Published Paper Details:

Type 2 diabetes mellitus (T2DM) is a widespread metabolic disorder characterized by insulin resistance and insufficient insulin production, leading to hyperglycemia. With the increasing prevalence of T2DM worldwide, there is an urgent need for the development of novel and effective therapeutic agents. In silico drug design techniques have emerged as powerful tools in the drug discovery pipeline, facilitating the identification and optimization of potential drug candidates. This review provides a comprehensive overview of in silico methods employed in the discovery of anti-diabetic agents targeting various proteins and pathways implicated in T2DM pathogenesis. We discuss structure-based drug design (SBDD) approaches, including molecular docking, virtual screening, and pharmacophore modeling, as well as ligand-based drug design (LBDD) techniques such as quantitative structure-activity relationship (QSAR) modeling and similarity searching. Additionally, we highlight the application of machine learning and artificial intelligence in drug design for T2DM. Furthermore, we examine the integration of in silico methods with experimental techniques and the challenges associated with in silico drug design for T2DM. Finally, we provide insights into future perspectives and the potential impact of in silico approaches on the development of novel anti-diabetic therapies.