Computational investigation of single herbal drugs in Ayurveda for diabetes and obesity using knowledge graph and network pharmacology

Metabolic diseases such as type 2 diabetes and obesity represent a rapidly escalating global health burden, yet current therapeutic strategies largely target isolated symptoms or single molecular pathways. To this end, we developed an integrated computational pipeline leveraging knowledge graph, pathway analysis and network pharmacology to elucidate the multi-target mechanisms of Ayurvedic Single Herbal Drugs (SHDs). SHDs associated with diabetes and obesity were curated from the Ayurvedic Pharmacopoeia of India, followed by phytochemical identification using IMPPAT database, yielding a shortlist of 11 SHDs and their 188 phytochemicals after drug-likeness and bioavailability filtering. Subsequently, molecular targets of the phytochemicals in SHDs, disease-associated genes and therapeutic targets of FDA-approved drugs, were curated via integration of data from several databases. Pathway enrichment analysis revealed significant functional overlap between SHD-associated and disease-associated pathways. All curated data were embedded into a Neo4j-based knowledge graph, enabling SHD-disease intersection analysis that prioritized key disease-relevant targets, including PTPN1, GLP1R, and DPP4. Also, the SHD-Target-FDA-approved drug profile elucidated the molecular and mechanistic aspects of the SHDs as a phytochemical cocktail, and is in alignment with the clinically studied synergistic FDA-approved drug combinations. Network pharmacology based protein-protein interaction analysis identified PPARG as another central regulator. Using a quantitative framework, we identified phytochemical pairs within SHDs, which were structurally dissimilar and target-wise distinct, yet acted on shared or different disease-associated pathways, indicating complementary and potentially synergistic interactions. Molecular docking analysis of two selected druggable targets identified putative lead phytochemicals.

💡 Research Summary

This study presents an integrated computational pipeline that bridges traditional Ayurvedic knowledge with modern systems biology to elucidate the multi‑target mechanisms of single herbal drugs (SHDs) for type‑2 diabetes and obesity. Starting from the Ayurvedic Pharmacopoeia of India, the authors curated 11 SHDs explicitly indicated for both conditions. Phytochemical constituents were retrieved from the IMPPAT database, supplemented with literature‑derived compounds, and filtered for drug‑likeness (Lipinski’s rule) and oral bioavailability (SwissADME score ≥ 0.5), resulting in 188 candidate molecules. Human protein targets of these phytochemicals were assembled from NPASS, BindingDB, and ChEMBL, while disease‑associated genes were collected from DisGeNET, DISEASES, CTD, GeneCards, and other high‑confidence sources. In parallel, therapeutic targets of FDA‑approved anti‑diabetic and anti‑obesity drugs were curated from DrugBank, TTD, and DrugCentral. All entities—herbs, phytochemicals, protein targets, disease genes, and FDA drug targets—were integrated into a Neo4j knowledge graph, enabling systematic SHD‑disease intersection analysis. Pathway enrichment revealed substantial overlap between SHD‑associated and disease‑associated pathways, highlighting key nodes such as PTPN1, GLP1R, and DPP4. Protein‑protein interaction analysis identified PPARG as an additional central hub. A quantitative framework assessed complementarity among phytochemicals: structurally dissimilar pairs were often target‑wise distinct yet converged on shared or complementary disease pathways, suggesting synergistic potential. Molecular docking against two druggable targets (DPP4 and PPARG) identified promising lead compounds—Chitraline, Isovitexin, and Pakistanine for DPP4; Sulfurein, Sesamin, and Pterosupin for PPARG. The pipeline—data acquisition → drug‑likeness filtering → target mapping → knowledge‑graph construction → pathway/network analysis → complementarity scoring → docking validation—is reproducible and scalable to other disease contexts. By integrating heterogeneous biomedical data into a unified graph and applying network pharmacology, the work provides a robust framework for mechanistic dissection of Ayurvedic herbs, supporting multi‑target drug discovery, rational design of phytochemical combinations, and the broader translation of traditional medicine into modern therapeutic development.