Exploring structural requirement and binding interactions of β-amyloid cleavage enzyme inhibitors using molecular modeling techniques

β-Amyloid precursor protein cleavage enzyme (BACE) has been shown to be an attractive therapeutic target to control Alzheimer’s disease (AD). Inhibition of β-secretase enzyme can prevent the deposition of Aβ (β-amyloid) peptides, which is thought to be the major cause of AD. The present study has been considered to explore 3D-QSAR, HQSAR, and pharmacophore mapping studies of BACE inhibitors. Contour maps of 3D-QSAR studies (CoMFA: R ² = 0.998, se = 0.067, Q ² = 0.765, R _pred ²  = 0.772, r _m ²  = 0.739; CoMSIA: R ² = 0.992, se = 0.125, Q ² = 0.730, R _pred ²  = 0.713, r _m ²  = 0.687) explain the importance of steric and electrostatic, along with hydrogen-bond (HB) acceptor and donor for binding affinity to BACE. HQSAR study (R ² = 0.941, se = 0.326, Q ² = 0.792, R _pred ²  = 0.713, r _m ²  = 0.709) indicates the important fragments of the molecular fingerprints that might be crucial for binding affinity. Pharmacophore space modeling (R ² = 0.937, rmsd = 0.937, Q ² = 0.935, R _pred ²  = 0.709, r _m ²  = 0.837) describes that HB acceptor, donor, hydrophobic, and steric are the important features for interaction with receptor cavity. Finally, the models are adjudged through the docking study elucidating the interactions between the receptor and the ligand, indicating the structural requirements of potent BACE inhibitors.     

Identification of structural requirements of estrogen receptor modulators using pharmacoinformatics techniques for application to estrogen therapy

In this study, we explored the structural requirements of known estrogen receptor modulators for biological activity using pharmacoinformatics approaches to elucidate critical functionalities for new, potent and less toxic chemical agents for successful application in estrogen therapy. For this purpose, a group of nonsteroidal ligands 7-thiabicyclo[2.2.1]hept-2-ene-7-oxide derivatives were collected from the literature to perform quantitative structure–activity relationship (QSAR), pharmacophore and molecular docking studies. The 2D QSAR models (R _α ²  = 0.857, se _α  = 0.370, Q _α ²  = 0.848, R _pred?α ²  = 0.675, s _pα  = 0.537; R _β ²  = 0.874, se _β  = 0.261, Q _β ²  = 0.859, R _pred?β ²  = 0.659, s _pβ  = 0.408) explained that hydrophobicity and molar refractivity were crucial for binding affinity in both α- and β-subtypes. The space modeling study (R _α ²  = 0.955, se _α  = 1.311, Q _α ²  = 0.932, R _pred?α ²  = 0.737, s _pα  = 0.497; R _β ²  = 0.885, se _β  = 1.328, Q _β ²  = 0.878, R _pred?β ²  = 0.769, s _pβ  = 0.336) revealed the importance of HB donor and hydrophobic features for both subtypes, whereas HB acceptor and aromatic ring were critical for α- and β-subtypes, respectively. The functionalities developed in the QSAR and pharmacophore studies were substantiated by molecular docking studies which provided the preferred orientation of ligands for effective interaction at the active site cavity.     

QSAR studies on pyrrolidine amides derivatives as DPP-IV inhibitors for type 2 diabetes

A large series of pyrrolidine amides derivatives as DPP-IV inhibitors was subjected to quantitative structure–activity relationship (QSAR) analysis. These 248 geometrical structures were constructed and optimized at the HF/6-31G* level of theory by the Gaussian program. The 2D–QSAR model was developed from a training set consisting of 186 compounds by the minimum redundancy maximum relevance–sequential floating back–support vector regression method with a good determination coefficient: the squared correlation coefficient (R _train ²  = 0.867) and the tenfold cross-validation squared correlation coefficient (q _train-CV ²  = 0.669). The QSAR model was then tested using an external test set consisting of 62 compounds and provided a satisfactory external predictive ability (R _test ²  = 0.666). 2D–QSAR model is robust and reliable when compared with 3D–QSAR techniques for the analogous compounds. According to the QSAR analysis, the electronic effect plays an important role for the substituents of the pyrrolidine and carbon rings. The study would serve as a guideline in designing more potent and selective drugs against type 2 diabetes.     

