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 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 ◽  
Growth Factor Receptor ◽  
Pharmacophore Model ◽  
3D Qsar ◽  
Protein Database ◽  
Hydrogen Bond Interaction ◽  
Stromal Microenvironment ◽  
Good Ability ◽  
Zinc Database ◽  
Crystal Complex

Abstract Background 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.Methods The known inhibitors were collected to construct 3D-QSAR pharmacophore model, which was verified by cost analysis, test set validation and Fischer test. Virtual screening of zinc database based on pharmacophore was carried out. FGFR1 crystal complex was downloaded from the protein database to dock with the compound. Finally, the absorption, distribution, metabolism and excretion (ADME) characteristics and toxicity of a series of potential inhibitors were studied.Results It was found that the constructed pharmacophore had a good ability to predict the activity. 2763 compounds in the database could hit well and the predicted activity value was less than 1 µM. Through molecular docking, we found that six compounds can bind to protein stably and inhibit the activity of FGFR1 through hydrogen bond interaction. In ADME and toxicity studies, we have successfully screened out a compound with a new structure scaffold, and found that it has good oral bioavailability and non-toxic.Conclusions This study screened out a new potential drug for cancer treatment, which can be further studied to explore its better therapeutic effect.

scholarly journals Structural Insight into PRMT5 Inhibitors through Amalgamating Pharmacophore-Based Virtual Screening, ADME-Toxicity and Binding Energy Studies and Identify New Inhibitors by Molecular Docking.

2021 ◽  
Author(s):  
Revanth Bathula ◽  
Sree kanth Sivan ◽  
Gururaj Somadi ◽  
Narasimha Muddagoni ◽  
Goverdhan Lanka ◽  
...  
Keyword(s):  
Free Energy ◽  
Molecular Docking ◽  
Virtual Screening ◽  
Binding Free Energy ◽  
Protein A ◽  
Pharmacophore Model ◽  
Receptor Site ◽  
3D Qsar ◽  
Qsar Modeling ◽  
Energy Prediction

Abstract Protein arginine methyltransferase 5 (PRMT5) is a member of the methyltransferases family, a type II arginine enzyme that is crucial for many cellular processes and is associated with many cancer diseases. In this study, pharmacophore-based 3D QSAR modeling, virtual screening and binding free energy studies were carried out from a set of 61 potent compounds reported being inhibitors of PRMT5 protein. A five-point pharmacophore model (AADHR) was generated and this model is used to generate an atom-based 3-Dimensional quantitative structure-activity relationship (3D-QSAR). The obtained 3D-QSAR model has high correlation coefficient (R2 = 0.91), cross-validation coefficient (Q2 = 0.82), F value (140.3), low RMSE (0.47) and pearson R-value (0.91). A library of 329825 molecules (ChEMBL database) is screened with pharmacophore model to retrieve hit molecules that are further subjected for molecular docking to identify best fit-active conformations binding at the receptor site of PRMT5 protein. Further, we are calculated ADME and toxicity properties using QikProp module and pkCSM server and finally prioritized the lead molecules by binding free energy prediction.

scholarly journals Discovery of Novel DPP-IV Inhibitors as Potential Candidates for the Treatment of Type 2 Diabetes mellitus Predicted by 3D QSAR Pharmacophore Models, Molecular Docking and de novo Evolution

Molecules ◽  
2019 ◽  
Vol 24 (16) ◽  
pp. 2870 ◽  
Author(s):  
Musoev ◽  
Numonov ◽  
You ◽  
Gao
Keyword(s):  
Molecular Docking ◽  
Amino Acid ◽  
Virtual Screening ◽  
De Novo ◽  
Pharmacophore Model ◽  
3D Qsar ◽  
Amino Acid Residues ◽  
Training Set ◽  
Dpp Iv ◽  
Pharmacophore Models

