KGDB-DDI:Knowledge graph-based drug background data fusion model for drug-drug interaction prediction.

Abstract

The combined use of multiple drugs has been widely used in the treatment of many diseases, so accurate prediction of drug-drug interaction (DDI) plays a critical role in ensuring the health of patients' medication and improving the efficiency of drug development. However, for many existing DDI prediction methods (KGNN, TIGER, DANN-DDI, etc.), They fail to fully utilize the potential information in drug background data, limiting the predictive power of unknown drug interactions. Therefore, this paper proposes a drug interaction model based on knowledge graph and drug background data (KGDB-DDI), which can effectively integrate knowledge graph and drug background data and use the integrated data to predict DDI. We evaluated the model on DrugBank and KEGG datasets, and the AUC and AUPR of the KGDB-DDI model on the DrugBank dataset reached 0.9952. The experimental results show that the KGDB-DDI model outperforms the classic and other state-of-the-art models and has excellent prediction performance. In addition, multi-angle ablation studies further demonstrate its effectiveness and its potential in predicting drug interactions.