Computational investigation of single herbal drugs in Ayurveda for diabetes and obesity using knowledge graph and network pharmacology
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| Natura: | Preprint |
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2026
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| author | Sil, Priyotosh Tiwari, Rahul Garisetti, Vasavi Baskaran, Shanmuga Priya Dhanaseelan, Fenita Hephzibah Srivastava, Smita Samal, Areejit |
| author_facet | Sil, Priyotosh Tiwari, Rahul Garisetti, Vasavi Baskaran, Shanmuga Priya Dhanaseelan, Fenita Hephzibah Srivastava, Smita Samal, Areejit |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_21643 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Computational investigation of single herbal drugs in Ayurveda for diabetes and obesity using knowledge graph and network pharmacology Sil, Priyotosh Tiwari, Rahul Garisetti, Vasavi Baskaran, Shanmuga Priya Dhanaseelan, Fenita Hephzibah Srivastava, Smita Samal, Areejit Molecular Networks Subcellular Processes 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. |
| title | Computational investigation of single herbal drugs in Ayurveda for diabetes and obesity using knowledge graph and network pharmacology |
| topic | Molecular Networks Subcellular Processes |
| url | https://arxiv.org/abs/2601.21643 |