Trustworthy Drug-Drug Interaction Prediction Model via Weighted Aggregating Conformal Prediction

Drug-Drug Interaction (DDI) prediction is crucial for ensuring the safety and efficacy of combination drug therapies. However, most existing methods lack uncertainty estimation, limiting their reliability and practical adoption. While conformal prediction (CP) offers guaranteed coverage in classification tasks, its direct application to DDI prediction faces challenges in achieving valid marginal coverage and efficient prediction set sizes. To overcome these limitations, this work introduces WeightedCPDDI, the first conformal prediction framework tailored for DDI tasks. WeightedCPDDI incorporates a novel weighting mechanism to achieve user-specific coverage and enhance prediction efficiency. Evaluation across transductive and inductive settings demonstrates WeightedCPDDI's strong performance. In the transductive setting, it achieves target coverage with smaller or comparable prediction sets, indicating improved calibration and efficiency. In the inductive setting, WeightedCPDDI remains competitive with or better than state-of-the-art methods in both seen-unseen and unseen-unseen scenarios, with the largest efficiency gains in the challenging unseen-unseen drug-pair setting, where it reduces prediction set sizes relative to the strongest baselines while maintaining the target coverage rate. Its strong performance across various backbone models highlights its generalizability and practical reliability for DDI prediction under uncertainty.