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<p>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.</p>

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Robust Prediction of Drug Interactions using Chemical Descriptors

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月20日

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  • Quang-Hien Kha,
  • Duc-Quang-Anh Nguyen,
  • Phi Pham Van Hoang,
  • Uyen Khoi-Minh Huynh,
  • Khoa D. Pham,
  • Minh-Thu Phung,
  • Tan-Phat Huynh,
  • Hoang-Bach-Dat Le,
  • Nguyen-Phat Vo,
  • Minh-Hieu Do,
  • Khanh T. Q. Le,
  • Ngoc-Thac Pham,
  • Huong-Giang Le,
  • Ky-Phat Nguyen,
  • Phan Nguyen,
  • Thanh-Huy Nguyen,
  • Phat K. Huynh,
  • Minh H. N. Le,
  • Min Xu,
  • Nguyen Quoc Khanh Le

摘要

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.