What question did this study set out to answer?

The study aims to identify molecular targets of Danlou Tablet in treating atherosclerosis through computational methods.

February 12, 2026Naunyn-Schmiedeberg s Archives of Pharmacology0 citationsOpen Access

Bioinformatics approach to identifying molecular targets of Danlou tablet against atherosclerosis: a machine learning pharmacology study

LWLi Wang MZM. Zhang HWHongli Wang

Key Points

The study aims to identify molecular targets of Danlou Tablet in treating atherosclerosis through computational methods.
Integrated network pharmacology and bioinformatics with machine learning techniques.
Retrieved bioactive components from TCMSP and AS-associated targets from various databases.
Conducted GO and KEGG enrichment analyses to explore target functions.
Validated ligand-target interactions using molecular docking and simulation.
Used CETSA and ELISA to confirm target engagement in human umbilical vein endothelial cells.
Identified 138 bioactive components in Danlou Tablet with six key constituents.
Discovered 230 overlapping targets linking Danlou Tablet to atherosclerosis.
Machine learning highlighted HAS2, HSF1, and PTGS1 as potential therapeutic targets.
CETSA confirmed interactions between key compounds and identified targets.
Correlation analysis indicated significant relationships between HAS2 and inflammatory markers.

Abstract

This research aims to identify novel molecular targets and generate mechanistic hypotheses for Danlou Tablet (DLT) in the treatment of atherosclerosis (AS) using an integrative computational framework combining network pharmacology, bioinformatics, single-cell RNA sequencing (scRNA-seq), machine learning, molecular docking, molecular dynamics simulation, and preliminary target engagement validation. Bioactive components and targets of DLT were retrieved from the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP). AS-associated targets were extracted from OMIM, DisGeNET, and GeneCards databases. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses characterized core target functions. Hub targets of DLT against AS were identified through machine learning algorithms. Combined single-cell and bulk transcriptomic profiling revealed AS-associated immune pathway modulation. Molecular docking and dynamics simulations validated ligand-target interactions. Cellular thermal shift assay (CETSA) combined with enzyme-linked immunosorbent assay (ELISA) in human umbilical vein endothelial cells (HUVECs) provided preliminary evidence for target engagement. A total of 138 bioactive DLT components were identified, with six high-degree constituents: quercetin, β-sitosterol, kaempferol, luteolin, naringenin, and formononetin. A total of 230 overlapping targets linked DLT to AS. GO analysis indicated DLT modulates stress response, metabolism, and signal transduction. KEGG analysis highlighted potential anti-AS effects via AGE-RAGE and IL-17 pathways. Machine learning prioritized HSF1, HAS2, PTGS1, NFATC1, and CD40LG as candidate targets. Correlation analysis revealed that HAS2 expression was significantly associated with inflammatory markers (IL6: r = 0.58, P < 0.001; TNF: r = 0.52, P = 0.002). Molecular docking identified HAS2-β-sitosterol (- 10.5 kcal/mol) and PTGS1-quercetin (- 8.8 kcal/mol) as top-binding pairs. CETSA validated β-sitosterol-HAS2 and quercetin-PTGS1 target engagement. This integrative approach proposes HAS2 as a novel candidate target mediating the anti-AS effects of β-sitosterol, providing new molecular hypotheses and a potential target landscape for future experimental validation.

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