Diabetes mellitus is a chronic metabolic ailment characterized by high blood glucose due to defective insulin secretion, insulin action, or both. The growing prevalence of diabetes on a global scale requires the identification and characterization of diabetes causing agents to understand their underlying mechanisms and assist in developing effective therapeutic interventions. In silico assay has emerged as a valuable tool in the field of drug discovery and toxicology, providing a cost effective and quick approach to recognize potential diabetogenic agents. The purpose of this review is to provide insight into different computational methods used in in silico analysis to investigate diabetes causing agents. These methods include molecular docking , virtual screening , pharmacophore modeling, and machine learning algorithms. In addition, we summarize existing knowledge about how these methods can be used to predict the diabetogenic potential of environmental chemicals, drugs and dietary compounds. Lastly, the limitations and future orientations of in silico analysis will be discussed. It underlines the need for integration with experimental validation to strengthen its predictive power and reliability

Keywords:

In silico analysis

Diabetogenic agents

Diabetes mellitus type 2