MSResG: Using GAE and Residual GCN to Predict Drug–Drug Interactions Based on Multi-source Drug Features

• Published in: Interdisciplinary sciences : computational life sciences Vol. 15; no. 2; pp. 171 - 188
• Main Authors: Guo, Lin, Lei, Xiujuan, Chen, Ming, Pan, Yi
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.06.2023; Springer Nature B.V
• Subjects: Biomedical and Life Sciences; Coders; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Appl. in Life Sciences; Databases, Factual; Deep learning; Drug interaction; Drug Interactions; Drugs; Experimental methods; Graph neural networks; Health Sciences; Humans; Life Sciences; Mathematical and Computational Physics; Medicine; Original Research Article; Research Design; Similarity; Statistics for Life Sciences; Theoretical; Theoretical and Computational Chemistry; Graph auto-encoder; Heterogeneous network; Residual graph convolutional network; Drug–drug interactions
• ISSN: 1913-2751; 1867-1462; 1867-1462
• DOI: 10.1007/s12539-023-00550-6

Drug–drug interaction refers to taking the two drugs may produce certain reaction which may be a threat to patients’ health, or enhance the efficacy helpful for medical work. Therefore, it is necessary to study and predict it. In fact, traditional experimental methods can be used for drug–drug interaction prediction, but they are time-consuming and costly, so we prefer to use more accurate and convenient calculation methods to predict the unknown drug–drug interaction. In this paper, we proposed a deep learning framework called MSResG that considers multi-sources features of drugs and combines them with Graph Auto-Encoder to predicting. Firstly, the model obtains four feature representations of drugs from the database, namely, chemical substructure, target, pathway and enzyme, and then calculates the Jaccard similarity of the drugs. To balance different drug features, we perform similarity integration by finding the mean value. Then we will be comprehensive similarity network combined with drug interaction network, and encodes and decodes it using the graph auto-encoder based on residual graph convolution network. Encoding is to learn the potential feature vectors of drugs, which contain similar information and interaction information. Decoding is to reconstruct the network to predict unknown drug-drug interaction. The experimental results show that our model has advanced performance and is superior to other existing advanced methods. Case study also shows that MSResG has practical significance. Graphical Abstract