QSAR studies on triazole derivatives as sglt inhibitors via CoMFA and CoMSIA

Artificial intelligence in antidiabetic drug discovery: The advances in QSAR and the prediction of α-glucosidase inhibitors

Artificial Intelligence is transforming drug discovery, particularly in the hit identification phase of therapeutic compounds. One tool that has been instrumental in this transformation is Quantitative Structure-Activity Relationship (QSAR) analysis. This computer-aided drug design tool uses machine learning to predict the biological activity of new compounds based on the numerical representation of chemical structures against various biological targets. With diabetes mellitus becoming a significant health challenge in recent times, there is intense research interest in modulating antidiabetic drug targets. α-Glucosidase is an antidiabetic target that has gained attention due to its ability to suppress postprandial hyperglycaemia, a key contributor to diabetic complications. This review explored a detailed approach to developing QSAR models, focusing on strategies for generating input variables (molecular descriptors) and computational approaches ranging from classical machine learning algorithms to modern deep learning algorithms. We also highlighted studies that have used these approaches to develop predictive models for α-glucosidase inhibitors to modulate this critical antidiabetic drug target.

Structural Insights on Hyp-Gly-Containing Peptides as Antiplatelet Compounds through Topomer CoMFA and CoMSIA Analysis

Increasing evidence has shown collagen hydrolysate involves a variety of bioactivities. In our previous study, multiple antiplatelet peptides containing Hyp/Pro-Gly were identified in collagen hydrolysates from Salmo salar and silver carp skin and exhibited anti-thrombosis activity without bleeding risks in vivo. However, the relationship between structure and activity remains unknown. We performed 3D-QSAR studies on 23 Hyp/Pro-Gly-containing peptides in which 13 peptides were reported before. CoMFA, Topomer CoMFA and CoMSIA analyses were used to generate the QSAR models. Topomer CoMFA analysis showed a q² value of 0.710, an r² value of 0.826, an r²_pred value of 0.930, and the results showed that Hyp instead of Pro was more important for improving the antiplatelet activity. CoMSIA analysis showed a q² value of 0.461, an r² value of 0.999, and an r²_pred value of 0.999. Compared with the electrostatic field and hydrogen bond donor field, the steric field, hydrophobic field and hydrogen bond receptor field have great influence on the activity of antiplatelet peptides. The predicted peptide EOGE exhibited antiplatelet activity induced by ADP, and inhibited thrombus formation (300 μmol/kg bw) without bleeding risks. Combined results of these studies indicate that OG-containing peptides had a potential to be developed into an effective specific medical food in the prevention of thrombotic diseases.

A Competition between Hydrogen, Stacking, and Halogen Bonding in N-(4-((3-Methyl-1,4-dioxo-1,4-dihydronaphthalen-2-yl)selanyl)phenyl)acetamide: Structure, Hirshfeld Surface Analysis, 3D Energy Framework Approach, and DFT Calculation

N-(4-((3-Methyl-1,4-dioxo-1,4-dihydronaphthalen-2-yl)selanyl)phenyl)acetamide (5), C₁₉H₁₅NO₃Se, was prepared in two steps from 4,4'-diselanediyldianiline (3) via reduction and subsequent nucleophilic reaction with 2-methyl-3-bromo-1,4-naphthalenedione, followed by acetylation with acetic anhydride. The cytotoxicity was estimated against 158N and 158JP oligodendrocytes and the redox profile was also evaluated using different in vitro assays. The technique of single-crystal X-ray diffraction is used to confirm the structure of compound 5. The enantiopure 5 crystallizes in space group P21 with Flack parameter 0.017 (8), exhibiting a chiral layered absolute structure. Molecular structural studies showed that the crystal structure is foremost stabilized by N-H···O and relatively weak C-H···O contacts between molecules, and additionally stabilized by weak C-H···π and Se···N interactions. Hirshfeld surface analysis is used to quantitatively investigate the noncovalent interactions that stabilize crystal packing. Framework energy diagrams were used to graphically represent the stabilizing interaction energies for crystal packing. The analysis of the energy framework shows that the interactions energies of and C-H···π and C-O···π are primarily dispersive and are the crystal's main important forces. Density functional theory (DFT) calculations were used to determine the compound's stability, chemical reactivity, and other parameters by determining the HOMO-LUMO energy differences. The determination of its optimized surface of the molecular electrostatic potential (MEP) was also carried out. This study was conducted to demonstrate both the electron-rich and electron-poor sites.

