Computational identification of multitarget antidiabetic lichen metabolites from the Nilgiris using integrated in silico and in vitro models

Type 2 diabetes mellitus (T2DM) is a multifactorial metabolic disorder requiring therapeutic strategies capable of modulating multiple biochemical pathways. In silico approaches provide ethical, predictive, and cost-effective platforms for prioritizing bioactive compounds before extensive experimental validation. In this study, secondary metabolites from South Indian lichens collected in the Nilgiri biosphere were computationally screened as multitarget antidiabetic candidates, followed by focused in vitro validation. Putative lichen metabolites identified by GC–MS were evaluated for drug-likeness, pharmacokinetic properties, and toxicity profiles using in silico ADMET prediction. Molecular docking was performed against key metabolic targets involved in carbohydrate digestion, lipid metabolism, and insulin signaling regulation, including α-amylase, α-glucosidase, pancreatic lipase, and protein tyrosine phosphatase 1B (PTP1B). Docking protocol reliability was confirmed through redocking and RMSD analysis. Diospyrol, lecanoric acid, and O-(4-biphenylylcarbonyl) benzoic acid consistently exhibited favorable predicted binding affinities across multiple targets. Computationally prioritized extracts were subsequently assessed using in vitro antioxidant assays, enzyme inhibition studies, cytocompatibility analysis in HepG2 and Caco-2 cells, and glucose uptake evaluation in L6 myotubes. Selected extracts demonstrated screening-level inhibition of metabolic enzymes, maintained >85% cell viability at non-cytotoxic concentrations, and significantly enhanced cellular glucose uptake. Collectively, this study establishes an in silico–driven framework for multitarget antidiabetic screening of lichen-derived metabolites, providing early-stage computational prioritization supported by biological validation. The findings support further mechanistic and simulation-based investigations to advance these metabolites toward translational relevance.