Research on drug-drug interaction prediction using capsule neural network based on self-attention mechanism

Experimental results on two benchmark datasets show that the ACaps-DDI model outperforms six other classification models across seven evaluation metrics, demonstrating its strong predictive performance and generalization ability. Ablation studies further confirm the effectiveness of individual components within the ACaps-DDI architecture. Finally, case studies involving three drugs (cannabidiol, torasemide, and cyclophosphamide) validate the model's ability to predict previously unknown drug interactions. In conclusion, the ACaps-DDI model exhibits high predictive accuracy for known drugs and demonstrates promising predictive capability for unseen drugs, highlighting its practical significance for clinical research on drug interactions.