QSAR and Molecular Docking Studies of Oxadiazole-Ligated Pyrrole Derivatives as Enoyl-ACP (CoA) Reductase Inhibitors

A quantitative structure-activity relationship model was developed on a series of compounds containing oxadiazole-ligated pyrrole pharmacophore to identify key structural fragments required for anti-tubercular activity. Two-dimensional (2D) and three-dimensional (3D) QSAR studies were performed using multiple linear regression (MLR) analysis and k-nearest neighbour molecular field analysis (kNN-MFA), respectively. The developed QSAR models were found to be statistically significant with respect to training, cross-validation, and external validation. New chemical entities (NCEs) were designed based on the results of the 2D- and 3D-QSAR. NCEs were subjected to Lipinski’s screen to ensure the drug-like pharmacokinetic profile of the designed compounds in order to improve their bioavailability. Also, the binding ability of the NCEs with enoyl-ACP (CoA) reductase was assessed by docking.     

Three-dimensional quantitative structure-activity relationship analysis of a set of Plasmodium falciparum dihydrofolate reductase inhibitors using a pharmacophore generation approach

A 3D pharmacophore model able to quantitatively predict inhibition constants was derived for a series of inhibitors of Plasmodium falciparum dihydrofolate reductase (PfDHFR), a validated target for antimalarial therapy. The data set included 52 inhibitors, with 23 of these comprising the training set and 29 an external test set. The activity range, expressed as Ki, of the training set molecules was from 0.3 to 11 300 nM. The 3D pharmacophore, generated with the HypoGen module of Catalyst 4.7, consisted of two hydrogen bond donors, one positive ionizable feature, one hydrophobic aliphatic feature, and one hydrophobic aromatic feature and provided a 3D-QSAR model with a correlation coefficient of 0.954. Importantly, the type and spatial location of the chemical features encoded in the pharmacophore were in full agreement with the key binding interactions of PfDHFR inhibitors as previously established by molecular modeling and crystallography of enzyme-inhibitor complexes. The model was validated using several techniques, namely, Fisher's randomization test using CatScramble, leave-one-out test to ensure that the QSAR model is not strictly dependent on one particular compound of the training set, and activity prediction in an external test set of compounds. In addition, the pharmacophore was able to correctly classify as active and inactive the dihydrofolate reductase and aldose reductase inhibitors extracted from the MDDR database, respectively. This test was performed in order to challenge the predictive ability of the pharmacophore with two classes of inhibitors that target very different binding sites. Molecular diversity of the data sets was finally estimated by means of the Tanimoto approach. The results obtained provide confidence for the utility of the pharmacophore in the virtual screening of libraries and databases of compounds to discover novel PfDHFR inhibitors.     

Pharmacophore modeling of some novel indole β-diketo acid and coumarin-based derivatives as HIV integrase inhibitors

To design new chemotypes with enhanced potencies against the HIV integrase enzyme, 3D pharmacophore models were generated and QSAR study was carried out on 44 novel indole β-diketo acid derivatives and coumarin-based Inhibitors. A five-point pharmacophore with two hydrogen bond acceptors (A) and three aromatic rings (R) as pharmacophore features was developed by PHASE module of Schrodinger suite. The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of R ² = 0.81 for training set compounds. The model generated showed excellent predictive power, with a correlation coefficient of Q ² = 0.69 for a randomly chosen test set of eight compounds. The 3D-QSAR plots illustrated insights into the structure activity relationship of these compounds which may helps in the design and development of novel integrase inhibitors.     

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.     

Pharmacophore model generation and 3D-QSAR analysis of N-acyl and N-aroylpyrazolines for enzymatic and cellular B-Raf kinase inhibition

A successful 3D-QSAR study has been performed for amino-substituted N-acyl and N-aroylpyrazolines as B-Raf kinase inhibitors by means of a common five-point pharmacophore model. In this study, highly predictive 3D-QSAR models have been developed for B-Raf kinase inhibition and pERK inhibition using AADRR-2 and AADRR-6 hypothesis, respectively. The best models showed statistically outstanding values of 0.97, 0.95 and 0.91, 0.87 for r ² and q ² for AADRR-2 and AADRR-6 hypothesis, respectively. The validation of the PHASE model was done by dividing the dataset into training and test set. From the QSAR model, it can implicated that electron-withdrawing and hydrophobic groups are not advantageous for both enzymatic and cellular activities. However, H-bond donor characteristic is favorable for cellular inhibition and unfavorable for enzymatic inhibition. Based on the findings of the 3D-QSAR study, novel and promising compounds for B-Raf kinase inhibition can be synthesized.     

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.     

Pharmacophore and quantitative structure-activity relationship modeling: complementary approaches for the rationalization and prediction of UDP-glucuronosyltransferase 1A4 substrate selectivity

Pharmacophore, two-dimensional (2D), and three-dimensional (3D) quantitative structure-activity relationship (QSAR) modeling techniques were used to develop and test models capable of rationalizing and predicting human UDP-glucuronosyltransferase 1A4 (UGT1A4) substrate selectivity and binding affinity (as K(m,app)). The dataset included 24 structurally diverse UGT1A4 substrates, with 18 of these comprising the training set and 6 an external prediction set. A common features pharmacophore was generated with the program Catalyst after overlapping the sites of conjugation using a novel, user-defined "glucuronidation" feature. Pharmacophore-based 3D-QSAR (r(2) = 0.88) and molecular-field-based 3D-QSAR (r(2) = 0.73) models were developed using Catalyst and self-organizing molecular field analysis (SOMFA) software, respectively. In addition, a 2D-QSAR (r(2) = 0.80, CV r(2) = 0.73) was generated using partial least-squares (PLS) regression and variable selection using an unsupervised forward selection (UFS) algorithm. Both UGT1A4 pharmacophores included two hydrophobic features and the glucuronidation site. The 2D-QSAR showed the best overall predictivity and highlighted the importance of hydrophobicity (as log P) in substrate-enzyme binding.     

