Highlight: It was proven that renal injury in Corona Virus Disease 2019 (COVID-19) patients has been a real concern. There are three possible mechanisms of renal injury in COVID-19 infection: direct viral mediated, prolonged cytokine storm mediated, and drug-induced renal injury. There is no targeted drug therapy in the treatment of renal injury, while Chinese Herbal Medicine can be a potential solution. Huang Qi, Fu Ling, Di Huang, Shan Yao and Bai Zhu are the core herbs in Chinese Herbal Medicine to treat renal injury. Chinese Herbal Medicine may act on PTGS2, PTGS1, IL6 and CASP3, etc., to prevent the pharmacological and non-pharmacological COVID-19 related renal injury.

The advent of large-scale biomedical data and computational algorithms provides new opportunities for drug repurposing and discovery. It is of great interest to find an appropriate data representation and modeling method to facilitate these studies. The anatomical therapeutic chemical (ATC) classification system, proposed by the World Health Organization (WHO), is an essential source of information for drug repurposing and discovery. Besides, computational methods are applied to predict drug ATC classification. We conducted a systematic review of ATC computational prediction studies and revealed the differences in data sets, data representation, algorithm approaches, and evaluation metrics. We then proposed a deep fusion learning (DFL) framework to optimize the ATC prediction model, namely DeepATC. The methods based on graph convolutional network, inferring biological network and multimodel attentive fusion network were applied in DeepATC to extract the molecular topological information and low-dimensional representation from the molecular graph and heterogeneous biological networks. The results indicated that DeepATC achieved superior model performance with area under the curve (AUC) value at 0.968. Furthermore, the DFL framework was performed for the transcriptome data–based ATC prediction, as well as another independent task that is significantly relevant to drug discovery, namely drug–target interaction. The DFL-based model achieved excellent performance in the above-extended validation task, suggesting that the idea of aggregating the heterogeneous biological network and node’s (molecule or protein) self-topological features will bring inspiration for broader drug repurposing and discovery research.

No abstract