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

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Abstract

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.

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Funding

This work was supported by the National Natural Science Foundation of China under Grants 62272288, 61972451, 61902230 and U22A2041, and the Shenzhen Science and Technology Program under Grant KQTD20200820113106007.

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Authors and Affiliations

  1. School of Computer Science, Shaanxi Normal University, Xi’an, 710119, China

    Lin Guo & Xiujuan Lei

  2. College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China

    Ming Chen

  3. Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology Chinese Academy of Sciences, Shenzhen, 518055, China

    Yi Pan

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  1. Lin Guo
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Correspondence to Xiujuan Lei.

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Guo, L., Lei, X., Chen, M. et al. MSResG: Using GAE and Residual GCN to Predict Drug–Drug Interactions Based on Multi-source Drug Features. Interdiscip Sci Comput Life Sci 15, 171–188 (2023). https://doi.org/10.1007/s12539-023-00550-6

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