A multi-task graph convolutional network modeling of drug-drug interactions and synergistic efficacy
Yuanyuan Deng, Song Yu, Lei Deng, Hui Liu, Xuejun Liu, Yi Ping Luo · 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · 2021
Identification of drug-drug interaction(DDI) is critical for safer and more effective drug co-prescription. As wetlab screening assays are time-consuming, labor-intensive and expensive, it is highly desired to develop an effective computational method to predict drug-drug interactions. In this work, we aim to predict of drug-drug interactions and synergistic drug combinations by proposing an end-to-end multi-task learning framework based on graph convolutional network (GCN). Precisely, we first convert the drug into a molecular graph, in which vertices represent atoms and edges represent chemical bonds. Next, the r-radius subgraph method is applied to molecular graph so that a series of subgraphs are produced for each drug. Next, the subgraphs are used as the input of the graph convolutional network to learn the embedding vector. Finally, the pairwise drug embeddings learned by GCN are concatenated as input into a fully-connected layer for predicting drug-drug interaction and synergistic effects. We conducted extensive performance evaluations on different data sets, including benchmark DDI data sets and manually collected drug combination data sets, and the results show that our proposed method is significantly better than the newly proposed methods (DeepCCI) and four typical machine learning methods (FFNN, SVM, RF, AdaBoost). In addition, our case study showed that 11 our of top 20 predicted DDIs have been reported by PubMed literature.