# In silico based exploration of natural and synthetic antidiabetic compounds: A comprehensive review of computational approaches

## TL;DR

This paper reviews how combining computer modeling and lab experiments helps find new diabetes treatments from natural and synthetic sources.

## Contribution

The paper introduces an integrated pipeline combining computational screening and biological validation for antidiabetic drug discovery.

## Key findings

- Integrated computational and experimental methods identified compounds with strong target binding and enzyme inhibition.
- In vivo tests showed significant glucose reduction and improved insulin response in diabetic animal models.
- ADMET analysis confirmed the drug-likeness and safety of the identified compounds.

## Abstract

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.

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.

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.

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.