scholarly journals Exploration of Novel Xanthine Oxidase Inhibitors Based on 1,6-Dihydropyrimidine-5-Carboxylic Acids by an Integrated in Silico Study

2021 ◽  
Vol 22 (15) ◽  
pp. 8122
Author(s):  
Na Zhai ◽  
Chenchen Wang ◽  
Fengshou Wu ◽  
Liwei Xiong ◽  
Xiaogang Luo ◽  
...  
Keyword(s):  
Xanthine Oxidase ◽  
Carboxylic Acids ◽  
Hydrophobic Interactions ◽  
Pharmacophore Model ◽  
3D Qsar ◽  
Md Simulations ◽  
Pharmacophore Modeling ◽  
Binding Modes ◽  
Comfa Model ◽  
Validation Parameters

Xanthine oxidase (XO) is an important target for the effective treatment of hyperuricemia-associated diseases. A series of novel 2-substituted 6-oxo-1,6-dihydropyrimidine-5-carboxylic acids (ODCs) as XO inhibitors (XOIs) with remarkable activities have been reported recently. To better understand the key pharmacological characteristics of these XOIs and explore more hit compounds, in the present study, the three-dimensional quantitative structure–activity relationship (3D-QSAR), molecular docking, pharmacophore modeling, and molecular dynamics (MD) studies were performed on 46 ODCs. The constructed 3D-QSAR models exhibited reliable predictability with satisfactory validation parameters, including q2 = 0.897, R2 = 0.983, rpred2 = 0.948 in a CoMFA model, and q2 = 0.922, R2 = 0.990, rpred2 = 0.840 in a CoMSIA model. Docking and MD simulations further gave insights into the binding modes of these ODCs with the XO protein. The results indicated that key residues Glu802, Arg880, Asn768, Thr1010, Phe914, and Phe1009 could interact with ODCs by hydrogen bonds, π-π stackings, or hydrophobic interactions, which might be significant for the activity of these XOIs. Four potential hits were virtually screened out using the constructed pharmacophore model in combination with molecular dockings and ADME predictions. The four hits were also found to be relatively stable in the binding pocket by MD simulations. The results in this study might provide effective information for the design and development of novel XOIs.

scholarly journals In Silico Screening of Novel α1-GABAA Receptor PAMs towards Schizophrenia Based on Combined Modeling Studies of Imidazo [1,2-a]-Pyridines

2021 ◽  
Vol 22 (17) ◽  
pp. 9645
Author(s):  
Xiaojiao Zheng ◽  
Chenchen Wang ◽  
Na Zhai ◽  
Xiaogang Luo ◽  
Genyan Liu ◽  
...  
Keyword(s):  
Gabaa Receptor ◽  
Rational Design ◽  
Hydrophobic Interactions ◽  
Three Dimensional ◽  
Pharmacophore Model ◽  
3D Qsar ◽  
Md Simulations ◽  
Binding Pocket ◽  
Pharmacophore Modeling ◽  
Topomer Search

The ionotropic GABAA receptor (GABAAR) has been proven to be an important target of atypical antipsychotics. A novel series of imidazo [1,2-a]-pyridine derivatives, as selective positive allosteric modulators (PAMs) of α1-containing GABAARs with potent antipsychotic activities, have been reported recently. To better clarify the pharmacological essentiality of these PAMs and explore novel antipsychotics hits, three-dimensional quantitative structure–activity relationships (3D-QSAR), molecular docking, pharmacophore modeling, and molecular dynamics (MD) were performed on 33 imidazo [1,2-a]-pyridines. The constructed 3D-QSAR models exhibited good predictive abilities. The dockings results and MD simulations demonstrated that hydrogen bonds, π–π stackings, and hydrophobic interactions play essential roles in the binding of these novel PAMs in the GABAAR binding pocket. Four hit compounds (DS01–04) were then screened out by the combination of the constructed models and computations, including the pharmacophore model, Topomer Search, molecular dockings, ADME/T predictions, and MD simulations. The compounds DS03 and DS04, with higher docking scores and better predicted activities, were also found to be relatively stable in the binding pocket by MD simulations. These results might provide a significant theoretical direction or information for the rational design and development of novel α1-GABAAR PAMs with antipsychotic activities.

