP analyses further revealed representation-dependent generalization patterns and highlighted contributions from lipophilicity, polarity, solubility, and molecular complexity.

Overall, the workflow maintained useful predictive performance under small and imbalanced conditions and provides a practical framework for early-stage BSEP risk screening.