New dehydroabietic acid (DHA) derivatives with anticancer activity against HepG2 cancer cell lines as a potential drug targeting EGFR kinase domain. CoMFA study and virtual ligand-based screening

Liver cancer has become the third type of cancer that causes death; this is why the design of new chemotherapeutic drugs against this disease is a major need. With this idea, a series of Dehydroabietic Acid-Based Acylhydrazones have been used to generate a CoMFA model to design new anticancer agents. In this study, we employed a Comparative Molecular Field Analysis studies, we performed those methods on Dehydroabietic Acid-Based Acylhydrazones against HepG2 human cancer cell line. The statistical results are encouraging with Q2 equal to 0.527 and R2 equal to 0.962. The predictive ability of this model was determined using a test set of Dehydroabietic Acid-Based Acylhydrazones that gave an acceptable predictive correlation (R2test) value of 0.614. The developed model guides to design five new molecules with enhanced activity as potential drugs. On the other hand to determine a potential target to these ligands we have established a virtual screening using reverse docking with the most active molecule and 42 antiproliferative targets. Based on the affinity of complex ligand-Target, the intracellular domain of EGFR shows high stability. This suggests that our designed molecules can inhibit the target EGFR which is an important target on targeted therapy of many types of cancer.Communicated by Ramaswamy H. Sarma.

The activity prediction of indole inhibitors against HCV NS5B polymerase

Non-structural viral protein 5B (NS5B) is a viral protein in hepatitis C virus. Although various inhibitors against NS5B have been found, the activity prediction of similar untested inhibitors is still highly desirable. In this respect, the Tchebichef moments (TMs) calculated from the images of molecular structures were regarded as the independent variables while the inhibitory activity (pIC₅₀ ) was the dependent variable, and the predictive model was established by means of stepwise regression. The R-squared of leave-one-out cross-validation (Q² ) for the training set and the R-squared of prediction ( R p 2 ) for external independent test set were 0.919 and 0.927, respectively. The obtained model was also evaluated strictly. Compared with the multivariate curve resolution with alternating least squares (MCR-ALS) and the QSAR approaches derived from the literature, the proposed method is more accurate and reliable. This study not only provides an effective approach to predict the biological activity of RNA replication's inhibitors, but also extends the QSAR modeling technique.

DFT and QSAR investigations of substituent effects in pyrazolooxazine derivatives: Activity prediction

The theoretical investigation of the physico-chemical properties of pyrazolooxazine derivatives, which could exhibit anti-inflammatory activity, has been carried out using DFT computations at the B3LYP/6-311[Formula: see text]G (d, p) level. It appears that both pyrazolooxazin-2-one (1) and pyrazolooxazin-2-thione (2) exhibit thermal stability whereas the electron donating character of the latter is higher than that of the former species. Molecular electrostatic potential surfaces (MESP) have been used in order to determine the activity sites of these species. The HSAB (Hard and Soft Acids and Bases) principle helped to determine the reactivity as well as the effect of substituting groups on the molecules. The stability of these compounds has been analyzed using natural bond orbital analyses NBO. Then, the structure-activity properties of a large series of derivatives were investigated using the HyperChem 8.0.6 software considering the Lipinski’s “rule of five”. The results of QSAR analyses showed that the most promising compound among others, named f1 (a thiophen ring being attached to the nitrogen atom 3 of 1) should be a good candidate for experimental testing.

