MS-ADR: predicting drug–drug adverse reactions base on multi-source heterogeneous convolutional signed network

Concomitant drugs therapy is effective and inevitable for most patients. However, drug–drug adverse reactions (DDADRs) caused by combination drugs may bring about medical malpractices. Therefore, the accurate prediction of DDADRs is beneficial to human health and pharmaceutical researches. Currently, existing machine learning-based methods focus on a single feature. In this paper, we propose a novel method, MS-ADR, to extract multi-source features and predict DDADRs. First, we obtain four biomedical views by using four drug signed networks, respectively, namely enzyme view, indication view, side effect view, and transporter view. Then, different biomedical views are fed into graph convolutional neural networks (GCN) to extract multi-source features. Second, we propose an attention block to merge multi-source features from different biomedical views. Finally, a reconstructed drug–drug adverse reaction network is embedded to predict DDADR. The experiment shows that MS-ADR achieves better performance compared with other start-of-the-art baselines.