KRDQN: An Interpretable Prediction Framework for Adverse Drug Reactions via Knowledge-Graph Reinforced Deep Q-Learning

February 2026 in “ Pharmaceuticals ”

Qiao Ni, Xue Min, Cui Chen … Bin Peng

adverse drug reactions knowledge graphs reinforcement learning sunitinib alopecia biological pathways pharmacovigilance clinical decision-making hair loss drug side effects

Studysummary This study introduced the KRDQN predictive framework, which outperformed existing methods in predicting adverse drug reactions and provided interpretable insights into drug mechanisms, aiding pharmacovigilance and clinical decision-making.

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