Prediction of Drug-Drug Interactions Based on Meta-path-based Fusion Mechanism in Heterogeneous Information Network
Xueling Yuan, Weizhong Zhao, Xianjun Shen, Xingpeng Jiang, Tingting He · 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · 2022
Drug-drug interactions (DDIs) refer to the compound effects that may impair the effectiveness of drugs or cause unexpected side effects when two or more drugs are taken together. Therefore, it is very important to accurately predict DDIs for the drug development and drug safety monitoring. Many methods have been proposed to accomplish this task, but the existing methods fail to make full use of the biological knowledge related to DDIs, and do not effectively capture the complex semantics between biological entities related to drugs, resulting in poor performance. In this paper, we propose a novel DDIs prediction framework based on heterogeneous information network (HIN). More specifically, we construct the HIN which combines biological knowledge related to DDIs. In order to capture the complex semantics in HIN, a meta-path-based fusion mechanism is proposed to obtain high-quality drugs’ representations. Moreover, we design a meta-path level attention to combine semantics obtained from meta-paths with different lengths to obtain the final representations of drugs for DDIs prediction. The experimental results demonstrate that the framework performs better than the selected representative baselines on 2410 approved drugs.