DDI-HierPred: An Artificial Intelligence-Based Hierarchical PK/PD Platform for Drug-Drug Interaction Prediction

Background/Objectives: Pharmacokinetic and pharmacodynamic drug-drug interactions are major determinants of drug safety in polypharmacy, with potential consequences including reduced therapeutic efficacy, altered drug exposure, and increased adverse effects. This study presents DDI-HierPred, an artificial intelligence-based hierarchical framework for drug-drug interaction prediction using Morgan fingerprint-based drug-pair representations. Methods: At Level 1, Logistic Regression, Random Forest, Linear SVM, and XGBoost were compared for pharmacokinetic/pharmacodynamic (PK/PD) classification. At Level 2, an XGBoost multiclass classifier was used to predict 61 interaction subtype classes. End-to-end performance was evaluated using predicted Level 1 routing, and additional drug-identity-disjoint evaluations were conducted to assess generalization when one or both drugs were unseen during training. Y-randomization analyses were performed for both classification levels. Results: Linear SVM achieved the best Level 1 performance, yielding an accuracy of 0.8661, a ROC-AUC of 0.9380, and an MCC of 0.7303 on the independent test set. At Level 2, the XGBoost classifier achieved an accuracy of 0.8712, a balanced accuracy of 0.9087, a macro F1-score of 0.9115, and a Top-3 accuracy of 0.9868. Because the Level 2 test partition was also used for algorithm comparison and model selection, these results should be regarded as exploratory and potentially optimistic rather than as an independent final evaluation. When evaluated end-to-end using predicted Level 1 routing, performance decreased to an accuracy of 0.5767, balanced accuracy of 0.3573, and macro F1-score of 0.4379. Additional drug-identity-disjoint evaluations showed further performance reductions when one or both drugs were unseen during training, highlighting the greater difficulty of generalization to previously unseen drug identities. Y-randomization analyses supported the robustness of both classification levels. Conclusions: The framework was deployed as a publicly accessible web platform integrating documented interaction lookup, hierarchical PK/PD classification, Level 1-constrained subtype prediction, confidence scoring, single-pair and batch analysis, ranked Top-3 predictions, and downloadable reports. These findings indicate that upstream routing and unseen-drug generalization remain important limitations of the current framework. DDI-HierPred therefore provides a computational platform for research-oriented screening, interpretation, and prioritization of potential drug-drug interactions.