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).     

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.     

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.     

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.     

Molecular dynamics-based self-organizing molecular field analysis on 3-amino-6-arylpyrazines as the ataxia telangiectasia mutated and Rad3 related (ATR) protein kinase inhibitors

The ataxia telangiectasia mutated and Rad3 related (ATR) protein kinase is one of the apical regulators in the DNA damaging response signaling pathways. Inhibition of ATR kinase may greatly potentiates the cyto-toxicity by DNA damaging agents to the tumor cells while, have minimum effect on normal cells. In this article, the impact of ligand conformation on the three-dimensional quantitative structure–activity relationship (3D-QSAR) model of a series of 3-amino-6-arylpyrazines as ATR kinase inhibitors was investigated. We employed molecular dynamics (MDs) simulations to get the dynamic active conformations (DACs) of the compounds in the ATP-binding site of ATR kinase. As a result, the model based on the DACs extracted from the first nanosecond MD simulation is superior to that using static active conformations from docking with r ² = 0.917; $q_{\text{LOO}}^{2}$  = 0.880; standard error of estimate [SEE] = 0.259; F = 347.29; $r_{\text{pred}}^{2}$  = 0.923; SEE_pred = 0.301. Our results highlight the importance of incorporating DACs of ligand using MD simulation in 3D-QSAR studies. This study may also provide useful information to rationalize the design of novel ATR kinase inhibitors.     

Ligand-based 3D-QSAR analysis and virtual screening in exploration of new scaffolds as Plasmodium falciparum glutathione reductase inhibitors

Plasmodium falciparum glutathione reductases involved in redox homeostasis pathway of parasite are found to be the most emerging target in the treatment of malaria. In the present study, a 3D-QSAR pharmacophore model was developed, based on twenty-three 1,4-naphthoquinone derivatives reported previously with marked inhibition against glutathione reductase (GR). The pharmacophore model development and 3D-QSAR analysis was carried out by PHASE program. The hypothesis with best survival score was found to be AAHRR. Thus the resulting pharmacophore model contained two aromatic rings, a hydrophobic and two hydrogen-bond acceptor sites. A statistically reliable model with good predictive power (r ² = 0.8155, q ² = 0.7054, average r _m ²  = 0.745) was obtained. Using these pharmacophore features, we screened a library of 214,029 compounds (Asinex Database) to find potential ligands that could inhibit the PFGR protein. The compounds then subjected to a number of filters of virtual screening workflow of Schrödinger software. Here, we report the best seven compounds based on their docking scores and mode of interactions. The aromatic ring, hydrophobic group and hydrogen-bond acceptor effects contribute to the inhibitory activity. Binding interaction of the inhibitors can further provide the information regarding the role of different features in ligands responsible for linkage with receptor. Both compound 1 and screened hit with highest docking score (lead-1) found to interact with ASN 278, LYS 32, GLU 31, GLU 277, ASP 275, THR 38, LYS 151 within same binding pocket of PFGR enzyme. The backbone structural scaffolds of these seven lead compounds obtained after screening could serve as building blocks when designing drug-like molecules for inhibition of P. falciparum GR.     

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.     

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.     

Molecular docking and 3D-QSAR studies on the MAPKAP-K2 inhibitors

Mitogen-activated protein kinase-activated protein kinase 2 (MAPKAP-K2) has been identified as a drug target for the treatment of inflammatory diseases. Therefore, there is an urgent need to develop new classes of MAPKAP-K2 inhibitors. To understand the structure activity correlation of MAPKAP-K2 inhibitors, we have carried out a molecular docking study and three-dimensional quantitative structure–activity relationship (3D-QSAR) modeling. Both comparative molecular field analysis ( $r_{\text{cv}}^{2}$  = 0.602, $r_{\text{ncv}}^{2}$  = 0.955) and comparative molecular similarity indices analysis ( $r_{\text{cv}}^{2}$  = 0.546, $r_{\text{ncv}}^{2}$  = 0.891) models were generated using the training set on the basis of the common substructure-based alignment and gave reasonable results. The structural insights obtained from both the 3D-QSAR contour maps and molecular docking help to better interpret the structure activity relationship. The results obtained from this study will be useful in the design of potent MAPKAP-K2 inhibitors.     