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 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 Discovery of Novel Acetylcholinesterase Inhibitors as Potential Candidates for the Treatment of Alzheimer’s Disease

2019 ◽  
Vol 20 (4) ◽  
pp. 1000 ◽  
Author(s):  
Minky Son ◽  
Chanin Park ◽  
Shailima Rampogu ◽  
Amir Zeb ◽  
Keun Lee
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Active Site ◽  
Neurodegenerative Disorder ◽  
3D Qsar ◽  
Pharmacophore Modeling ◽  
Huperzine A ◽  
Potential Candidate ◽  
Binding Modes ◽  
Ache Inhibitors

Acetylcholinesterase (AChE) catalyzes the hydrolysis of neurotransmitter acetylcholine to acetate and choline in a synaptic cleft. Deficits in cholinergic neurotransmitters are linked closely with the progression of Alzheimer’s disease (AD), which is a neurodegenerative disorder characterized by memory impairment, and a disordered cognitive function. Since the previously approved AChE inhibitors, donepezil (Aricept), galantamine (Reminyl), and rivastigmine (Exelon), have side effects and several studies are being carried out out to develop novel AD drugs, we have applied a three-dimensional quantitative structure−activity relationship (3D QSAR) and structure-based pharmacophore modeling methodologies to identify potential candidate inhibitors against AChE. Herein, 3D QSAR and structure-based pharmacophore models were built from known inhibitors and crystal structures of human AChE in complex with donepezil, galantamine, huperzine A, and huprine W, respectively. The generated models were used as 3D queries to screen new scaffolds from various chemical databases. The hit compounds obtained from the virtual screening were subjected to an assessment of drug-like properties, followed by molecular docking. The final hit compounds were selected based on binding modes and molecular interactions in the active site of the enzyme. Furthermore, molecular dynamics simulations for AChE in complex with the final hits were performed to evaluate that they maintained stable interactions with the active site residues. The binding free energies of the final hits were also calculated using molecular mechanics/Poisson-Boltzmann surface area method. Taken together, we proposed that these hits can be promising candidates for anti-AD drugs.

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.

scholarly journals Exploration of the structural requirements of Aurora Kinase B inhibitors by a combined QSAR, modelling and molecular simulation approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sajda Ashraf ◽  
Kara E. Ranaghan ◽  
Christopher J. Woods ◽  
Adrian J. Mulholland ◽  
Zaheer Ul-Haq
Keyword(s):  
Binding Free Energy ◽  
Pharmacophore Model ◽  
3D Qsar ◽  
Aurora Kinase ◽  
Md Simulations ◽  
Structural Features ◽  
Free Energy Calculations ◽  
Active Site Residues ◽  
Qsar Analysis ◽  
Aurora Kinase B

AbstractAurora kinase B plays an important role in the cell cycle to orchestrate the mitotic process. The amplification and overexpression of this kinase have been implicated in several human malignancies. Therefore, Aurora kinase B is a potential drug target for anticancer therapies. Here, we combine atom-based 3D-QSAR analysis and pharmacophore model generation to identify the principal structural features of acylureidoindolin derivatives that could potentially be responsible for the inhibition of Aurora kinase B. The selected CoMFA and CoMSIA model showed significant results with cross-validation values (q2) of 0.68, 0.641 and linear regression values (r2) of 0.971, 0.933 respectively. These values support the statistical reliability of our model. A pharmacophore model was also generated, incorporating features of reported crystal complex structures of Aurora kinase B. The pharmacophore model was used to screen commercial databases to retrieve potential lead candidates. The resulting hits were analyzed at each stage for diversity based on the pharmacophore model, followed by molecular docking and filtering based on their interaction with active site residues and 3D-QSAR predictions. Subsequently, MD simulations and binding free energy calculations were performed to test the predictions and to characterize interactions at the molecular level. The results suggested that the identified compounds retained the interactions with binding residues. Binding energy decomposition identified residues Glu155, Trp156 and Ala157 of site B and Leu83 and Leu207 of site C as major contributors to binding affinity, complementary to 3D-QSAR results. To best of our knowledge, this is the first comparison of WaterSwap field and 3D-QSAR maps. Overall, this integrated strategy provides a basis for the development of new and potential AK-B inhibitors and is applicable to other protein targets.

