ArticleChinese medicine2025
Machine learning-assisted analysis of serum metabolomics and network pharmacology reveals the effective compound from herbal formula against alcoholic liver injury.
Article in Chinese medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Preliminary Prediction of Potential Hepatoprotective Properties of Jujube Extract in Rats Using Metabolomics and Bioinformatics.Foods (Basel, Switzerland) · 2026Article
- Machine learning-guided Huanglian Jiedu decoction targets STING in periodontitis-induced Alzheimer's Disease.NPJ digital medicine · 2026Article
- Network Pharmacology-Driven Sustainability: AI and Multi-Omics Synergy for Drug Discovery in Traditional Chinese Medicine.Pharmaceuticals (Basel, Switzerland) · 2025Review
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16 authors.
Funding
Abstract
backgroundThe popularity of herbal formulas is increasing worldwide. Nevertheless, the effective compound is challenging to identify due to its intricate composition and multiple targets.
methodsAn integration machine learning-assisted approach was established, whereby the particular action mechanism and direct target were obtained through the correlation of compounds, targets, and metabolites. The association between a compound and an action pathway was selected from the shortest path of the "compound-target-pathway-disease" network, which was analyzed using the Floyd-Warshall algorithm. Subsequently, an investigation was conducted into the relationship between metabolites and action pathways, as well as targets, through the analysis of serum metabolomic profiling and the selection of metabolite biomarkers by random forest. In order to accurately identify the direct acting target as well as the most effective compound, the relationship between the compounds and their targets was investigated using a feature-based prediction model conducted by AdaBoost. The binding mode of the effective compound and the direct-acting target was verified by molecular docking, dynamics simulations, and western blotting. In this study, Baiji Wuweizi Granule (BWG) was employed to elucidate the effective compound against alcoholic liver injury (ALD).
resultsBWG exerted an influence on the serum metabolomic, resulting in the identification of seven potential biomarkers. Furthermore, six effective compounds and the PI3K-AKT signalling pathway were identified through a co-analysis with the shortest path from compound to ALD in the "compound-target-pathway-disease" network. It was postulated that the effective compounds would bind with key targets from the PI3K-AKT signaling pathway, as indicated by the prediction model of compound-target interaction (R
conclusionsOur findings demonstrate the value of a machine learning-assisted approach in identifying the key compound, target and pathway that underpin the efficacy of an herbal formula. This provides a foundation for future clinical and fundamental research.
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