In Silico Assessment of Gongronema Latifolium Fruit Metabolites as Potential Multi-Target Antidiabetic Leads using Network Pharmacology and Molecular Docking
Background: Type 2 diabetes mellitus (T2DM) is a multifactorial disease for which single-target drugs are often inadequate. The fruit of Gongronema latifolium is used in traditional Nigerian medicine, but its metabolites and molecular targets are poorly described. Methods: Metabolites previously identified from the fruit were screened against Lipinski's rule of five and, in SwissADME, for gastrointestinal absorption and drug-likeness. Their targets were predicted and intersected with T2DM-associated genes, a protein–protein interaction network was analysed to identify hub genes, and Gene Ontology and KEGG enrichment was performed. The retained metabolites were docked against two hub targets, AKT1 and GAPDH. Results: Of 251 metabolites that satisfied Lipinski's rule of five, 241 were retained after SwissADME screening. A total of 1561 targets were identified for the G. latifolium fruit compounds and 6,392 genes were associated with type 2 diabetes mellitus, of which 1,028 overlapped and were taken forward as common targets. Network analysis identified 25 hub genes, led by GAPDH, AKT1, TP53, INS and TNF. The best docking score for AKT1 was −11.9 kcal/mol, with −9.4 kcal/mol being the score for the co-crystallised ligand; for GAPDH, it was −9.7 kcal/mol, while the co-crystallised ligand had a score of −7.0 kcal/mol. Twenty metabolites outscored both references, and C20 and C513 ranked highly for both targets. Conclusion: Metabolites of G. latifolium fruit are predicted to engage several T2DM-relevant targets, notably insulin-signalling and inflammatory nodes, supporting the traditional use of the fruit and prioritising candidates for in vitro and in vivo validation.
Authors
- Gideon Ampoma Gyebi (ORCID: https://orcid.org/0000-0002-1945-1739)
- Chinenye Florence Ogah
- Victoria Temitope Makanjuola
Institutions
- Bingham University (NG)
Publication Details
- Journal
- FUDMA Journal of Sciences
- Published
- 2026-11-09
- DOI
- https://doi.org/10.33003/fjs-2026-1024-6321
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00