Interpretable Graph-Embedding Framework Based on Joint Feature Similarity for Drug–Drug Interaction Prediction

Deep learning methods have been extensively used for drug–drug interaction (DDI) prediction, aiding the development of effective and safe combination therapies. Most studies focus on either the internal molecular structure or external contextual information of individual drugs to improve feature diversity and validity. However, the latent similarities between drug pairs, which are essential for accurate predictions, have largely been overlooked. Therefore, we propose an interpretable predictive approach for graph embedding called PINGE, which relies solely on the interaction network of drugs. Specifically, we constrain the joint features of drug pairs to their interactions, allowing those with similar types to achieve cosine similarity. This similarity in direction helps the joint features converge to the same class during prediction. Additionally, each known drug can link to multiple others, enhancing its diversity. Extensive experiments demonstrate that PINGE outperforms current advanced prediction methods on both KEGG and Drugbank datasets, achieving improvements of 0.7% and 2.4% in ACC while providing network structure-based explanations for predictions. Furthermore, PINGE surpasses advanced baselines by 1% and 1.1% in AUC on the human drug–target dataset and HuRI protein–protein interaction dataset, showcasing excellent versatility.