scholarly journals Ligand Based Pharmacophore Modeling and Virtual Screening Studies to Design Novel HDAC2 Inhibitors

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Naresh Kandakatla ◽  
Geetha Ramakrishnan
Keyword(s):  
Virtual Screening ◽  
Histone Deacetylases ◽  
Pharmacophore Model ◽  
3D Qsar ◽  
Pharmacophore Modeling ◽  
Hydrogen Bond Acceptor ◽  
Lipinski’S Rule ◽  
Discovery Studio ◽  
Pharmacophore Generation ◽  
Important Therapeutic Target

Histone deacetylases 2 (HDAC2), Class I histone deacetylase (HDAC) family, emerged as an important therapeutic target for the treatment of various cancers. A total of 48 inhibitors of two different chemotypes were used to generate pharmacophore model using 3D QSAR pharmacophore generation (HypoGen algorithm) module in Discovery Studio. The best HypoGen model consists of four pharmacophore features namely, one hydrogen bond acceptor (HBA), and one hydrogen donor (HBD), one hydrophobic (HYP) and one aromatic centres, (RA). This model was validated against 20 test set compounds and this model was utilized as a 3D query for virtual screening to validate against NCI and Maybridge database and the hits further screened by Lipinski’s rule of 5, and a total of 382 hit compounds from NCI and 243 hit compounds from Maybridge were found and were subjected to molecular docking in the active site of HDAC2 (PDB: 3MAX). Finally eight hit compounds, NSC108392, NSC127064, NSC110782, and NSC748337 from NCI database and MFCD01935795, MFCD00830779, MFCD00661790, and MFCD00124221 from Maybridge database, were considered as novel potential HDAC2 inhibitors.

scholarly journals Ligand Based 3D-QSAR Pharmacophore, Molecular Docking and Adme to Identify Potential Fibroblast Growth Factor Receptor 1 Inhibitors 

2020 ◽  
Author(s):  
Zizhong Tang ◽  
Lu Huang ◽  
Xiaoli Fu ◽  
Haoxiang Wang ◽  
Biao Tang ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Virtual Screening ◽  
Growth Factor Receptor ◽  
Pharmacophore Model ◽  
3D Qsar ◽  
Drug Carriers ◽  
Therapeutic Effects ◽  
Lipinski’S Rule ◽  
Discovery Studio ◽  
Zinc Database

Abstract The FGF/FGFR system may affect tumor cells and stromal microenvironment through autocrine and paracrine stimulation, thereby significantly promoting oncogene transformation and tumor growth. Abnormal expression of FGFR1 in cells is considered to be the main cause of tumorigenesis and a potential target for the treatment of cancer. In this study, a combination of structure-based drug carriers and molecular docking-based virtual screening was used to screen new potential FGFR1 inhibitors. Twenty-one known inhibitors were collected as training sets to establish a 3D-QSAR pharmacophore model, and cost analysis, test set validation, and Fischer randomization test were used to validate the efficiency of the pharmacophore model. In Accelrys Discovery Studio 2016, the zinc database was filtered by Lipinski's Rule of Five and SMART's filtration. Then, Hypo01 was used for virtual screening of ZINC database. Compounds with predicted activity values less than 1 μM were molecularly docked with FGFR1 protein crystals, the docking results were observed, and the interaction between compounds and targets was studied. The absorption, distribution, metabolism and excretion (ADME) and toxicity of potential inhibitors were studied, and a compound with new structural scaffolds were obtained. It could be further studied to explore their better therapeutic effects.

scholarly journals Ligand Based Pharmacophore Modeling, Virtual Screening, and Molecular Docking Studies of Asymmetrical Hexahydro-2H-Indazole Analogs of Curcumin (AIACs) to Discover Novel Estrogen Receptors Alpha (ERα) Inhibitor

2020 ◽  
Vol 21 (1) ◽  
pp. 137
Author(s):  
Hariyanti Hariyanti ◽  
Kusmadi Kurmardi ◽  
Arry Yanuar ◽  
Hayun Hayun
Keyword(s):  
Molecular Docking ◽  
Virtual Screening ◽  
Estrogen Receptor Alpha ◽  
Pharmacophore Model ◽  
Pharmacophore Modeling ◽  
Cytotoxic Agents ◽  
Breast Development ◽  
Hydrogen Bond Acceptor ◽  
Lipinski’S Rule ◽  
Pharmacophore Models