QSAR study of mGlu5 inhibitors by genetic algorithm-multiple linear regressions

In this study, the quantitative structure–activity relationship (QSAR) model for some pyrazole/imidazole amide derivatives as mGlu5 inhibitors was developed. The data set was split into the training and test subsets, randomly. The most relevant variables were selected using the genetic algorithm (GA) variable selection method. Multiple linear regression (MLR) method was used as a linear model to predict the activity of mGlu5 inhibitors based on compounds in training set. The external set of nine compounds selected out of 47 compounds, and used to evaluate the predictive ability of QSAR model. The built model could give high statistical quantities (R _train ²  = 0.837, Q ² = 0.759, R _test ²  = 0.919) in which proved that the GA-MLR model was a useful tool to predict the inhibitory activity of pyrazole/imidazole amide derivatives. The results suggested that the atomic masses, atomic van der Waals volumes, atomic electronegativities, and the number of imines (aromatic) are the most important independent factors that contribute to the mGlu5 inhibition activity of pyrazole/imidazole amides derivatives.     

Shuffling multivariate adaptive regression splines as a predictive method for modeling of novel pyridylmethylthio derivatives as VEGFR2 inhibitors

The vascular endothelial growth factor receptor (VEGFR2) is an attractive target for the development of novel anticancer agents. Molecular docking and quantitative structure–activity relationship (QSAR) were used to investigate how inhibitors’ chemical structures relate to the inhibitory activities. The molecular docking studies show that at least one hydrogen bond with LYS866 residue is one of the essential requirements for the optimum binding of a series of 42 pyridylmethylthio inhibitors. The obtained QSAR model indicates that the inhibitory activity can be described by solvent-accessible molecular surface area, topological electronic indices, local dipole index, steric interaction, and hydrogen bonding energies between the receptor and the inhibitors. Furthermore, several validation methods were used to evaluate the predictive capacity of the generated models. The satisfactory results (R _L25 %O ²  = 0.819, Q _LOO ²  = 0.838, R _p ²  = 0.866, RMSE_LOO = 0.315, and RMSE_L25 %O = 0.337) suggest that the models exhibited considerable predictive power which can be used in prediction of activity of new pyridylmethylthio inhibitors. Also the docking analysis showed that the interaction of the inhibitors with residues ALA879, ASP(1044, 1026), LEU880, PHE843, and LYS866 plays an important role in the activities of the inhibitors.     

Quantitative structure–activity relationship studies of diarylpyrimidine derivatives as anti-HIV drugs using new three-dimensional structure descriptors

A novel three-dimensional holographic vector of atomic interaction field (3D-HoVAIF) was used to describe the chemical structures of 34 wild-type DAPYs, 33 mutant form L100I, 30 mutant form Y181C and 29 mutant form Y188L as anti-HIV drugs. Here four quantitative structure activity relationship models were built by partial least square regression. The estimation stability and prediction ability of models were strictly analyzed by both internal and external validations. The correlation coefficient (R _cum ² ), leave-one-out cross-validation correlation coefficient (Q _CV ² ) and predicted values versus experimental ones of external samples (Q _ext ² ) were 0.925, 0.769 and 0.949 for 34 diarylpyrimidines; 0.899, 0.788 and 0.889 for 33 mutant form L100I; 0.844, 0.761 and 0.935 for 30 mutant form Y181C; 0.890, 0.757 and 0.912 for 29 mutant form Y188L. These values indicated that the built PLS models had both favorable estimation stability and good prediction capabilities. Furthermore, the satisfactory results showed that 3D-HoVAIF could preferably express the information related to the biological activity of DAPY derivatives.     