Dipeptidyl peptidase-IV (DPP-IV) rapidly breaks down the incretin hormones glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic peptide (GIP). Thus, the use of DPP-IV inhibitors to retard the degradation of endogenous GLP-1 is a possible mode of therapy correcting the defect in incretin-related physiology. The aim of this study is to find a new small molecule and explore the inhibition activity to the DPP-IV enzyme using a computer aided simulation. In this study, the predicted compounds were suggested as potent anti-diabetic candidates. Chosen structures were applied following computational strategies: The generation of the three-dimensional quantitative structure-activity relationship (3D QSAR) pharmacophore models, virtual screening, molecular docking, and de novo Evolution. The method also validated by performing re-docking and cross-docking studies of seven protein systems for which crystal structures were available for all bound ligands. The molecular docking experiments of predicted compounds within the binding pocket of DPP-IV were conducted. By using 25 training set inhibitors, ten pharmacophore models were generated, among which hypo1 was the best pharmacophore model with the best predictive power on account of the highest cost difference (352.03), the lowest root mean squared deviation (RMSD) (2.234), and the best correlation coefficient (0.925). Hypo1 pharmacophore model was used for virtual screening. A total of 161 compounds including 120 from the databases, 25 from the training set, 16 from the test set were selected for molecular docking. Analyzing the amino acid residues of the ligand-receptor interaction, it can be concluded that Arg125, Glu205, Glu206, Tyr547, Tyr662, and Tyr666 are the main amino acid residues. The last step in this study was de novo Evolution that generated 11 novel compounds. The derivative dpp4_45_Evo_1 by all scores CDOCKER_ENERGY (CDOCKER, -41.79), LigScore1 (LScore1, 5.86), LigScore2 (LScore2, 7.07), PLP1 (-112.01), PLP2 (-105.77), PMF (-162.5)—have exceeded the control compound. Thus the most active compound among 11 derivative compounds is dpp4_45_Evo_1. Additionally, for derivatives dpp4_42_Evo_1, dpp4_43_Evo2, dpp4_46_Evo_4, and dpp4_47_Evo_2, significant upward shifts were recorded. The consensus score for the derivatives of dpp4_45_Evo_1 from 1 to 6, dpp4_43_Evo2 from 4 to 6, dpp4_46_Evo_4 from 1 to 6, and dpp4_47_Evo_2 from 0 to 6 were increased. Generally, predicted candidates can act as potent occurring DPP-IV inhibitors given their ability to bind directly to the active sites of DPP-IV. Our result described that the 6 re-docked and 27 cross-docked protein-ligand complexes showed RMSD values of less than 2 Å. Further investigation will result in the development of novel and potential antidiabetic drugs.

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.

Discovery of structural prospects of imidazo[1,5-a]pyrazine derivatives as BTK inhibitors against cancer: A computational study

2021 ◽  
Vol 18 ◽  
Author(s):  
Amena Ali ◽  
Abuzer Ali ◽  
Mohamed Jawed Ahsan
Keyword(s):  
Molecular Docking ◽  
Virtual Screening ◽  
Anticancer Agents ◽  
Oral Absorption ◽  
Computational Study ◽  
Pharmacophore Model ◽  
3D Qsar ◽  
Pharmacokinetic Parameters ◽  
Pharmacophore Hypothesis ◽  
Btk Inhibitors

Background: Bruton’s tyrosine kinase (BTK) plays an important role in cell development and proliferation. BTK inhibitors are encouraging novel agents against B-cell malignancies and autoimmune diseases. Although BTK inhibitors have been approved by the FDA, but to lower off-target effects and to reduce emerging resistances, it is necessary to develop novel BTK inhibitors with better outcomes and minimum side effects. Objective: The present study includes pharmacophore hypothesis, 3D QSAR, virtual screening, docking, ADME analysis and screening of potential imidazo[1,5-a]pyrazine derivatives as BTK inhibitors. Methods: Generation of pharmacophore hypothesis, virtual screening, 3D QSAR, molecular docking and ADME analysis. Methods: Generation of pharmacophore hypothesis, virtual screening, 3D QSAR, molecular docking and ADME analysis. Results: Pharmacophore study generated 20 pharmacophore hypotheses as BTK inhibitor. The five-point hypothesis DPRRR_1 were selected, consist one hydrogen bond donor, one positive ionic, and three ring aromatic features. 3D QSAR study of the compounds provided the best model with high Q2 (0.8683), R2 (0.983) and R2CV (0.5338) values. The developed pharmacophore model was further taken for screening of ZINC database ligands for evaluation of docking interaction and physiochemical properties. Potent compounds of the series 15, 27, 8n and 38 showed good docking scores -8.567, -7.465, -6.922, -6.137, respectively. Conclusion: All the pharmacokinetic parameters analysed, including human oral absorption of active compounds of the series were found to be within the permissible range. The present geometry and features included in pharmacophore hypothesis can be used for the development of novel BTK inhibitors as anticancer agents.

