In silico study of the antidiabetic potential of chlorogenic acid and caffeine from coffea canephora using network pharmacology and molecular docking

Abstract

Background: Type 2 diabetes mellitus (T2DM) is a global health challenge requiring safer and more effective therapies. Coffea canephora contains chlorogenic acid and caffeine, which have reported antidiabetic potential; however, their molecular mechanisms remain unclear.

Objective: To identify candidate targets and explore mechanisms relevant to T2DM for chlorogenic acid and caffeine from Coffea canephora through integrated network pharmacology and molecular docking.

Methods: Drug likeness was assessed using SwissADME. Target prediction, protein interaction network analysis, and functional enrichment analysis were performed to prioritize candidate targets associated with T2DM. Molecular docking was subsequently conducted to assess predicted binding affinities and characterize ligand interactions with the selected proteins. Docking results were compared with those of the respective reference ligands.

Results: Network pharmacology prioritized AKR1B1 for chlorogenic acid and ADORA2B for caffeine. Functional enrichment identified significant associations with insulin signaling, glucagon signaling, carbohydrate metabolism, and inflammatory pathways. Chlorogenic acid yielded a docking score of −8.74 kcal/mol against AKR1B1, compared with −10.10 kcal/mol for lidorestat, and formed favorable interactions with binding site residues. Caffeine yielded a less favorable docking score against ADORA2B (−4.82 kcal/mol) than BAY 60-6583 (−7.08 kcal/mol), with fewer critical interactions.

Conclusion: Chlorogenic acid and caffeine exhibited less favorable docking scores than their respective reference ligands. These findings identify potential interactions with proteins relevant to T2DM but do not establish biological activity or therapeutic efficacy. Experimental validation is required to confirm target engagement and functional effects. This study supports computational approaches for prioritizing natural compounds and generating mechanistic hypotheses for antidiabetic drug discovery.