A novel saponin from Manilkara hexandra seeds and anti-inflammatory activity

Chromatographic separation of acetone precipitate of the seeds of Manilkara hexandra has resulted in a novel saponin, 3-O-(β-d-apiofuranosyl-(1 → 3)-β-d-glucopyranosyl)-28-O-(α-l-rhamnopyranosyl(1 → 3)-β-d-xylopyranosyl(1 → 4)-α-l-rhamnopyranosyl(1 → 2)-α-l-arabinopyranosyl)-16-α-hydroxyprotobassic acid (Saponin 3), together with two known saponins isolated for the first time from the family Sapotaceae, viz, 3-O-β-d-glucopyranosyl(1 → 6)[(β-d-apiofuranosyl-(1 → 3)]-β-d-glucopyranosyl)-28-O-(α-l-rhamnopyranosyl(1 → 3)-β-d-xylopyranosyl(1 → 4)-α-l-rhamnopyranosyl(1 → 2)-α-l-arabinopyranosyl)-16α-hydroxyprotobassic acid (Saponin 1), 3-O-(β-d-glucopyranosyl)-28-O-(α-l-rhamnopyranosyl(1 → 3)-β-d-xylopyranosyl(1 → 4)-α-l-rhamnopyranosyl(1 → 2)-α-l-arabinopyranosyl)-protobassic acid, and 3-O-(β-d-glucopyranosyl)-28-O-(α-l-rhamnopyranosyl(1 → 3)-β-d-xylopyranosyl(1 → 4)-α-l-rhamnopyranosyl(1 → 2)-α-l-arabinopyranosyl)-protobassic acid (Saponin 2). Also, three known phenolic compounds were isolated for the first time from the species hexandra, viz, gallic acid, myrecetin, and quercetin. The chemical structures of the isolated saponin compounds were established by spectral techniques (UV, ¹H, ¹³C NMR, and MS). The acetone fraction containing the crude saponin mixture possessed a significant inhibitory effect on LPS-induced nitric oxide to the extent of 60 % compared to the LPS-stimulated cells and to the extent of 20 % compared to the control level showing significant anti-inflammatory activity. Acetone and MeOH seed extracts as well as the crude saponin fraction of M. hexandra showed no antioxidant activity as measured by DPPH assay (SC₅₀ = 217.65, 496.68, and 562.38 μg/ml, respectively) compared to that of ascorbic acid (SC₅₀ = 12.9). The MeOH seed extract showed no cytotoxic activity against three different human cancer cell lines, viz, colon carcinoma (HCT-116), hepatocellular carcinoma (Hep-G2), and breast adenocarcinoma (MCF-7), estimated by MTT assay (IC₅₀ = 95.20, 73.39, and 79.15 μg/ml, respectively).     

QSAR studies of antituberculosis drug using three-dimensional structure descriptors

A newly developed three-dimensional holographic vector of atomic interaction field (3D-HoVAIF) was used to describe the chemical structures of 28 arylamide derivatives as antituberculosis drug. Here, a quantitative structure activity relationship model was built by partial least square regression (PLS). The estimation stability and generalization ability of the model was strictly analyzed by both internal and external validations. The correlation coefficients of established PLS model (R ²), leave-one-out cross-validation (Q _LOO ² ), and predicted values versus experimental ones of external samples (Q _ext ² ) were 0.800, 0.778 and 0.821, respectively. The results of PLS exhibited both favorable estimation stability and good prediction capability. Thus, this newly developed 3D-HoVAIF could preferably express information related to biological activity of arylamide derivatives.     

A 2D image-based method for modeling some c-Src tyrosine kinase inhibitors

The detailed application of multivariate image analysis method for evaluation of quantitative structure–activity relationship (MIA–QSAR) of some c-Src tyrosine kinase inhibitors is demonstrated here. Partial least squares (PLS) and genetic algorithm principal components general regression neural network (GA–PC–GRNN) methods were applied. The performance of PLS and GA–PC–GRNN were investigated by several validation methods. The resulted PLS model had a high statistical quality (R ² = 0.951 and R _CV ²  = 0.949) for predicting the activity of the compounds. For GA–PC–GRNN model, these values were R ² = 0.850 and R _CV ²  = 0.692. Applicability domain of developed models was considered using leverage and William plots were used to visualize it. PLS method was proved to be more predictive and accurate.     

Binding site analysis, 3D-QSAR studies, and molecular design of flavonoids derivatives as potent neuraminidase inhibitors

Binding site analysis of flavonoids derivatives indicated that Arg152, Trp178, Ile222, Glu227, and Ala246 were the key residues in the active pocket of 1nnc. Main influencing factors of interactions between flavonoids derivatives and neuraminidase (NA) were hydrogen bond and electrostatic. Meanwhile, 3D-QSAR models of flavonoids derivatives were constructed to understand chemical–biological interactions governing their activities toward NA. The developed 3D-QSAR models were robust and had good predictive capabilities. R ², Q ², R _test ² , and Q _ext ² of the CoMFA and CoMSIA models were 0.816 and 0.929, 0.607 and 0.750, 0.507 and 0.642, and 0.478 and 0.568, respectively. Moreover, hydrogen bonds and electrostatic factors highly contributed to inhibitory activity, which were unanimous in the docking results. In addition, based on the most active sample ID33, seven new compounds with high inhibitory activity and docking score were obtained.     

