Abstract
Drug-drug interaction (DDI), as a possible result of drug combination treatment, could lead to adverse physiological reactions and increasing mortality rates of patients. Therefore, predicting potential DDI has always been an important and challenging issue in medical health applications. Owing to the extensive pharmacological research, we can get access to various drug-related features for DDI predictions; however, most of the existing works on DDI prediction do not incorporate comprehensive features to analyze the DDI patterns. Despite the high performance that the existing works have achieved, the incomplete and noisy information generated from limited sources usually leads to sub-optimal performance and poor generalization ability on the unknown DDI pairs. In this work, we propose a holistic framework, namely Multi-modality Feature Optimal Fusion for Drug-Drug Interaction Prediction (MOF-DDI), that incorporates the features from multiple data sources to resolve the DDI predictions. Specifically, the proposed model jointly considers DDIs literature descriptions, biomedical knowledge graphs, and drug molecular structures to make the prediction. To overcome the issue induced by directly aggregating features in different modalities, we bring a new insight by mapping the representations learned from different sources to a unified hidden space before the combination. The empirical results show that MOF-DDI achieves a large performance gain on different DDI datasets compared with multiple state-of-the-art baselines, especially under the inductive setting.