3D-QSAR and pharmacophore model study on aryl diphenolic azoles as estrogen receptor-β ligands

Pharmacophore and three-dimensional quantitative structure–activity relationship (3D-QSAR) model of 106 aryl diphenolic azoles as estrogen receptor-β ligands were proposed. The best pharmacophore model, five-site model including two hydrogen bond donors and three aromatic rings, has been established. The biological activity values (pIC₅₀) and classification (active or inactive) of compounds were predicted by 3D-QSAR model. The model correlation coefficient (R ²) is 0.9623. The classification accuracies in predicting for training, test, and total datasets are 97.5, 84.6, and 94.3 %, respectively. The 3D-QSAR model maps revealed that hydrogen bond donor, hydrophobic, and electron-withdrawing fields would be the essential factors for designing new ligands with higher biological activity. The results indicate that the combination of pharmacophore and 3D-QSAR analyses could be a powerful modeling tool for finding novel estrogen receptor-β ligands.     

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.     

A comprehensive structure–activity analysis 2,3,5-trisubstituted 4,5-dihydro-4-oxo-3H-imidazo [4,5-c] pyridine derivatives as angiotensin II receptor antagonists: using 2D- and 3D-QSAR approach

The QSAR studies were performed on a series of 2,3,5-trisubstituted 4,5-dihydro-4-oxo-3H-imidazo [4,5-c] pyridine derivatives as angiotensin II AT₁ receptor antagonists activity to find out the structural features requirements for the antihypertensive activity. The QSAR study was carried out on V-life Molecular Design Suite software and the derived best QSAR model by partial least square principal component regression and multiple linear regression method showed variation in biological activity. The statistically best significant model with high-correlation coefficient (r ² = 0.9425) was selected for further study and the resulted validation parameters of the model, cross-validated correlation coefficient (q ² = 0.7786 and pred_r ² = 0.8562) show the model has good predictive activities. The model showed that the parameters SdssCcount, SssNHcount, and SaaaCcount and H_Donor count are highly correlated with angiotensin II AT₁ receptor antagonists activity of 2,3,5-trisubstituted 4,5-dihydro-4-oxo-3H-imidazo [4,5-c] pyridine derivatives. Partial least square (PLS) methodology coupled with various feature selection methods viz. stepwise, simulated annealing and genetic algorithm were applied to derive 3D-QSAR models which were further validated for statistical significance and predictive ability by internal and external validation. Molecular field analysis was used to construct the best 3D-QSAR model-7 using k-nearest neighbor (kNN) method, showing good correlative and predictive capabilities in terms of q ² = 0.8316 and pred_r ² = 0.8152. Both 2D-and 3D-QSAR study of such derivatives provide guidance for further lead optimization and designing of potent anti-hypertensive agents.     

Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches

Identification of endocrine disrupting chemicals is one of the important goals of environmental chemical hazard screening. We report on the development of validated in silico predictors of chemicals likely to cause estrogen receptor (ER)-mediated endocrine disruption to facilitate their prioritization for future screening. A database of relative binding affinity of a large number of ERα and/or ERβ ligands was assembled (546 for ERα and 137 for ERβ). Both single-task learning (STL) and multi-task learning (MTL) continuous quantitative structure–activity relationship (QSAR) models were developed for predicting ligand binding affinity to ERα or ERβ. High predictive accuracy was achieved for ERα binding affinity (MTL R² = 0.71, STL R² = 0.73). For ERβ binding affinity, MTL models were significantly more predictive (R² = 0.53, p < 0.05) than STL models. In addition, docking studies were performed on a set of ER agonists/antagonists (67 agonists and 39 antagonists for ERα, 48 agonists and 32 antagonists for ERβ, supplemented by putative decoys/non-binders) using the following ER structures (in complexes with respective ligands) retrieved from the Protein Data Bank: ERα agonist (PDB ID: 1L2I), ERα antagonist (PDB ID: 3DT3), ERβ agonist (PDB ID: 2NV7), and ERβ antagonist (PDB ID: 1L2J). We found that all four ER conformations discriminated their corresponding ligands from presumed non-binders. Finally, both QSAR models and ER structures were employed in parallel to virtually screen several large libraries of environmental chemicals to derive a ligand- and structure-based prioritized list of putative estrogenic compounds to be used for in vitro and in vivo experimental validation.     

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.     

Pharmacophore-based 3D-QSAR study of fungal chitin synthase inhibitors

To design new compounds with enhanced activity against the fungal chitin synthase enzyme, 3D-pharmacophore models were generated and QSAR study was carried out on 44 novel homoallylamines and related compounds, nikkomycin, maleimide, chalcones, and quinolin-2-one derivatives. A three-point pharmacophore with two hydrophobic (H) and one aromatic ring (R) as pharmacophore features was developed by PHASE module of Schrodinger molecular modeling suite. The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of R ² of 0.84 for training set compounds. The model generated showed excellent predictive power, with a correlation coefficient of Q ² of 0.63 and Pearson-R value of 0.82 for a randomly chosen test set of nine compounds. The 3D-QSAR model explains the structure–activity relationship of these compounds which may help in the design and development of novel fungal chitin synthase inhibitors.     

