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 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.     

Atom based 3D-QSAR study of 1,4-benzodiazepine-2-ones as potent anti-trypanosomal agents and its validation

In the investigation, a robust pharmacophore model to investigate structure–activity relationship of 1,4-benzodiazepine-2-ones derivatives was developed for human African trypanosomiasis (HAT) using PHASE module of Schrodinger software. The pharmacophore model consists of five contours such as two aromatic rings (R), one hydrophobic hydrogen (H) and two hydrogen-bond acceptor (A) with discrete geometries. The generated pharmacophore model was used to derive a predictive atom-based three-dimensional quantitative structure–activity relationship (3D-QSAR) model for the studied data set. The obtained 3D-QSAR model has an excellent correlation coefficient value (R ² = 0.97) along with good statistical significance as shown by high Fisher ratio (F = 216.80). The model also exhibits good predictive power confirmed by the high value of cross-validated correlation coefficient (Q ² = 0.80). To confirm the validity of the model further calculation of the enrichment factor and percentage recovery studies were calculated, which confirm the validity of the model. The findings of the QSAR study provide a set of guidelines for designing compounds with better HAT inhibitory activity.     

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.     

Pharmacophore modeling studies on N-hydroxyphenyl acrylamides and N-hydroxypyridin-2-yl-acrylamides as inhibitor of human cancer leukemia K562 cells

In order to understand the essential structural features for inhibitors of human cancer leukemia K562 cells, three-dimensional pharmacophore hypotheses were built on the basis of a set of inhibitors of human cancer leukemia K562 selected from literature using PHASE program. Five point pharmacophore with two hydrogen bond acceptor (A), one hydrogen bond donor (D), and two aromatic rings (R) as pharmacophoric features were developed. Among them, the pharmacophore hypothesis AADRR 62 yielded a statistically significant 3D-QSAR model with as R ² value 0.883 and Q ² value 0.528 and was considered to be the best pharmacophore hypothesis. The developed pharmacophore model was externally validated by predicting the activity of test set molecules. The squared predictive correlation coefficient of 0.765 was observed between experimental and predicted activity values of test set molecules.     

2D, 3D-QSAR,and pharmacophore studies on thiazolidine-4-carboxylic acid derivatives as neuraminidase inhibitors in H3N2 influenza virus

Thiazolidine-4-carboxylic acid derivative compounds with their inhibitory activity against influenza A neuraminidase (NA) were used as a data set for developing the quantitative structure–activity relationship (QSAR) model. The 2D-QSAR model was developed using multiple linear regression analysis with r ² and r ² (CV) value of 0.98 and 0.70, respectively. The generated QSAR model has shown that the electrostatic and steric properties have the predominant influence on biological activity. 3D-QSAR was modeled with r ² value of 0.98 and RMSD of 0.12. The pharmacophore alignments were generated and suggested that the hydrophobic, hydrogen bond donor, and hydrogen bond acceptor features on R¹ and R² site substitutions of the core of thiazolidine are important properties to enhance the activities of molecule against influenza A NA.     

Computation of pharmacophore models for the prediction of mitogen-activated protein kinase activated protein kinase-2 inhibitory activity of pyrrolopyridines

This article is an attempt to formulate the three-dimensional pharmacophore modelling of pyrrolopyridine derivatives inhibiting mitogen-activated protein kinase activated protein kinase-2 (MK₂). To understand the essential structural features for MK₂ inhibitors, pharmacophore hypothesis were built on the basis of a set of known MK₂ inhibitors selected from literature using PHASE program. Three pharmacophore models with one hydrogen-bond acceptor (A), two hydrogen-bond donors (D), one hydrophobic group (H) and one aromatic ring (R) as pharmacophoric features were developed. Amongst them the pharmacophore hypothesis ADDHR1 yielded a statistically significant 3D-QSAR model with 0.926 as R ² value and was considered to be the best pharmacophore hypothesis. The developed pharmacophore model was externally validated by predicting the activity of test set molecules. The squared predictive correlation coefficient of 0.882 was observed between experimental and predicted activity values of test set molecules. The geometry and features of pharmacophore was expected to be useful for the design of selective MK₂ inhibitors.     

