TL;DR
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.
Background: Metabolic Disorder Type 2 Diabetes mellitus (T2DM) is a chronic disease that involves hyperglycemia due to insulin resistance and impaired insulin secretion. α-glucosidase inhibition has been proven to be a therapeutic approach for controlling postprandial hyperglycemia and is a well-established treatment for T2DM. 1,3,4-thiadiazole is a promising pharmacophore among heterocyclic scaffolds due to its broad range of biological activities, including α-glucosidase inhibition. Purpose: The present study was designed to identify and optimize novel 1,3,4-thiadiazole derivatives as α-glucosidase inhibitors using an integrated computer-aided drug design (CADD) approach that includes pharmacophore modeling, 3D-QSAR, molecular docking, optimization of R-groups, and ADMET prediction. Methods: A total of 34 reported 1,3,4-thiadiazole derivatives were analyzed to build an optimal pharmacophore model (AADHR₃) as well as atom-based (R² = 0.799, Q² = 0.822) and Gaussian field-based 3D-QSAR models (R² = 0.967, Q² = 0.682). The structurally optimized lead compounds were then identified by molecular docking, R-group optimization, and ADMET prediction. Results: The analysis of the contour map showed that bulky hydrophobic groups in the R₁ position and electron-withdrawing groups in the R₂ position were beneficial for enhancing α-glucosidase inhibitory activity. The docking score of five compounds was comparable or better than that of acarbose (−6.260 kcal/mol), with the best score being compound 25 (−6.686 kcal/mol). The designed derivatives (DM1 (−7.625 kcal/mol), DM2 (−7.418 kcal/mol), and DM3 (−7.284 kcal/mol)) showed better binding abilities and favorable interactions with Arg281, Asp282, Asp404, Asp518, Asp616, His674, and Phe525, in addition to promising ADMET properties. Conclusion: Pharmacophore analysis, 3D-QSAR, molecular docking, SAR, and ADMET studies indicated that the 1,3,4-thiadiazole scaffold could be a promising template for designing potent α-glucosidase inhibitors. The optimized derivatives, especially DM1–DM3, are good lead candidates for further development into therapeutic drugs for T2DM.
Background: Chronic diabetic complications develop through the important role of aldose reductase (AR), a key enzyme in the polyol pathway. Clearly, it is important to identify potent AR inhibitors with better PK properties for treatment of diabetes-associated complications. Purpose: The purpose of this study was to discover novel 1,3,4-thiadiazole derivatives as aldose reductase inhibitors using an integrated computational drug discovery approach. Methods: The dataset consisted of 30 reported 1,3,4-thiadiazole derivatives, which were analysed by the pharmacophore modelling, atom-based three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking, structure-activity relationship (SAR) analysis, R-group enumeration, virtual screening and ADMET prediction methods. Using the best pharmacophore model (AHHRR_1), a validated 3D-QSAR model was developed, and 1,419 new derivatives were designed. These compounds were then further optimised for binding interactions and pharmacokinetic parameters with the top-ranked ones, including the designed derivative PD01. Results: The optimised pharmacophore and 3D-QSAR models were able to recognise the crucial structural elements that are essential for AR inhibition. Activity increased with the hydrophobic and electron-withdrawing groups, while the bulky polar groups were responsible for decreased activity. Docking studies showed that compounds 04 (-10.178 kcal/mol), 01 (-10.081 kcal/mol), 02 (-10.050 kcal/mol), 10 (-9.977 kcal/mol), and 11 (-9.672 kcal/mol) exhibited stronger binding than Epalrestat (-8.182 kcal/mol). The highest docking score was obtained for PD01 (-10.605 kcal/mol), which had strong hydrogen-bond, hydrophobic, π-π stacking, π-cation and halogen-bond interactions. The ADMET analysis showed good drug-likeness and good oral absorption. Conclusion: The integrated computational workflow has concluded that PD01 is the most promising lead candidate with excellent binding affinity, a favourable interaction pattern and desirable ADMET properties. 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.
The docking analysis suggest that selected N-(substituted-1,3-benzothiazol-2-yl)benzamide derivatives, particularly Cp1, Cp3, Cp7, Cp10, Cp12, Cp13, and Cp14 were identified as the most promising lead candidates, with significant potential for anticonvulsant activity.
