Computational screening of 1-(2-methylphenyl) ethan-1-one derivatives for multi-target modulation of insulin signal transduction and glucose metabolism in diabetes mellitus

Background: The multifactorial nature of type 2 diabetes mellitus necessitates therapeutic strategies capable of modulating multiple biological targets simultaneously. Proteins such as protein tyrosine phosphatase 1B (PTP1B), dipeptidyl peptidase-4 (DPP-4), alpha-glucosidase, and aldose reductase play central roles in insulin signalling, glucose metabolism, and diabetic complications. Objective: This study evaluated the predicted multi-target binding potential of selected 1-(2-methylphenyl)ethan-1-one derivatives using a structure-based computational approach. Methods: Five 1-(2-methylphenyl) ethan-1-one derivatives were screened against PTP1B, DPP-4, alpha-glucosidase, and aldose reductase using molecular docking. Binding affinities and interaction profiles were analysed, followed by in silico drug-likeness and ADMET prediction to assess pharmacokinetic suitability. Results: All compounds produced docking poses across the four targets, with AutoDock Vina scores ranging from −5.2 to −9.8 kcal/mol. L1 showed the most consistently favourable predicted binding, particularly toward α-glucosidase (−9.8 kcal/mol) and aldose reductase (−9.7 kcal/mol), and formed several predicted hydrogen-bonding contacts. L3 also showed favourable multi-target docking scores, with interactions dominated by hydrophobic contacts. In contrast, the parent compound and L2 produced less favourable docking scores. The in silico predictions indicated compliance with Lipinski’s Rule of Five and high gastrointestinal absorption for all compounds; however, L1 produced a borderline Ames-toxicity alert. These computational findings require experimental validation. Conclusion: The docking results identified L1 and L3 as candidates for experimental follow-up because they produced comparatively favourable scores across several diabetes-related targets. However, molecular docking and ADMET predictions alone do not establish enzyme inhibition, antidiabetic activity, efficacy, or safety. Biochemical, cellular, pharmacokinetic, and toxicological studies are required before the compounds can be considered potential antidiabetic agents. Novelty of the Study: To our knowledge, this is the first study to evaluate the predicted binding of substituted 1-(2-methylphenyl) ethan-1-one derivatives to PTP1B, DPP-4, α-glucosidase, and aldose reductase using an integrated computational workflow combining molecular docking, interaction profiling, and ADMET prediction. The findings identify structural features and candidate compounds that warrant experimental investigation and may inform future screening of structurally related food-derived bioactive compounds. Keywords: Computational Screening; 1-(2-methylphenyl)ethan-1-one Derivatives; Multi-Target Modulation; Insulin Signalling; Glucose Metabolism; Diabetes