Prospective atom-based 3D-QSAR model prediction,pharmacophore generation,and molecular docking study of carbamate derivatives as dual inhibitors of AChE and MAO-B for Alzheimer’s disease

Carbamates are well known for AChE as well as MAO inhibition. In this study, atom-based 3D-QSAR model generation, virtual screening, and molecular docking studies were performed for a known series of 31 carbamate derivatives. The best hypothesis yielded four different pharmacophoric features with statistically significant 3D-QSAR model (correlation coefficient of R ² = 0.994 for training set molecules and very good predictive powers with Q ² and Pearson-R were 0.60 and 0.91, respectively). By virtual screening done against Schrödinger database, we identified 11 distinct drug-like molecules binding to both targets AChE and MAO-B efficiently. This generated 3D-QSAR hybrid model for dual enzymes provides basis for new structural scaffold would serve as building blocks in designing drug-like molecules for Alzheimer’s disease.     

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.     

Molecular modelling studies on flavonoid derivatives as dual site inhibitors of human acetyl cholinesterase using 3D-QSAR,pharmacophore and high throughput screening approaches

Alzheimer’s disease (AD) is a multi-factorial neurodegenerative disease that affects millions of elderly people worldwide. Due to its massive occurrence and severity, there is continuing and compelling need for the development of novel and effective drugs for improved treatment of AD. Since AD is characterized by the deficiency in cholinergic neurotransmission, acetyl cholinesterase (AChE) has been considered as a promising drug target. Herein we triggered our effort to design novel and potential inhibitors of AChE using a set of 24 flavonoid compounds having inhibitory activity against AChE. We carried out 3D-QSAR-based and pharmacophore-based identification of novel natural lead candidates. The 3D-QSAR model obtained using partial least square regression showed satisfactory parametric values (r ² = 0.8227, q ² = 0.6833 and pred-r ² = 0.7893). Amongst total 14 pharmacophore hypothesis generated the one possessing following five features: one hydrogen bond acceptor, two hydrophobic regions and two aromatic rings, was considered to be the best pharmacophore hypothesis. Above-described robust and validated 3D-QSAR and pharmacophore models were used for carrying out prospective generic prediction and virtual screening on large natural compound libraries. The screened molecules from both the approaches were subjected for further docking analysis to reveal the binding modes of actions of these ligands. All the ligands were found to bind with both catalytic and anionic subsite of AChE. The molecular insights obtained from this study will be of high value for design and development of novel drugs for AD, possessing improved binding properties and low toxicity to human.     

Pharmacophore generation and atom-based 3D-QSAR of N-iso-propyl pyrrole-based derivatives as HMG-CoA reductase inhibitors

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 A (HMG-CoA) reductase activity by small molecule inhibitors has been shown to inhibit hypercholesterolemia. Herein, we describe the development of effective and robust pharmacophore model and the structure–activity relationship studies of 43N-iso-propyl pyrrole-based derivatives previously reported for HMG-CoA reductase inhibition.

Results

A 5-point pharmacophore model was developed and the generated pharmacophore model was used to derive a predictive atom-based 3D quantitative structure–activity relationship analysis (3D-QSAR) model for the studied dataset. The obtained 3D-QSAR model has an excellent correlation coefficient value (r ²?=?0.96) 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 (q ²?=?0.672). Further, pharmacophoric model was employed for virtual screening to identify four potential HMG-CoA reductase inhibitors.

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.     