The estrogen receptor alpha (ERα) plays an important role in breast development and pro-proliferation signal activation in the normal and cancerous breast. The ERα inhibitors were potentially active as cytotoxic agents against breast cancer. This study was conducted in order to find Asymmetrical Hexahydro-2H-Indazole Analogs of Curcumin (AIACs) as hits of ERα inhibitor. A training set of 17 selected ERα inhibitors was used to create 10 pharmacophore models using LigandScout 4.2. The pharmacophore models were validated using 383 active compounds as positive data and 20674 decoys as negative data obtained from DUD.E. Model 2 was found as the best pharmacophore model and consisted of three types of pharmacophore features, viz. one hydrophobic, one hydrogen bond acceptor, and aromatic interactions. Model 2 was utilized for ligand-based virtual screening 186 of AIACs, AMACs, intermediates, and Mannich base derivative compounds. The hits obtained were further screened using molecular docking, analyzed using drug scan, and tested for its synthesis accessibility. Fourteen compounds were fulfilled as hits in pharmacophore modeling, in which 10 hits were selected by molecular docking, but only seven hits met Lipinski’s rule of five and had medium synthesis accessibility. In conclusion, seven compounds were suggested to be potentially active as ERα inhibitors and deserve to be synthesized and further investigated.

scholarly journals COMBINATORIAL PHARMACOPHORE MODELING AND ATOM BASED 3D QSAR STUDIES OF BENZOTHIADIAZINES AS HCV-NS5B INHIBITORS

2018 ◽  
Vol 10 (3) ◽  
pp. 43
Author(s):  
Prasanthi Polamreddy ◽  
Vinita Vishwakarma ◽  
Manoj Kumar Mahto
Keyword(s):  
Virtual Screening ◽  
Correlation Coefficient ◽  
Current Model ◽  
Pharmacophore Model ◽  
3D Qsar ◽  
Qsar Model ◽  
Pharmacophore Modeling ◽  
Structural Protein ◽  
Pearson's R ◽  
Pearson’S R

Objective: The objective of the current study was to elucidate the 3D pharmacophoric features of benzothiadiazine derivatives that are crucial for inhibiting Hepatitis C virus (HCV) Non-structural protein 5B (NS5B) and quantifying the features by building an atom based 3D quantitative structure-activity relationship (3D QSAR) model.Methods: Generation of QSAR model was carried out using PHASE 3.3.Results: A five-point pharmacophore model with two hydrogen bond acceptors, one negative ionization potential and two aromatic rings (AANRR) was found to be common among a maximum number of benzothiadiazine based NS5B inhibitors. A statistically significant 3D QSAR model was obtained from AANRR.6 which had correlation-coefficient (R2) value of 0.924, cross-validated correlation-coefficient (Q2) of 0.774, high Fisher ratio of 138 and low root mean square standard error (RMSE=0.29). There is another parameter, Pearson’s R, its value emphasizes correlation between predicted and observed activities of the test set. For the current model, Pearson’s R-value is 0.90, hence underlining the good quality of the model. The present study suggests that nitrogen atom of benzothiadiazine sulfamide ring, oxyacetamide group attached to C7 carbon of benzothiadiazine and sulfonamide oxygens are crucial for NS5B inhibitory activity. Prediction of activities of hit drugs generated in earlier research suggests that Aprepitant (Phase predicted activity: 6.9) could be a potential NS5B inhibitor.Conclusion: This 3D QSAR model developed was statistically good and can be used to predict the activities of newly designed NS5B inhibitors and virtual screening as well. Predict the activities of newly designed NS5B inhibitors and virtual screening as well.

scholarly journals VIRTUAL SCREENING, PHARMACOPHORE MODELING, AND QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP STUDIES ON HISTAMINE 4 RECEPTOR

2017 ◽  
Vol 10 (12) ◽  
pp. 150
Author(s):  
Shobana Sugumar
Keyword(s):  
Virtual Screening ◽  
3D Qsar ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Pharmacophore Modeling ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Hydrogen Bond Acceptor ◽  
Structure Activity