Introduction: The α-amylase enzyme plays a critical role in the digestion of complex carbohydrates. Inhibiting this enzyme offers a promising strategy for improving glucose regulation in diabetic patients. Methods: In this study, a comprehensive computational approach, combining 3D-QSAR modeling, ADMET profiling, molecular docking, molecular dynamics, ligand transport analysis, and retrosynthesis, was used to identify novel ligands with potent inhibitory activity against various indenoquinoxaline-phenylacrylohydrazide hybrids. Results: The optimal 3D-QSAR model, developed using partial least squares (PLS) and Comparative Molecular Similarity Indices Analysis (CoMSIA), demonstrated strong correlation and predictive power (Q2=0.541, R2=0.973, SEE=0.076). ADMET analysis showed that the designed ligands possess acceptable pharmacokinetic and toxicological profiles, supporting their potential for further drug development. Molecular docking revealed that the designed ligands effectively interacted with the active site of α-amylase (PDB ID: 7TAA). Furthermore, molecular dynamics simulations (100 ns) and MM-PBSA free energy calculations confirmed the stability of ligand-enzyme complexes. Ligand transport was further examined using the CaverDock program, tracking the movement of molecules from the enzyme’s active site to its surface. Finally, retrosynthetic analysis was performed to propose feasible synthesis routes for the most active compound. Conclusion: Overall, the findings highlight a promising lead compound for further in vitro and in vivo investigations targeting α-amylase inhibition.
This study investigated the therapeutic potential of novel 1,2,4-triazole- and 1,3,4-oxadiazole-based acetamide derivatives as dual-target inhibitors of urease and α-glucosidase, two enzymes implicated in gastrointestinal disorders and type 2 diabetes, respectively. An integrated approach combining multistep organic synthesis, in vitro biological evaluation, molecular docking, in silico ADME prediction, and density functional theory (DFT) analysis was employed to identify potent lead compounds with favorable pharmacokinetic properties. Starting from benzoic acid, a series of 1,2,4-triazole derivatives (8a-c) and 1,3,4-oxadiazole derivatives (9d-f) bearing substituted acetamide moieties were successfully synthesized. All compounds exhibited inhibitory activity against both target enzymes. Compound 8c was the most potent urease inhibitor (IC₅₀ = 6.14 ± 1.06 µM), while compound 9e showed the strongest α-glucosidase inhibition (IC₅₀ = 27.29 ± 0.41 µM), surpassing the reference inhibitor acarbose (IC₅₀ = 38.25 ± 0.12 µM). Computational analyses supported the experimental findings: ADME predictions indicated favorable drug-like properties, and molecular docking demonstrated strong binding interactions, with compound 8a exhibiting the highest affinity for α-glucosidase (-7.165 kcal/mol) and compound 9e showing the strongest binding to urease (-7.30 kcal/mol). DFT calculations further correlated biological activity with electronic properties, revealing relatively small HOMO-LUMO energy gaps for the most active compounds, 8c (0.14831 a.u.) and 9e (0.14302 a.u.). Collectively, these findings identify compounds 8c and 9e as promising lead scaffolds for the development of next-generation inhibitors of urease and α-glucosidase.
Caspase-1 is a crucial inflammatory cysteine protease that facilitates the maturation of pro-inflammatory cytokines such as interleukin-1β and interleukin-18, making it a significant therapeutic target for inflammatory diseases. However, existing caspase-1 inhibitors often face challenges like toxicity and suboptimal drug-like properties, underscoring the need for new inhibitors. This study employed an integrated computational strategy, combining quantitative structure–activity relationship (QSAR) modeling and application of this validated model to a large natural product database followed by molecular docking, rigorous binding free energy analysis and extended molecular dynamics simulations. Initially, a dataset of 185 caspase-1 inhibitors with experimentally reported pKi values (ranging from 4.05 to 9.24) was used to construct a QSAR model using Partial Least Squares (PLS) regression. The PLS-based QSAR model was developed with 18 descriptors out of 5799 calculated descriptors for each compound and 10 latent variables, demonstrating strong statistical performance with R2 values of 0.870 and 0.838 for the training and test sets, respectively, and leave-one-out cross-validation coefficient Q2LOO and 5-fold cross-validation (Q25-fold) values of 0.819 and 0.814, respectively. Y-randomization tests further confirmed the model’s robustness, as the randomized models exhibited significantly lower statistical parameters than the original model. The validated QSAR model was applied to 276,518 natural products in the LOTUS database. Subsequent molecular docking, Molecular Mechanics/General Born Surface Area (MM/GBSA) scoring, and Pan-Assay INterference Compounds (PAINS) and Chemical Frequent Hitter (ChemFH) filtering identified 14 candidate compounds, which were further evaluated using 300 ns molecular dynamics simulations. Among these, four natural products (LTS0162325, LTS0221286, LTS0016840, and LTS0070407) showed the most stable binding behavior and maintained persistent interactions with key catalytic and substrate-binding residues of caspase-1 in a mimicked physiological condition. Overall, this study highlights natural diterpenoids and coumarin glycosides as promising scaffolds for caspase-1 inhibition and demonstrates that integrating QSAR modeling with structure-based approaches provides an efficient strategy for discovering potential anti-inflammatory drug candidates.
