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.     

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.     

Molecular modeling studies on structural requirement of diarylpropionitrile for selectivity to estrogen receptor subtypes

Molecular modeling techniques are widely used to discover drug candidates for selective disease. In the present study, ligand-based drug design techniques have been explored to find the structural requirement of diarylpropionitrile derivatives, a group of non-steroidal estrogen receptor (ER) modulators for selective binding to receptor subtypes. 2D/3D quantitative structure activity relationship (QSAR) and pharmacophore space modeling studies have been explored for this purpose. The classical QSAR models (R _α ² ?=?0.870, Q _α ² ?=?0.813, R _α-pred ² ?=?0.636; R _β ² ?=?0.853, Q _β ² ?=?0.745, R _β-pred ² ?=?0.565) show the importance of molecular refractivity, electronic contribution of atoms C₃, C₇, C₁₃ and C₁₄, and R₂ and R₄ substituents (Fig.?1) for specificity. The 3D QSAR, molecular fields (CoMFA, R _α ² ?=?0.999, Q _α ² ?=?0.679, R _α-pred ² ?=?0.678 and R _β ² ?=?0.999, Q _β ² ?=?0.611, R _β-pred ² ?=?0.691) and similarity (CoMSIA, R _α ² ?=?0.999, Q _α ² ?=?0.670, R _α-pred ² ?=?0.686 and R _β ² ?=?0.999, Q _β ² ?=?0.671, R _β-pred ² ?=?0.590) analyses show contour maps of steric, hydrophobic along with hydrogen bond (HB) donor and acceptor are important factors for binding affinity to both α- and β-subtypes. In addition, electronic contribution is crucial for α-subtype binding. Pharmacophore models derive the importance of HB acceptor and donor, aromatic ring, molecular steric, and hydrophobic interactions for selective binding to receptor subtypes. The derived models are correlated with structure-based molecular docking study, explaining the significant interactions between receptor and ligand for selective subtypes binding.

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.     

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.     

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.     

Quasi 4D-QSAR and 3D-QSAR study of the pan class I phosphoinositide-3-kinase (PI3K) inhibitors

The class I phosphoinositide-3-kinases (PI3Ks) is currently investigated and attracted as a promising target toward anticancer therapies. The quasi 4D-QSAR model is developed by a training set of 30 pan class I PI3K inhibitors. This methodology is based on the generation of a conformational ensemble profile for each compound instead of only one conformation, followed by the calculation of intermolecular interaction energies at each grid point considering probes and all aligned conformations resulting from molecular dynamic simulations. A comparison of the proposed methodology with comparative molecular field analysis (CoMFA) formalism has been performed. This paradigm explores jointly the main features of CoMFA and 4D-QSAR models. The best 4D-QSAR model is checked for free from chance correlation, reliability and robustness by leave-N-out cross-validation and Y-randomization in addition to analysis of the independent test set. Statistical parameters of the best 4D-QSAR model are R ² = 0.871, q _LOO ²  = 0.661, and R _Pred ²  = 0.751. The results of the suggested model are in good agreement with docking study that was previously reported by Rewcastle et al. (J Med Chem 54:7105–7126, 2011).     

Design of potent human steroid 5α-reductase inhibitors: 3D-QSAR CoMFA, CoMSIA and docking studies

3D-QSAR CoMFA, CoMSIA and docking studies were performed on a set of 4-azasteroidal human steroid 5α-reductase inhibitors. The models developed using maximal common substructure-based alignment was found to be reliable and significant with good predictive r ² value. CoMSIA model developed using combination of steric, electrostatic, hydrophobic, hydrogen bond donor and hydrogen bond acceptor features has shown r _cv ²  = 0.564 with six optimum components, r _ncv ²  = 0.945, F value = 101.196, r _Pred ²  = 0.693 and SEE = 0.209. The contour plots obtained has shown a favourable effect of bulkier groups at C-17 position. Docking studies indicates the importance of bulkier groups at C-17 position for favourable activity. The study further helps in design of potent inhibitors of the enzyme.     