  Objective: To find out novel inhibitors for histamine 4 receptor (H4R), the target for various allergic and inflammatory pathophysiological conditions.Methods: Homology modeling of H4R was performed using easy modeler and validated using structure analysis and verification server, and with the modeled structure, virtual screening, pharmacophore modeling, and quantitative structure activity relationship (QSAR) studies were performed using the Schrodinger 9.3 software.Results: Among all the synthetic and natural ligands, hesperidin, vitexin, and diosmin were found to have the highest dock score, and with that, a five-point pharmacophore model was developed consisting of two hydrogen bond acceptor and three ring atoms, and the pharmacophore hypothesis yielded a statistically significant three-dimensional QSAR (3D-QSAR) model with a correlation coefficient of r2=0.8962 as well as good predictive power.Conclusion: The pharmacophore-based 3D-QSAR model generated from natural antihistamines can provide intricate structural knowledge about a new class of anti-allergic and anti-inflammatory drug research.

Pharmacophore modeling, 3D-QSAR and molecular docking studies of quinazolines and aminopyridines as selective inhibitors of inducible nitric oxide synthase

2019 ◽  
Vol 18 (01) ◽  
pp. 1950002
Author(s):  
Anshika Mittal ◽  
Ritu Arora ◽  
Rita Kakkar
Keyword(s):  
Nitric Oxide ◽  
Nitric Oxide Synthase ◽  
Inducible Nitric Oxide Synthase ◽  
Pharmacophore Model ◽  
3D Qsar ◽  
Pharmacophore Modeling ◽  
Docking Studies ◽  
Hydrogen Bond Acceptor ◽  
Oxide Synthase ◽  
Inducible Nitric Oxide

Pharmacophore modeling and 3D-Quantitative Structure Activity Relationship (3D-QSAR) studies have been performed on a dataset of thirty-two quinazoline and aminopyridine derivatives to get an insight into the important structural features required for binding to inducible nitric oxide synthase (iNOS). A four-point CPH (Common Pharmacophore Hypothesis), AHPR.29, with a hydrogen bond acceptor, hydrophobic group, positively charged ionizable group and an aromatic ring, has been obtained as the best pharmacophore model. Satisfactory statistical parameters of correlation ([Formula: see text]) and cross-validated ([Formula: see text]) correlation coefficients, 0.9288 and 0.6353, respectively, show high robustness and good predictive ability of our selected model. The contour maps have been developed from this model and the analysis has provided an interpretable explanation of the effect that various features and substituents have on the potency and selectivity of inhibitors towards iNOS. Docking studies have also been performed in order to analyze the interactions between the enzyme and the inhibitors. Our proposed model can thus be further used for screening a large database of compounds and design new iNOS inhibitors.

Pharmacophore generation and atom based 3D-QSAR of quinoline derivatives as selective phosphodiesterase 4B inhibitors

RSC Advances ◽  
2016 ◽  
Vol 6 (79) ◽  
pp. 75805-75819 ◽  
Author(s):  
Vidushi Sharma ◽  
Hirdesh Kumar ◽  
Sharad Wakode
Keyword(s):  
Virtual Screening ◽  
Pharmacophore Model ◽  
3D Qsar ◽  
Quinoline Derivatives ◽  
Cross Docking ◽  
Pharmacophore Generation ◽  
Phosphodiesterase 4B ◽  
Specific Inhibitors

Reported PDE4B inhibitors were used to design QSAR based pharmacophore model. Using developed pharmacophore model, virtual screening was performed followed by cross-docking to identify novel PDE4B specific inhibitors.

scholarly journals Prediction of Novel Anoctamin1 (ANO1) Inhibitors Using 3D-QSAR Pharmacophore Modeling and Molecular Docking

2018 ◽  
Vol 19 (10) ◽  
pp. 3204 ◽  
Author(s):  
Yoon Lee ◽  
Gwan-Su Yi
Keyword(s):  
Hydrogen Bond ◽  
Molecular Docking ◽  
Correlation Coefficient ◽  
High Throughput Screening ◽  
Interaction Analysis ◽  
Pharmacophore Model ◽  
3D Qsar ◽  
Pharmacophore Modeling ◽  
Hydrogen Bond Acceptor ◽  
Test Set

