Knowledge Mining of Interactions between Drugs from the Extensive Literature with a Novel Graph-Convolutional-Network-Based Method

Abstract

Interactions between drugs can occur when two or more drugs are used for the same patient. This may result in changes in the drug’s pharmacological activity, some of which are beneficial and some of which are harmful. Thus, identifying possible drug–drug interactions (DDIs) has always been a crucial research topic in the field of clinical pharmacology. As clinical trials are time-consuming and expensive, current approaches for predicting DDIs are mainly based on knowledge mining from the literature using computational methods. However, since the literature contain a large amount of unrelated information, the task of identifying drug interactions with high confidence has become challenging. Thus, here, we present a novel graph-convolutional-network-based method called DDINN to detect potential DDIs. Combining cBiLSTM, graph convolutional networks and weight-rebalanced dependency matrix, DDINN is able to extract both contexture and syntactic information efficiently from the extensive biomedical literature. At last, we compare our DDINN with some other state-of-the-art models, and it is proved that our work is more effective. In addition, the ablation experiments demonstrate the advantages of DDINN’s optimization techniques as well.