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.

scholarly journals Molecular Modeling and Design Studies of Purine Derivatives as Novel CDK2 Inhibitors

Molecules ◽  
2018 ◽  
Vol 23 (11) ◽  
pp. 2924 ◽  
Author(s):  
Gaomin Zhang ◽  
Yujie Ren
Keyword(s):  
Molecular Docking ◽  
Molecular Modeling ◽  
Virtual Screening ◽  
Interaction Mechanism ◽  
3D Qsar ◽  
Md Simulations ◽  
Qsar Model ◽  
Purine Derivatives ◽  
Hydrogen Bond Donor ◽  
Comfa Model

Cyclin-dependent kinase 2 (CDK2) is a potential target for treating cancer. Purine heterocycles have attracted particular attention as the scaffolds for the development of CDK2 inhibitors. To explore the interaction mechanism and the structure–activity relationship (SAR) and to design novel candidate compounds as potential CDK2 inhibitors, a systematic molecular modeling study was conducted on 35 purine derivatives as CDK2 inhibitors by combining three-dimensional quantitative SAR (3D-QSAR), virtual screening, molecular docking, and molecular dynamics (MD) simulations. The predictive CoMFA model (q2 = 0.743, r pred 2 = 0.991), the CoMSIA model (q2 = 0.808, r pred 2 = 0.990), and the Topomer CoMFA model (q2 = 0.779, r pred 2 = 0.962) were obtained. Contour maps revealed that the electrostatic, hydrophobic, hydrogen bond donor and steric fields played key roles in the QSAR models. Thirty-one novel candidate compounds with suitable predicted activity (predicted pIC50 > 8) were designed by using the results of virtual screening. Molecular docking indicated that residues Asp86, Glu81, Leu83, Lys89, Lys33, and Gln131 formed hydrogen bonds with the ligand, which affected activity of the ligand. Based on the QSAR model prediction and molecular docking, two candidate compounds, I13 and I60 (predicted pIC50 > 8, docking score > 10), with the most potential research value were further screened out. MD simulations of the corresponding complexes of these two candidate compounds further verified their stability. This study provided valuable information for the development of new potential CDK2 inhibitors.

Pharmacophore modeling and 3D-QSAR studies of 2,4-disubstituted pyrimidine derivatives as Janus kinase 3 inhibitors

2020 ◽  
Vol 19 (01) ◽  
pp. 2050001
Author(s):  
Neetu Agrawal
Keyword(s):  
Statistical Significance ◽  
Three Dimensional ◽  
Pharmacophore Model ◽  
3D Qsar ◽  
Pharmacophore Modeling ◽  
Janus Kinase ◽  
Hydrogen Bond Donor ◽  
Pyrimidine Derivatives ◽  
Qsar Studies ◽  
Janus Kinase 3

A robust pharmacophore model was developed and the structure-activity relationship was analyzed using 71 pyrimidine derivatives reported for covalent Janus Kinase 3 (JAK3) inhibition. Pharmacophore modeling developed a five featured pharmacophore: one H-bond acceptor, two H-bond donors, one hydrophobic, and one aromatic ring features. The atom-based three-dimensional QSAR models with statistical significance were generated using the training set of 52 compounds. The excellent predictive correlation coefficients were obtained for 3D models determined using a test set of 19 molecules. The generated QSAR model implies that the hydrophobic character is important for the JAK3 inhibitory activity of these compounds. Additionally, electron-withdrawing and hydrogen bond donor groups at specific positions positively contribute to the JAK3 inhibition potency. These results provided essential three-dimensional structural requirements and the crucial binding features of 2,4-disubstituted pyrimidine derivatives, which may direct for the design and discovery of novel potent JAK3 inhibitors.

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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