Recently, anoctamin1 (ANO1), a calcium-activated chloride channel, has been considered an important drug target, due to its involvement in various physiological functions, as well as its possibility for treatment of cancer, pain, diarrhea, hypertension, and asthma. Although several ANO1 inhibitors have been discovered by high-throughput screening, a discovery of new ANO1 inhibitors is still in the early phase, in terms of their potency and specificity. Moreover, there is no computational model to be able to identify a novel lead candidate of ANO1 inhibitor. Therefore, three-dimensional quantitative structure-activity relationship (3D-QSAR) pharmacophore modeling approach was employed for identifying the essential chemical features to be required in the inhibition of ANO1. The pharmacophore hypothesis 2 (Hypo2) was selected as the best model based on the highest correlation coefficient of prediction on the test set (0.909). Hypo2 comprised a hydrogen bond acceptor, a hydrogen bond donor, a hydrophobic, and a ring aromatic feature with good statistics of the total cost (73.604), the correlation coefficient of the training set (0.969), and the root-mean-square deviation (RMSD) value (0.946). Hypo2 was well assessed by the test set, Fischer randomization, and leave-one-out methods. Virtual screening of the ZINC database with Hypo2 retrieved the 580 drug-like candidates with good potency and ADMET properties. Finally, two compounds were selected as novel lead candidates of ANO1 inhibitor, based on the molecular docking score and the interaction analysis. In this study, the best pharmacophore model, Hypo2, with notable predictive ability was successfully generated, and two potential leads of ANO1 inhibitors were identified. We believe that these compounds and the 3D-QSAR pharmacophore model could contribute to discovering novel and potent ANO1 inhibitors in the future.

Structural Investigations of Aroylindole Derivatives through 3D-QSAR and Multiple Pharmacophore Modeling for the Search of Novel Colchicines Inhibitor

2020 ◽  
Vol 17 ◽  
Author(s):  
Vijay K. Patel ◽  
Harish Rajak
Keyword(s):  
Virtual Screening ◽  
Anticancer Agents ◽  
Structural Alignment ◽  
Pharmacophore Model ◽  
3D Qsar ◽  
Qsar Model ◽  
Structural Features ◽  
Pharmacophore Modeling ◽  
Structural Investigations ◽  
Pharmacophore Modelling

Background : The ligand and structure based integrated strategies are being repeatedly and effectively employed for the precise search and design of novel ligands against various disease targets. Aroylindole derivative have a similar structural analogy as Combretastatin A-4, and exhibited potent anticancer activity on several cancer cell lines. Objective: To identify structural features of aroylindole derivatives through 3D-QSAR and multiple pharmacophore modelling for the search of novel colchicines inhibitor via virtual screening. Method: The present study utilizes ligand and structure based methodology for the establishment of structure activity correlation among trimethoxyaroylindole derivatives and search of novel colchicines inhibitor via virtual screening. The 3DQSAR studies were performed using Phase module and provided details of relationship between structure and biological activity. A single ligand based pharmacophore model was generated from Phase on compound 3 and compound 29 and three energetically optimized structure based pharmacophore models were generated from e-pharmacophore for co-crystallized ligand, compound 3 and compound 29 with protein PBD ID 1SA0, 5EYP and 5LYJ. These pharmacophoric features containing hit-like compounds were collected from commercially available ZINC database and screened using virtual screening workflow. Results and Discussion: The 3D-QSAR model studies with good PLSs statistics for factor four was characterized by the best prediction coefficient Q2 (0.8122), regression R2 (0.9405), SD (0.2581), F (102.7), P (1.56e-015), RMSE (0.402), Stability (0.5411) and Pearson-r (0.9397). The generated e-pharmacophores have GH scores over 0.5 and AUAC ≥ 0.7 indicated that all the pharmacophores were suitable for pharmacophore-based virtual screening. The virtual screened compounds ZINC12323179, ZINC01642724, ZINC14238006 have showed similar structural alignment as co-crystallized ligand and showed the hydrogen bonding of ligand with ASN101, SER178, THR179, VAL238, CYS241 amino acid of protein. Conclusion: The study illustrates that the ligand and structure based pharmacophoric approach is beneficial for identification of structurally diverse hits, having better binding affinity on colchicines binding site as novel anticancer agents.

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