Study on the quantitative structure–toxicity relationships of aconitine compounds basing on PCA-ANN method

Aconitine compounds are diterpenoid alkaloids found in the roots/rhizome of Aconitum napellus, Aconitum carmichaeli, and other Aconitum plants in the family of Ranunculanceae, which have beneficial pharmacological activity along with toxicity. The quantum chemistry parameters of thirty-six aconitine compounds were calculated using Gaussian software, and the quantitative structure–toxicity relationships of aconitine compounds were studied in mice via oral acute toxicity (LD₅₀). A model was built to more accurately predict the toxicity of aconitine compounds in mice versus oral LD₅₀. Twenty-seven aconitine compounds were used as a training dataset for building the principal component analysis combined with artificial neural network model and nine others as a forecasting dataset to test the prediction ability of the model using SAS 9.0 program software and Matlab 7.5 software. The model derived a good forecasting ability (MSE = 0.50, R ² = 0.9818 $ R_{\text{pred}}^{2} $  = 0.9457, $ r_{{{\text{m}}\left( {\text{test}} \right)}}^{2} $  = 0.9130, $ r_{{{\text{m}}\left( {\text{overal}} \right)}}^{2} $  = 0.9207, $ R_{\text{r}}^{2} $  = 0.6561, $ {\text{c}}R_{\text{r}}^{2} $  = 0.5655).     

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).     

Molecular docking and 3D-QSAR studies on checkpoint kinase 1 inhibitors

Checkpoint kinase 1(Chk1) is a promising target for cancer treatment. Here three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were performed on 174 1,4-dihydroindeno[1,2-c]pyrazole inhibitors of Chk1 using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Two satisfactory ligand-based QSAR models were built (CoMFA model: q ² = 0.541, r ² = 0.880, CoMSIA model: q ² = 0.590, r ² = 0.902). The docking-based studies presented a detailed understanding of interaction between the inhibitors and Chk1. The obtained QSAR models are highly predictable (CoMFA model: q ² = 0.567, r ² = 0.891, CoMSIA model: q ² = 0.596, r ² = 0.917). The models were further validated by an external testing set obtaining $ r_{\text{pred}}^{2} $ r pred 2 values 0.896 and 0.923 for CoMFA and CoMSIA, respectively. So our models might be helpful for further modification of 1,4-dihydroindeno[1,2-c]pyrazole derivatives.     

In situ relationships between spatial–temporal variations in potential ecological risk indexes for metals and the short-term effects on periphyton in a macrophyte-dominated lake: a comparison of structural and functional metrics

Heavy metals may adversely affect the structure and function of the periphyton community in lake ecosystems. We carried out samplings of three habitats at eight sites located in the Lake Baiyangdian that is strongly influenced by wastewater discharge (Sites 1 and 2), aquaculture and densely populated villages (Sites 3, 6, and 8), and the least disturbed (Sites 4, 5, and 7). Cu, Ni, Pb, Zn, Hg, Cd, and Cr were determined in these samples, and the periphyton community was simultaneously studied. The contamination factor (C _f ⁱ ) was estimated for every metal as the ratio between pre-industrial records from sediments (C _n ⁱ ) and present concentration values (C ⁱ ), and the individual potential risk (E _r ⁱ ) was calculated by multiply the toxic response factor (Tr ⁱ ) and C _f ⁱ for a given substance were based on Hakanson’s methodology. The RI was obtained for each sampling site by summing the values of E _r ⁱ first and the average was calculated across the sampling sites. The results showed that the RI for all three habitats was lower than 94, and they are in decreasing order: wastewater discharge, aquaculture and densely populated villages, and the least anthropogenic impacted. When the three sampling seasons were compared, August appeared to show the highest risk, followed by April and November. The RI values showed negative correlations (r = ?0.444 to ?0.851, p < 0.05) with the structural and functional metrics. The best correlation was detected between chlorophyll c/chlorophyll a (Chl c/a) ratio and E _r ⁱ Hg (r = ?0.851, p < 0.01). Our results suggest the periphyton community can be used in bio-monitoring.     

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.