A combined approach based on 3D pharmacophore and docking for identification of new aurora A kinase inhibitors

Aurora kinase A is involved in multiple mitotic events in cell cycle and has been identified as a major regulator of centrosome function in mitosis. Aurora A has been found to be over-expressed in many tumor types including breast, lung, colon, ovarian, pancreatic and glial cells. Thus, inhibition of aurora A can be a potential target in oncology. A five-point pharmacophore was generated using PHASE for a set of aurora A inhibitors reported in literature. The generated pharmacophore yielded statistically significant 3D-QSAR model, with a correlation coefficient r ² of 0.936 and q ² of 0.703. The pharmacophore indicated that presence of two aromatic ring features (R), two acceptor features (A) and one donor feature (D) is necessary for potent inhibitory activity. The database screening was done initially by use of pharmacophore followed by an interaction-based selection using docking. Twelve hits with satisfactory pharmacokinetic properties have been identified.     

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.     

3D-QSAR of amino-substituted pyrido[3,2B]pyrazinones as PDE-5 inhibitors

A 3D-QSAR study on amino-substituted pyrido[3,2b]pyrazinones as PDE-5 inhibitors was successfully performed by means of pharmacophore mapping using PHASE module of Schrödinger-9. The 3D-QSAR obtained from AADHRR-183 hypothesis was found to be statistically good with r ² = 0.95 and q ² = 0.81 taking PLS factor 4. The statistical significance of the model was also confirmed by a high value of Fisher ratio of 85.1 and a very low value of RMSE 0.29. One of the other parameters which signify the model predictivity is Pearson R. Its value of 0.91 shows that the correlation between predicted and observed activities for the test set compounds is excellent. Hydrophobic groups are important for PDE-5 inhibition while H-bond donor groups are less favorable for the same. Electron withdrawing groups are favorable if include at ring A in the structures while unfavorable at other sites. Thus, it can be assumed that the present QSAR analysis is enough to demonstrate PDE-5 inhibition with the help of AADHRR-183 hypothesis and will help in designing novel and potent PDE-5 inhibitors.     

Three-dimensional quantitative structure activity relationship analysis of anilinoquinazolines for c-Src kinase inhibition

Three-dimensional quantitative structure activity relationship (3D-QSAR) models was developed using molecular field analysis (MFA) for 36 anilinoquinazoline derivatives, inhibiting c-Src kinase. The QSAR model was developed using 29 compounds and its predictive ability was assessed using a test set of seven compounds. The predictive 3D-QSAR model has conventional r ² values of 0.961 while the cross-validated coefficient q ² and bootstrap correlation coefficient r _BS² values of 0.910 and 0.957, respectively. The developed model provides a powerful tool to design potent c-Src inhibitors as novel antitumor agents. Six new inhibitors were designed and their pIC₅₀ were predicted.     

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.     

Receptor and ligand-based 3D-QSAR study on a series of pyrazines/pyrrolidylquinazolines as inhibitors of PDE10A enzyme

The 3D-QSAR analysis was performed on the set of 175 potent inhibitors of the PDE10A enzyme. Four separate models were built based on different conformations and superimposition methods. They were generated following next criteria: (I) database alignment based on the conformations most similar to the co-crystalized ligand conformation, (II) database alignment based on the minimum energy conformations, (III) docking alignment based on the docked conformations, and (IV) database alignment based on the docking conformations. The best CoMFA and CoMSIA models, derived from superimposition III, show leave-one-out cross-validated correlation coefficient (q ²) values of 0.673 and 0.707 as well as the non-cross-validated correlation coefficient (r ²) values of 0.936 and 0.924, respectively. In addition, the satisfactory results, based on the bootstrapping analysis and 10- and 50-fold cross-validation, further indicate the highly statistical significance of the models. The external predictive abilities of these models were evaluated using a prediction set of 35 compounds, producing the predicted correlation coefficients. Results were graphically interpreted in terms of field contribution maps. A DISCOtech pharmacophore model was also constructed to light important structural features that could be responsible for the low- or high-inhibition activity.     

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.