Pharmacophore-Based 3D-QSAR and Molecular Docking Studies of Novel 1,3,4-Thiadiazole Derivatives as Potential α-Glucosidase Inhibitors

Integrated Pharmacophore-Guided Atom-Based 3D-QSAR, Molecular Docking, Virtual Screening, and ADMET Analysis for the Identification of Novel 1,3,4-Thiadiazole-Based Aldose Reductase Inhibitors

The results suggest that the scaffold 1,3,4-thiadiazole is a promising structural template for designing new generation aldose reductase inhibitors for diabetic complications.

Multi-target analysis of 1,3,4-thiadiazolyl-thiazolidine-2,4-dione derivatives for type 2 diabetes mellitus: an integrated network pharmacology and molecular simulation study

Design of novel derivatives of 1,3,4-thiadiazole against the α-amylase enzyme using 3D-QSAR, ADMET evaluation, docking analysis, molecular dynamic simulations and MM-PBSA approaches

Findings suggest that PR1 and PR2 are promising candidates for advanced antidiabetic drug development, exhibiting predicted enhanced inhibitory activities and favorable pharmacokinetic and toxicological profiles.

Design, synthesis, and computational assessment of bis‑oxadiazole ligands targeting α-amylase and α-glucosidase: DFT, docking, and ADME studies.

AIM Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder characterized by persistent hyperglycemia, mainly due to postprandial glucose elevation. Inhibition of carbohydrate-hydrolyzing enzymes such as α-glucosidase and α-amylase is an effective strategy for its management. This study aimed to design, synthesize, characterize, and evaluate a series of bis-oxadiazole derivatives as potential anti-diabetic agents. METHODS The target compounds were synthesized via multistep organic synthesis and structurally confirmed using spectroscopic techniques including FTIR, NMR, and HRMS. The in-vitro anti-diabetic potential was assessed through α-glucosidase and α-amylase inhibition assays. In addition, molecular docking studies were performed to investigate binding interactions and conformational stability within the active sites of both enzymes. Absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiling was also carried out to evaluate drug-likeness and pharmacokinetic properties. RESULTS Among the synthesized derivatives, compound 10 exhibited the most potent activity, showing IC50 values of 1.80 and 2.10 μM against α-amylase and α-glucosidase, respectively, in comparison with the positive control acarbose (IC50 = 5.50 and 5.60 μM, respectively). Compound 10 exhibited the most potent inhibitory activity against both enzymes, showing strong binding affinity and key hydrogen-bonding and hydrophobic interactions in docking studies. The spectroscopic characterization confirmed the successful formation of all target compounds. ADMET analysis indicated favorable pharmacokinetic and toxicity profiles for the most active derivatives. CONCLUSION The combined experimental and computational results demonstrate significant structure-activity relationships within the bis-oxadiazole scaffold, identifying compound 10 as a promising lead candidate for further optimization as a dual enzyme inhibitor for diabetes management.

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

Structure-Based Virtual Screening and De Novo Design Guided by 3D-QSAR Triage for the Identification of Nanomolar STK3 Inhibitors

Serine/threonine kinase 3 (STK3) is a core kinase in the Hippo signaling pathway that regulates cell proliferation, differentiation, and apoptosis. Dysregulation of this pathway is linked to various cancers and immune disorders, emphasizing STK3 as a promising therapeutic target for diseases that involve abnormal cell growth. Despite their therapeutic relevance, only a limited number of potent STK3 inhibitors have been reported, likely due to the difficulty in designing molecules that can bind tightly to the ATP-binding site of STK3. Using the 2-(2-amino-5-phenylpyrimidin-4-yl)-5-methoxyphenol (APPP) scaffold identified through virtual screening, we discovered a series of novel, potent STK3 inhibitors with half-maximal inhibitory concentration (IC50) values as low as 3.52 nM. This was made possible through an integrated molecular design strategy that combined structure-based de novo design with a predictive 3D-QSAR model to estimate IC50 values and prioritize compounds for synthesis. This approach improved the prioritization of compounds for synthesis by enhancing binding energy discrimination beyond the limitations of scoring functions in de novo design. Strategic derivatization at four defined positions on the APPP core enhanced interactions in the ATP-binding site of STK3, resulting in 18 of the 25 synthesized derivatives exhibiting IC50 values below 100 nM. These findings are particularly significant given the scarcity of known STK3 inhibitors with comparable potency, while also indicating that 3D-QSAR modeling contributed as a complementary triage tool rather than a standalone driver of lead discovery. This advancement stems from the use of quantum-mechanically derived 3D structural alignments and the incorporation of electrostatic potential distributions as quantitative molecular descriptors. Taken together, this study establishes a robust design paradigm for the rapid development of highly potent kinase inhibitors through structure-based computational strategies.