Pharmacophore-based in silico high-throughput screening to identify novel topoisomerase-I inhibitors

Topoisomerase-I (TOP-I) has emerged as a potential target for the design and development of anticancer compounds. TOP-I inhibitors have shown promise in the treatment of various cancers including renal cell cancer, whose exact cause is yet to be known. Recent studies indicate that indenoisoquinolines can provide greater stability to drug-topoisomerase-DNA cleavage complexes, which makes them a more appropriate anticancer class of compounds compared to camptothecin. In view of such significance, a three-dimensional pharmacophore model has been developed using a training set of 36 indenoisoquinoline-based topoisomerase inhibitors. The validated best model consists of three chemical features: one hydrophobic, one positive ionizable, and one ring aromatic with good correlation values of r _(training) ²  = 0.827 and r _(test) ²  = 0.702. Furthermore, 98 % validation by CatScramble method and a good r ² of 0.703 from 22 external test set compounds have testified the universal applicability of the generated model. Validated three feature pharmacophore model has been used to screen the chemical database from the National Cancer Institute (NCI) leading to the identification of 17 druggable TOP-I inhibitors which can be raised into drug candidates after further evaluation.     

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.

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.     

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).     

Pharmacophore modeling, 3D-QSAR, and molecular docking study on naphthyridine derivatives as inhibitors of 3-phosphoinositide-dependent protein kinase-1

The 3-phosphoinositide-dependent protein kinase-1 (PDK1) is an imminent target for discovering novel anticancer drugs. In order to understand the structure–activity correlation of naphthyridine-based PDK-1 inhibitors, we have carried out a combined pharmacophore, three-dimensional quantitative structure–activity relationship (3D-QSAR), and molecular docking studies. The study has resulted in six point pharmacophore models with four hydrogen bond acceptors (A), one hydrogen bond donor (D), and one aromatic ring (R) are used to derive a predictive atom-based 3D-QSAR model. The generated 3D-QSAR model shows that the alignment has good correlation coefficient for the training set compounds which comprises the values of R ² = 0.96, SD = 0.2, and F = 198.2. Test set compounds shows Q ² = 0.84, RMSE = 0.56, and Pearson-R = 0.84. The external validation was carried out to validate the predicted QSAR model which shows good predictive power of $ r_{m}^{2} $  = 0.83 and k = 1.01, respectively. The external validation results also confirm the fitness of the model. The results indicated that, atom-based 3D-QSAR models and further modifications in PDK1 inhibitors via pharmacophore hypothesis are rational for the prediction of the activity of new inhibitors in prospect of drug design.     

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 and docking studies of novel β-carboline derivatives as anticancer

Quantitative structure–activity relationship (QSAR) studies were performed on β-carboline derivatives for prediction of anticancer activity. The statistically significant 2D-QSAR model having r ² = 0.726 and q ² = 0.654 with pred_r ² = 0.763 was developed by stepwise multiple linear regression method. In order to understand the structural requirement of these β-carboline derivatives, a ligand-based pharmacophore 3D-QSAR model was developed. The five-point pharmacophore hypothesis yielded a 3D-QSAR model with good partial least-square statistics results (r ² = 0.73, Q _ext ²  = 0.755, F = 67.5, SD = 0.245, RMSE = 0.241, Pearson-R = 0.883). A docking study revealed the binding orientations of these derivatives at the active site amino residues of DNA intercalate (PDB ID: 1D12). The results of 2D-QSAR, atom-based 3D-QSAR, and docking studies gave detailed structural insights as well as highlighted important binding features of β-carboline derivatives as anticancer agent which provided guidance for the rational design of novel potent anticancer agents.     

Molecular modeling studies on 3,4-dihydroquinazolines as trypanothione reductase inhibitors using 3D-QSAR and docking approaches

The trypanothione reductase (TryR) has been used as a key validated target to guide drug discovery for human African trypanosomiasis (HAT). 3D-QSAR and docking studies were performed on a series of 3,4-dihydroquinazolines as TryR inhibitors to establish a molecular model for new drug design. The CoMFA and CoMSIA models resulted from 53 molecules gave r _cv ² values of 0.591 and 0.574, r ² values of 0.968 and 0.943, respectively. The external validation indicated that CoMSIA model with a valid r _m ² value of 0.864 exhibited better predictive power than CoMFA model. 3D contour maps generated from CoMFA and CoMSIA along with the docking analyses have identified several key features responsible for the activity. A set of analogs were proposed by utilizing the results revealed in the present study, and were predicted with significantly improved potencies in the developed models. The results can be served as a useful guideline for designing novel 3,4-dihydroquinazoline derivatives with improved activity against human African trypanosomes.