In silico based exploration of natural and synthetic antidiabetic compounds: A comprehensive review of computational approaches
Abstract
Background and purpose: Diabetes mellitus type 2 is a global health issue marked by hyperglycemia and metabolic dysfunction. Despite progress, discovering safe and effective antidiabetic agents remains crucial. This review highlights integrated In Silico, In Vitro, and in vivo methods for identifying novel antidiabetic compounds from natural and synthetic origins. Experimental approach: Computational tools including molecular docking, molecular dynamics, and ADMET prediction identified inhibitors targeting DPP-IV, α-glucosidase, and PPAR. Promising compounds underwent in vitro enzymatic and cellular assays, followed by in vivo efficacy tests in diabetic animal models assessing glucose levels, biochemical markers, and tissue histopathology. Key results: Integrated computational and experimental approaches effectively pinpointed compounds with strong target binding, enzyme inhibition, and positive cellular effects. In vivo data showed significant glucose reduction, enhanced insulin response, and pancreatic protection. ADMET analysis further supported their drug-likeness and safety profiles. Conclusion: Combining computational screening with biological validations forms a cost-effective pipeline for antidiabetic drug discovery. Multi-disciplinary integration increases lead identification success, guiding future refinement of in silico models and expanded in vivo studies to accelerate novel diabetes therapeutic development.
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