Theoretical studies on benzimidazole derivatives as E. coli biotin carboxylase inhibitors

Biotin carboxylase (AccC) protein plays an essential role in cell wall biosynthesis in majority of bacterial genera. Inhibition of cell wall biosynthesis might be an ideal way to control the bacterial multiplication in the host system. AccC is one of the promising targets for the antibacterial drugs production. The benzimidazole derivatives are hopeful biotin carboxylase inhibitors, which sensitizes to the Escherichia coli (E. coli) and many other bacterial species too. In steam of developing better benzimidazole derivatives, we describe a quantitative pharmacophore model of benzimidazole derivatives using Phase module of Schr?dinger LLC. This model suggested that the following features are essential for ligand binding, i.e., two aromatic rings, two hydrogen bond donors, one hydrogen bond acceptor, and one hydrophobic group. Further, atom-based 3D-QSAR model was constructed using training set of 37 inhibitors. The constructed QSAR model has cross validated co-efficient value of (Q ²) 0.736 and regression co-efficient value of (R ²) 0.937. The external validation indicated that our QSAR model possessed high predicted powers with $ r_{o}^{2} $ value of 0.933, $ r_{\text m}^{2} $ value of 0.876. The best active and least active compounds were docked into the active site of receptor using Glide and hotspots of the active site were analyzed. The QSAR elucidated here for benzimidazole derivatives combined with their binding information will provide an opportunity to explore the chemical space to promote the potency of AccC inhibitors.     

Atom-based 3D QSAR studies on novel N-β-d-xylosylindole derivatives as SGLT2 inhibitors

Sodium-dependent glucose cotransporter 2 (SGLT2) have emerged as a novel drug target for hyperglycemia, a major complication of type 2 diabetes, with a multitude of therapeutic potential for their inhibitors. A series of N-β-d-xylosylindole derivatives has been reported as SGLT2 inhibitors. Therefore, to determine the structural requisite of these SGLT2 inhibitors, 3D pharmacophore models and atom-based 3D QSAR models have been developed using the PHASE module of Schrödinger. The best six-featured pharmacophore hypothesis with two hydrogen bond acceptors, two hydrogen bond donors, one hydrophobic features, and one aromatic ring yielded a 3D QSAR model. The derived model have significant PLS values as R ² = 0.9527, correlation coefficient of training set, and Q ² = 0.9045, correlation coefficient of test set, indicating the model have good predictive power. The results provide detailed insights of N-β-d-xylosylindole derivatives which can afford guidance for rational drug design of novel potent SGLT2 inhibitors.     

Synthesis,characterization, and computational studies on phthalic anhydride-based benzylidene-hydrazide derivatives as novel,potential anti-inflammatory agents

A series of phthalic anhydride-based substituted benzylidene-hydrazide derivatives (3a–i) was synthesized. The synthesized derivatives were authenticated by TLC, UV–visible, FTIR, NMR, and mass spectroscopic techniques and further screened for in vivo anti-inflammatory and analgesic activities by carrageenan-induced rat paw oedema and tail immersion methods, respectively, using diclofenac sodium as standard drug. The derivatives 3d, 3e, and 3h were found to be most active anti-inflammatory and analgesic agents among all the synthesized derivatives. The physico-chemical similarity of the derivatives with standard drugs was assessed by calculating various physicochemical properties using software programs. The percent similarity of synthesized derivatives was found to be good except 3i. The derivatives were subjected to QSAR by multilinear regression using Analyze it version 3.0 software and two statistically sound models were developed with R ² (0.933–0.960), $ R_{{adj}}^{2} $ (0.595–0.762) and Q ² (0.999) with good F (2.76–4.84) values. Molecular docking studies were performed by MVD software (version 2012.5.0.0). The derivative 3h has emerged out as most potent anti-inflammatory agent with highest dock score, i.e., ?93.64.