Robust Prediction of Drug Interactions using Chemical Descriptors

Polypharmacy requires accurate prediction of drug-drug interactions to prevent adverse events, yet existing models often lack reliability and explainability. We propose T-DDI, a descriptor-based deep learning framework for multi-class drug-drug interaction prediction. Rather than relying on complex graph embeddings, T-DDI uses explicit physicochemical descriptors and an uncertainty-aware estimator to handle severe class imbalance. Evaluated on 868,069 drug pairs spanning 178 interaction types, T-DDI achieves a Macro F1 of 0.8452 on the held-out test set, improving to 0.8992 within the high-confidence subset (87.91% of test samples), outperforming all evaluated baselines within the architectures and datasets considered here. An illustrative prospective case-study assessment on five newly FDA-approved drugs from late 2025 showed that T-DDI can generate mechanistically plausible DDI hypotheses for compounds not used during model development. T-DDI pairs confidence-stratified predictions with LIME-based feature-level explanations and a web application for screening, supporting more reliable drug safety monitoring.