In silico accounting of novel pyridazine analogues as h-PTP 1B inhibitors: pharmacophore modelling,atom-based 3D QSAR and docking studies

PTP 1B, a negative regulator of insulin signalling pathway, has been rigorously investigated for its potential for design and development of drugs for the management of type 2 diabetes. Pharmacophore modelling, atom-based 3D QSAR and docking studies were performed on a series of 37 pyridazine derivatives reported as PTP 1B inhibitors. A five-point pharmacophore model consisting of two hydrogen bond acceptors (A), one hydrogen bond donor (D), one hydrophobic (H) and one aromatic ring (R) with discrete geometries as pharmacophoric features were developed for a predictive 3D QSAR model. The robustness of the generated model was validated by good correlation coefficient value (r ² = 0.832), Pearson-R value (0.8593), cross-validated correlation coefficient (q ² = 0.663). This study investigated some of the indispensible structural features of pyridazine analogues which can further be exploited to optimize h-PTP 1B inhibitors.     

Prediction of anti-HCV activity of thiourea derivatives: QSAR approach

Abstract  

In pursuit of more active thioureas as anti-HCV agent, QSAR studies were performed on a series of thioureas to explore the various physico-chemical parameters responsible for their activity against HCV-infected AVa 5 cell. Physico-chemical parameters were calculated using WIN CAChe 6.1. Stepwise multiple linear regression analysis was performed to derive QSAR models which were further evaluated for statistical significance and predictive power by internal and external validation. The selected best QSAR model was having correlation coefficient (R) = 0.902 and cross-validated squared correlation coefficient (Q ²) = 0.734. The developed significant QSAR model indicates that hydrophobicity, dielectric energy, valence connectivity index (order 1), and ionization potential of whole molecule play an important role in anti-HCV activity of thioureas. The hydrophobicity of thioureas was found to have parabolic relation with its anti-HCV activity. The optimum logP value for these anti-HCV compounds was found to be 4.988.     

Quantitative structure activity relationship analysis of angiotensin II AT1 receptor antagonists

The quantitative structure activity relationship (QSAR) models were developed using multiple linear regression (MLR) and partial least square (PLS) for a set of 85 AT₁ receptor antagonists of hydantoin series. The MLR and PLS generated comparable models with good predictive ability and all the other statistical values, such as r, r², \textr_(\textcv)² , {\text{r}}_{{({\text{cv}})}}^{2} , and F and s values, were satisfactory. The results obtained from this study indicate the importance of steric (K-alpha3), hydrophobic (log P, and total lipole), and total energy (Cosmic total energy) in determining the activity of AT₁ receptor antagonists. The results clearly explained that optimum hydrophobicity of substituent at R₂ position is favorable for the activity and presence of a substituent of particular size and shape on phenyl ring at R₃ position is essential for the activity. This information is pertinent to the further design of new AT₁ receptor antagonist containing the hydantoin nucleus.     

Pharmacophore modeling and 3D QSAR analysis of isothiazolidinedione derivatives as PTP1B inhibitors

The article describes the development of a robust pharmacophore model and investigation of structure activity relationship analysis of 56 isothiazolidinedione derivatives reported as PTP1B inhibitors. A six-point pharmacophore model consisting of four aromatic rings (R), one hydrogen bond donor (D) and one hydrogen bond acceptor (A) with discrete geometries as pharmacophoric features was developed and the generated pharmacophore model was used to derive a predictive 3D QSAR model for the studied dataset. The obtained 3D QSAR model has an excellent correlation coefficient value (r ² = 0.98) along with good statistical significance as shown by a high Fisher ratio (F = 428.60). The model also exhibits good predictive power confirmed by the high value of cross-validated correlation coefficient (q ² = 0.62). The QSAR model suggests that hydrophobic aromatic character is crucial for the PTP1B inhibitory activity at the R-15 site.     

Structural features of GABAA receptor antagonists: pharmacophore modeling and 3D-QSAR studies

Pharmacophore modeling, comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA) studies have been carried out on 5-(4-piperidyl)-3-isoxazolol (4-PIOL) analogs as GABA_A receptor antagonists in this study. The best pharmacophore hypothesis generated by PHASE was ADHPR.6, which comprised a hydrogen bond acceptor (A), a hydrogen bond donor (D), a hydrophobic group (H), a positively charged group (P), and an aromatic ring (R). The pharmacophore model provided a good alignment for the further 3D-QSAR analyses, which presented a good R ² value of 0.943, 0.930, and 0.916 for atom-based QSAR model, CoMFA model, and CoMSIA model, respectively. All QSAR models presented good statistical significance and predictivity, the corresponding Q ² values for each 3D-QSAR model are 0.794, 0.569, and 0.637, respectively. Both pharmacophore and CoMSIA results showed that the hydrophobic sites are the key structural feature for GABA_A receptor antagonists with high activities.