Ligand-based Pharmacophore Modeling; Atom-based 3D-QSAR Analysis and Molecular Docking Studies of Phosphoinositide-Dependent Kinase-1 Inhibitors

Phosphoinositide-dependent kinase-1 plays a vital role in the PI3-kinase signaling pathway that regulates gene expression, cell cycle growth and proliferation. The common human cancers include lung, breast, blood and prostate possess over stimulation of the phosphoinositide-dependent kinase-1 signaling and making phosphoinositide-dependent kinase-1 an interesting therapeutic target in oncology. A ligand-based pharmacophore and atom-based 3D-QSAR studies were carried out on a set of 82 inhibitors of PDK1. A six point pharmacophore with two hydrogen bond acceptors (A), three hydrogen bond donors (D) and one hydrophobic group (H) was obtained. The pharmacophore hypothesis yielded a 3D-QSAR model with good partial least square statistics results. The training set correlation is characterized by partial least square factors (R² = 0.9557, SD = 0.2334, F = 215.5, P = 1.407e-32). The test set correlation is characterized by partial least square factors (Q² ext = 0.7510, RMSE = 0.5225, Pearson-R =0.8676). The external validation indicated that our QSAR model possess high predictive power with good value of 0.99 and value of 0.88. The docking results show the binding orientations of these inhibitors at active site amino acid residues (Ala162, Thr222, Glu209 and Glu166) of phosphoinositide-dependent kinase-1 protein. The binding free energy interactions of protein-ligand complex have been calculated, which plays an important role in molecular recognition and drug design approach.     

3D-QSAR studies of some tetrasubstituted pyrazoles as COX-II inhibitors

Pharmacophore mapping studies were undertaken for a series of molecules belonging to tetrasubstituted pyrazoles as canine COX-II inhibitors. A six point pharmacophore with 3 hydrogen bond acceptors (A), one hydrophobic group (H) and two aromatic rings (R) as pharmacophoric feature was developed. The pharmacophoric hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of r2 = 0.958. The developed pharmacophore model was externally validated by predicting the activity of test set molecules. The squared predictive correlation coefficient of 0.852 was observed between experimental and predicted activity values of test set molecules. The geometry and features of pharmacophore model describe the key structure-activity relationship of COX-II inhibitors, can predict their activities, and can thus be used to design novel inhibitors.     

Generation of ligand conformations in continuum solvent consistent with protein active site topology: application to thrombin

Using the crystal structure of an inhibitor complexed with the serine protease thrombin (PDB code ) and the functional group definitions contained within the Catalyst software, a representation of the enzyme's active site was produced (structure-based pharmacophore model). A training set of 16 homologous non-peptide inhibitors whose conformations had been generated in continuum solvent (MacroModel) and clustered into conformational families (XCluster) was regressed against this pharmacophore so as to obtain a 3D-QSAR model. To test the robustness of the resulting QSAR model, the synthesis of a series of non-peptide thrombin inhibitors based on arylsuphonyl derivatives of an aminophenol ring linked to a pyridyl-based S1 binding group was undertaken. These compounds served as a test set (20-24). The crystal structure for the novel symmetrical disulfonyl compound 24, in complex with thrombin, has been solved. Its calculated binding mode is in general agreement with the crystallographically observed one, and the predicted K(i) value is in close accord with the experimental value.     

Pharmacophore generation and atom-based 3D-QSAR of

ABSTRACT: BACKGROUND: Coronary heart disease continues to be the leading cause of mortality and a significant cause of morbidity and account for nearly 30% of all deaths each year worldwide. High levels of cholesterol are an important risk factor for coronary heart disease. The blockage of 3-hydroxy-3-methylglutaryl-coenzyme (HMG-CoA) reductase activity by small molecule inhibitors has been shown to inhibit hypercholesterolemia. METHODS: This article describes the development of a robust pharmacophore model and the investigation of structure activity relationship analysis of 43 N-iso-propyl pyrrole-based derivatives reported for HMG-CoA reductase inhibition. A five point pharmacophore model was developed and the generated pharmacophore model was used to derive a predictive atom-based 3D-QSAR model for the studied dataset. RESULTS: The obtained 3D-QSAR model has an excellent correlation coefficient value (r2 = 0.9566) along with good statistical significance as shown by high Fisher ratio (F = 143.2). The model also exhibited good predictive power confirmed by the high value of cross validated correlation coefficient (q2 = 0.6719). CONCLUSIONS: The QSAR model suggests that electron withdrawing character is crucial for the HMG-CoA reductase inhibitory activity. In addition to the electron withdrawing character, hydrogen bond donating groups, hydrophobic and negative ionic groups positively contribute to the HMG-CoA reductase inhibition. These findings provide a set of guidelines for designing compounds with better HMG-CoA reductase inhibitory potential.