155 related articles for article (PubMed ID: 27714030)
1. Identification of potential CCR5 inhibitors through pharmacophore-based virtual screening, molecular dynamics simulation and binding free energy analysis.
Wang J; Shu M; Wang Y; Hu Y; Wang Y; Luo Y; Lin Z
Mol Biosyst; 2016 Oct; 12(11):3396-3406. PubMed ID: 27714030
[TBL] [Abstract][Full Text] [Related]
2. Computational modeling of human coreceptor CCR5 antagonist as a HIV-1 entry inhibitor: using an integrated homology modeling, docking, and membrane molecular dynamics simulation analysis approach.
Gadhe CG; Kothandan G; Cho SJ
J Biomol Struct Dyn; 2013; 31(11):1251-76. PubMed ID: 23153179
[TBL] [Abstract][Full Text] [Related]
3. Structure-based identification of novel scaffolds as potential HIV-1 entry inhibitors involving CCR5.
Appiah-Kubi P; Iwuchukwu EA; Soliman MES
J Biomol Struct Dyn; 2022; 40(23):13115-13126. PubMed ID: 34569417
[TBL] [Abstract][Full Text] [Related]
4. Integrated computational tools for identification of CCR5 antagonists as potential HIV-1 entry inhibitors: homology modeling, virtual screening, molecular dynamics simulations and 3D QSAR analysis.
Moonsamy S; Dash RC; Soliman ME
Molecules; 2014 Apr; 19(4):5243-65. PubMed ID: 24762964
[TBL] [Abstract][Full Text] [Related]
5. Structure prediction of GPCRs using piecewise homologs and application to the human CCR5 chemokine receptor: validation through agonist and antagonist docking.
Arumugam K; Crouzy S; Chevigne A; Seguin-Devaux C; Schmit JC
J Biomol Struct Dyn; 2014; 32(8):1274-89. PubMed ID: 23869548
[TBL] [Abstract][Full Text] [Related]
6. Computational study on the interaction between CCR5 and HIV-1 entry inhibitor maraviroc: insight from accelerated molecular dynamics simulation and free energy calculation.
Bai Q; Zhang Y; Li X; Chen W; Liu H; Yao X
Phys Chem Chem Phys; 2014 Nov; 16(44):24332-8. PubMed ID: 25296959
[TBL] [Abstract][Full Text] [Related]
7. Homology modeling of human CCR5 and analysis of its binding properties through molecular docking and molecular dynamics simulation.
Shahlaei M; Madadkar-Sobhani A; Mahnam K; Fassihi A; Saghaie L; Mansourian M
Biochim Biophys Acta; 2011 Mar; 1808(3):802-17. PubMed ID: 21167131
[TBL] [Abstract][Full Text] [Related]
8. Computational Screening of CCR5 Inhibitors as Potential Entry Inhibitor Microbicides Using 3D-QSAR Studies, Docking and Molecular Dynamics Simulation.
Ramachandran R; Aathi M; Ruban DD; Piramanyagam S
Curr HIV Res; 2017; 15(4):234-244. PubMed ID: 28059045
[TBL] [Abstract][Full Text] [Related]
9. Pharmacophore-Oriented Identification of Potential Leads as CCR5 Inhibitors to Block HIV Cellular Entry.
Singh P; Kumar V; Lee G; Jung TS; Ha MW; Hong JC; Lee KW
Int J Mol Sci; 2022 Dec; 23(24):. PubMed ID: 36555761
[TBL] [Abstract][Full Text] [Related]
10. Molecular docking and 3D QSAR studies on 1-amino-2-phenyl-4-(piperidin-1-yl)-butanes based on the structural modeling of human CCR5 receptor.
Xu Y; Liu H; Niu C; Luo C; Luo X; Shen J; Chen K; Jiang H
Bioorg Med Chem; 2004 Dec; 12(23):6193-208. PubMed ID: 15519163
[TBL] [Abstract][Full Text] [Related]
11. Building three-dimensional structures of HIV-1 coreceptor CCR5 and its interaction with antagonist TAK779 by comparative molecular modeling.
Huang XQ; Jiang HL; Luo XM; Chen KX; Ji RY; Cao Y; Pei G
Acta Pharmacol Sin; 2000 Jun; 21(6):521-8. PubMed ID: 11360686
[TBL] [Abstract][Full Text] [Related]
12. Binding site exploration of CCR5 using in silico methodologies: a 3D-QSAR approach.
Gadhe CG; Kothandan G; Cho SJ
Arch Pharm Res; 2013 Jan; 36(1):6-31. PubMed ID: 23325486
[TBL] [Abstract][Full Text] [Related]
13. Ligand- and structure-based in silico studies to identify kinesin spindle protein (KSP) inhibitors as potential anticancer agents.
Balakumar C; Ramesh M; Tham CL; Khathi SP; Kozielski F; Srinivasulu C; Hampannavar GA; Sayyad N; Soliman ME; Karpoormath R
J Biomol Struct Dyn; 2018 Nov; 36(14):3687-3704. PubMed ID: 29064326
[TBL] [Abstract][Full Text] [Related]
14. Identification of nonpeptide CCR5 receptor agonists by structure-based virtual screening.
Kellenberger E; Springael JY; Parmentier M; Hachet-Haas M; Galzi JL; Rognan D
J Med Chem; 2007 Mar; 50(6):1294-303. PubMed ID: 17311371
[TBL] [Abstract][Full Text] [Related]
15. Structure modeling of the chemokine receptor CCR5: implications for ligand binding and selectivity.
Paterlini MG
Biophys J; 2002 Dec; 83(6):3012-31. PubMed ID: 12496074
[TBL] [Abstract][Full Text] [Related]
16. Drug Re-positioning Studies for Novel HIV-1 Inhibitors Using Binary QSAR Models and Multi-target-driven In Silico Studies.
Dogan B; Durdagi S
Mol Inform; 2021 Feb; 40(2):e2000012. PubMed ID: 33405326
[TBL] [Abstract][Full Text] [Related]
17. Discovery of HIV entry inhibitors via a hybrid CXCR4 and CCR5 receptor pharmacophore-based virtual screening approach.
Mirza MU; Saadabadi A; Vanmeert M; Salo-Ahen OMH; Abdullah I; Claes S; De Jonghe S; Schols D; Ahmad S; Froeyen M
Eur J Pharm Sci; 2020 Dec; 155():105537. PubMed ID: 32890663
[TBL] [Abstract][Full Text] [Related]
18. Identification of Novel Src Inhibitors: Pharmacophore-Based Virtual Screening, Molecular Docking and Molecular Dynamics Simulations.
Zhang Y; Zhang TJ; Tu S; Zhang ZH; Meng FH
Molecules; 2020 Sep; 25(18):. PubMed ID: 32911607
[TBL] [Abstract][Full Text] [Related]
19. Identification of potential dual agonists of FXR and TGR5 using e-pharmacophore based virtual screening.
Sindhu T; Srinivasan P
Mol Biosyst; 2015 May; 11(5):1305-18. PubMed ID: 25787676
[TBL] [Abstract][Full Text] [Related]
20. Exploration of the structural requirements of HIV-protease inhibitors using pharmacophore, virtual screening and molecular docking approaches for lead identification.
Islam MA; Pillay TS
J Mol Graph Model; 2015 Mar; 56():20-30. PubMed ID: 25541527
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