scholarly journals 3D Pharmacophore-based Virtual Screening, Docking Approaches toward the Discovery of Novel HPPD Inhibitors

2017 ◽  
Author(s):  
Ying Fu ◽  
Yi-Na Sun ◽  
Ke-Han Yi ◽  
Ming-Qiang Li ◽  
Hai-feng Cao ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Virtual Screening ◽  
Active Site ◽  
Pharmacophore Model ◽  
Computational Techniques ◽  
Type I ◽  
Binding Model ◽  
Discovery Studio ◽  
Life Threatening ◽  
Key Residues

p-Hydroxyphenylpyruvate dioxygenase (HPPD) is not only the useful molecular target in treating life-threatening tyrosinemia type I, but also an important target for chemical herbicides. A combined in silico structure-based pharmacophore and molecular docking based virtual screening were performed to identify novel potential HPPD inhibitors. The complex based pharmacophore model (CBP) with 0.721 of ROC used for screening compound showed remarkable ability to retrieve known active ligands from decoy molecule. The ChemDiv database was screened using CBP-Hypo2 as a 3D query, and the best-fit hits subjected to molecular docking with two methods of LibDock and CDOCKER in Accelrys Discovery Studio 2.5(DS 2.5) to discern interactions with key residues at the active site of HPPD. 4 Compounds with top rank in HipHop model and well-known binding model were finally chosen as identification of lead compounds with potentially inhibitory effects on active site of target. The results provided powerful insight to the development of novel HPPD inhibitors herbicides using computational techniques.

scholarly journals Structure based pharmacophore modeling, virtual screening, molecular docking and ADMET approaches for identification of natural anti-cancer agents targeting XIAP protein

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Firoz A. Dain Md Opo ◽  
Mohammed M. Rahman ◽  
Foysal Ahammad ◽  
Istiak Ahmed ◽  
Mohiuddin Ahmed Bhuiyan ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Virtual Screening ◽  
Md Simulation ◽  
Pharmacophore Model ◽  
Pharmacophore Modeling ◽  
Drug Candidate ◽  
Inhibitor Of Apoptosis ◽  
Inhibitor Of Apoptosis Protein ◽  
Apoptosis Protein ◽  
Apoptosis Process

AbstractX-linked inhibitor of apoptosis protein (XIAP) is a member of inhibitor of apoptosis protein (IAP) family responsible for neutralizing the caspases-3, caspases-7, and caspases-9. Overexpression of the protein decreased the apoptosis process in the cell and resulting development of cancer. Different types of XIAP antagonists are generally used to repair the defective apoptosis process that can eliminate carcinoma from living bodies. The chemically synthesis compounds discovered till now as XIAP inhibitors exhibiting side effects, which is making difficulties during the treatment of chemotherapy. So, the study has design to identifying new natural compounds that are able to induce apoptosis by freeing up caspases and will be low toxic. To identify natural compound, a structure-based pharmacophore model to the protein active site cavity was generated following by virtual screening, molecular docking and molecular dynamics (MD) simulation. Initially, seven hit compounds were retrieved and based on molecular docking approach four compounds has chosen for further evaluation. To confirm stability of the selected drug candidate to the target protein the MD simulation approach were employed, which confirmed stability of the three compounds. Based on the finding, three newly obtained compounds namely Caucasicoside A (ZINC77257307), Polygalaxanthone III (ZINC247950187), and MCULE-9896837409 (ZINC107434573) may serve as lead compounds to fight against the treatment of XIAP related cancer, although further evaluation through wet lab is necessary to measure the efficacy of the compounds.

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