Development of a broad-specificity antibody-based immunoassay for triazines in ginger and the quantitative structure-activity relationship study of cross-reactive molecules by molecular modeling

In the present study, molecular modeling and principle component analysis (PCA) were used to select appropriate haptens for group detection of triazine herbicides. Four new structures together with three reported triazine derivatives were chosen for the screening of immunizing and coating haptens. A total of 31 triazines coupled with a 3D-QSAR methodology were employed to investigate the relationship between antigen-antibody recognition and molecular structures, the results of which revealed that the antibodies may recognize triazines from the side of molecules with the distinguishing atom and a steric volume matching with the spatial structure of antibodies. Finally, a broad-specificity heterologous immunoassay was developed for determining 10 triazine herbicides in ginger, where the detection limits were 2.5-15.1 μg kg-1 and recoveries were 67.9-102.6%. This study may broaden insight into triazine-antibody interactions and benefit designing novel performance-enhanced antibodies. The developed immunoassay can be further used for triazine detection in other complicated matrices.

Combined HQSAR, topomer CoMFA, homology modeling and docking studies on triazole derivatives as SGLT2 inhibitors

Aim: Sodium–glucose cotransporter 2 (SGLT2) is a promising target for diabetes therapy. We aimed to develop computational approaches to identify structural features for more potential SGLT2 inhibitors. Materials & methods: In this work, 46 triazole derivatives as SGLT2 inhibitors were studied using a combination of several approaches, including hologram quantitative structure–activity relationships (HQSAR), topomer comparative molecular field analysis (CoMFA), homology modeling, and molecular docking. HQSAR and topomer CoMFA were used to construct models. Molecular docking was conducted to investigate the interaction of triazole derivatives and homology modeling of SGLT2, as well as to validate the results of the HQSAR and topomer CoMFA models. Results: The most effective HQSAR and topomer CoMFA models exhibited noncross-validated correlation coefficients of 0.928 and 0.891 for the training set, respectively. External predictions were made successfully on a test set and then compared with previously reported models. The graphical results of HQSAR and topomer CoMFA were proven to be consistent with the binding mode of the inhibitors and SGLT2 from molecular docking. Conclusion: The models and docking provided important insights into the design of potent inhibitors for SGLT2.

Structure–activity relationship and binding mode studies for a series of diketo-acids as HIV integrase inhibitors by 3D-QSAR, molecular docking and molecular dynamics simulations

We explored the main factors affecting the activity of compounds by different statistical and computational methods.

QSAR studies in the discovery of novel type-II diabetic therapies

INTRODUCTION

Type-II diabetes mellitus (T2DM) is a complex chronic disease that represents a major therapeutic challenge. Despite extensive efforts in T2DM drug development, therapies remain unsatisfactory. Currently, there are many novel and important antidiabetic drug targets under investigation by many research groups worldwide. One of the main challenges to develop effective orally active hypoglycemic agents is off-target effects. Computational tools have impacted drug discovery at many levels. One of the earliest methods is quantitative structure-activity relationship (QSAR) studies. QSAR strategies help medicinal chemists understand the relationship between hypoglycemic activity and molecular properties. Hence, QSAR may hold promise in guiding the synthesis of specifically designed novel ligands that demonstrate high potency and target selectivity.

AREAS COVERED

This review aims to provide an overview of the QSAR strategies used to model antidiabetic agents. In particular, this review focuses on drug targets that raised recent scientific interest and/or led to successful antidiabetic agents in the market. Special emphasis has been made on studies that led to the identification of novel antidiabetic scaffolds.

EXPERT OPINION

Computer-aided molecular design and discovery techniques like QSAR have a great potential in designing leads against complex diseases such as T2DM. Combined with other in silico techniques, QSAR can provide more useful and rational insights to facilitate the discovery of novel compounds. However, since T2DM is a complex disease that includes several faulty biological targets, multi-target QSAR studies are recommended in the future to achieve efficient antidiabetic therapies.