Background: Based on network pharmacology and molecular docking technology, the pharmacological mechanism of Taohong Siwu Decoction (THSWD) in the treatment of chronic glomerulonephritis (CGN) was analyzed to provide a theoretical basis for the subsequent development of new drugs and the clinical application of Traditional Chinese Medicine (TCM). Methods: Active ingredients of drugs and disease target genes were obtained from Traditional Chinese Medicine Database and Analysis Platform (TCMSP) database and GeneCards database. The “drug component target” network of THSWD was constructed using Cytoscape version 3.8.2 software. The protein interaction was analyzed using STRING platform, the protein–protein interaction (PPI) network was constructed, and the potential protein function modules in the network were mined. Metascape platform was used to analyze “drug component target” and its biological processes and pathways. The clusterProfiler R package was called to perform kyoto encyclopedia of genes and genomes (KEGG) pathway and gene ontology (GO) function enrichment analysis on CGN-related targets regulated by THSWD. Molecular docking verification was performed by AutoDock Vina software. Results: THSWD has 205 target genes and 45 active components, 104 of which are cross with the CGN inflammatory gene. Its main active ingredients, stigmasterol, kaempferol, and sitosterol, have positive relationships with the inflammatory targets of CGN, tumor necrosis factor (TNF), IL-6, AKT1, and MAPK14. THSWD modulates the biological pathway of CGN and mainly acts on TNF-α signal pathway, interleukin-17 signal pathway, etc., whose main functions are response to lipid sugar, heme binding, G protein-coupled amine receptor activity, etc. The results of molecular docking showed that the main active compounds could bind to the core targets and showed good affinity. Conclusion: The molecular mechanism of THSWD in the treatment of CGN from the perspective of network pharmacology are components such as beta-sitosterol, kaempferol, and quercetin and key action targets such as TNF, IL-6, AKT1 protein kinase, and MAPK14 protein kinase play a synergistic role in autoimmune, infection, and inflammatory response-related pathways.

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Background: In this study, we aimed to develop a new end-to-end learning model called Graph-Drug–Target Interaction (DTI), which integrates various types of information in the heterogeneous network data, and to explore automatic learning of the topology-maintaining representations of drugs and targets, thereby effectively contributing to the prediction of DTI. Precise predictions of DTI can guide drug discovery and development. Most machine learning algorithms integrate multiple data sources and combine them with common embedding methods. However, the relationship between the drugs and target proteins is not well reported. Although some existing studies have used heterogeneous network graphs for DTI prediction, there are many limitations in the neighborhood information between the nodes in the heterogeneous network graphs. Method: Our study consists of three major components. First, various drugs and target proteins were integrated, and a heterogeneous network was established based on a series of data sets. Second, the graph neural networks-inspired graph auto-encoding method was used to extract high-order structural information from the heterogeneous networks, thereby revealing the description of nodes (drugs and proteins) and their topological neighbors. Finally, potential DTI prediction was made, and the obtained samples were sent to the classifier for secondary classification. Results: The performance of Graph-DTI and all baseline methods was evaluated using the sums of the area under the precision-recall curve (AUPR) and the area under the receiver operating characteristic curve (AUC). The results indicated that Graph-DTI outperformed the baseline methods in both performance results. Conclusion: Compared with other baseline DTI prediction methods, the results showed that Graph-DTI had better prediction performance. Additionally, in this study, we effectively classified drugs corresponding to different targets and vice versa. The above findings showed that Graph-DTI provided a powerful tool for drug research, development, and repositioning. Graph-DTI can serve as a drug development and repositioning tool more effectively than previous studies that did not use heterogeneous network graph embedding.

Background Accurate prediction of drug-target interactions (DTIs) can guide the drug discovery process and thus facilitate drug development. Most existing computational models for machine learning tend to focus on integrating multiple data sources and combining them with popular embedding methods. However, researchers have paid less attention to the correlation between drugs and target proteins. In addition, recent studies have employed heterogeneous network graphs for DTI prediction, but there are limitations in obtaining rich neighborhood information among nodes in heterogeneous network graphs. Results Inspired by recent years of graph embedding and knowledge representation learning, we develop a new end-to-end learning model, called Graph-DTI, which integrates various information from heterogeneous network data and automatically learns topology-preserving representations of drugs and targets to facilitate DTI prediction. Our framework consists of three main building blocks. First, we integrate multiple data sources of drugs and target proteins and build a heterogeneous network from a collection of datasets. Second, the heterogeneous network is formed by extracting higher-order structural information using a GCN-inspired graph autoencoder to learn the nodes (drugs, proteins) and their topological neighborhood representations. The last part is to predict the potential DTIs and then send the trained samples to the classifier for binary classification. Conclusions The substantial improvement in prediction performance compared to other baseline DTI prediction methods demonstrates the superior predictive power of Graph-DTI. Moreover, the proposed framework has been successful in ranking drugs corresponding to different targets and vice versa. All these results suggest that Graph-DTI can provide a powerful tool for drug research, development and repositioning.