466 related articles for article (PubMed ID: 32472293)
1. Pharmacophore development, drug-likeness analysis, molecular docking, and molecular dynamics simulations for identification of new CK2 inhibitors.
Hammad S; Bouaziz-Terrachet S; Meghnem R; Meziane D
J Mol Model; 2020 May; 26(6):160. PubMed ID: 32472293
[TBL] [Abstract][Full Text] [Related]
2. Identification of potential PKC inhibitors through pharmacophore designing, 3D-QSAR and molecular dynamics simulations targeting Alzheimer's disease.
Iqbal S; Anantha Krishnan D; Gunasekaran K
J Biomol Struct Dyn; 2018 Nov; 36(15):4029-4044. PubMed ID: 29182053
[TBL] [Abstract][Full Text] [Related]
3. Multiple e-pharmacophore modelling pooled with high-throughput virtual screening, docking and molecular dynamics simulations to discover potential inhibitors of Plasmodium falciparum lactate dehydrogenase (PfLDH).
Saxena S; Durgam L; Guruprasad L
J Biomol Struct Dyn; 2019 Apr; 37(7):1783-1799. PubMed ID: 29718775
[TBL] [Abstract][Full Text] [Related]
4. Pharmacophore modeling, multiple docking, and molecular dynamics studies on Wee1 kinase inhibitors.
Hu Y; Zhou L; Zhu X; Dai D; Bao Y; Qiu Y
J Biomol Struct Dyn; 2019 Jul; 37(10):2703-2715. PubMed ID: 30052133
[TBL] [Abstract][Full Text] [Related]
5. Identification of selective MMP-9 inhibitors through multiple e-pharmacophore, ligand-based pharmacophore, molecular docking, and density functional theory approaches.
Jana S; Singh SK
J Biomol Struct Dyn; 2019 Mar; 37(4):944-965. PubMed ID: 29475408
[TBL] [Abstract][Full Text] [Related]
6. Identification of CK2 inhibitors with new scaffolds by a hybrid virtual screening approach based on Bayesian model; pharmacophore hypothesis and molecular docking.
Di-wu L; Li LL; Wang WJ; Xie HZ; Yang J; Zhang CH; Huang Q; Zhong L; Feng S; Yang SY
J Mol Graph Model; 2012 Jun; 36():42-7. PubMed ID: 22516037
[TBL] [Abstract][Full Text] [Related]
7. Pharmacophore modeling and virtual screening in search of novel Bruton's tyrosine kinase inhibitors.
Sharma A; Thelma BK
J Mol Model; 2019 Jun; 25(7):179. PubMed ID: 31172362
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. Identification of dual kinase inhibitors of CK2 and GSK3β: combined qualitative and quantitative pharmacophore modeling approach.
Pardhi T; Vasu K
J Biomol Struct Dyn; 2018 Jan; 36(1):177-194. PubMed ID: 27960601
[TBL] [Abstract][Full Text] [Related]
10. Pharmacophore modeling, virtual screening, docking and in silico ADMET analysis of protein kinase B (PKB β) inhibitors.
Vyas VK; Ghate M; Goel A
J Mol Graph Model; 2013 May; 42():17-25. PubMed ID: 23507201
[TBL] [Abstract][Full Text] [Related]
11. Pharmacophore based virtual screening for identification of effective inhibitors to combat HPV 16 E6 driven cervical cancer.
Mohan A; Krishnamoorthy S; Sabanayagam R; Schwenk G; Feng E; Ji HF; Muthusami S
Eur J Pharmacol; 2023 Oct; 957():175961. PubMed ID: 37549730
[TBL] [Abstract][Full Text] [Related]
12. Ligand-based and e-pharmacophore modeling, 3D-QSAR and hierarchical virtual screening to identify dual inhibitors of spleen tyrosine kinase (Syk) and janus kinase 3 (JAK3).
Kaur M; Silakari O
J Biomol Struct Dyn; 2017 Nov; 35(14):3043-3060. PubMed ID: 27678281
[TBL] [Abstract][Full Text] [Related]
13. Pharmacophore generation, atom-based 3D-QSAR, molecular docking and molecular dynamics simulation studies on benzamide analogues as FtsZ inhibitors.
Tripathy S; Azam MA; Jupudi S; Sahu SK
J Biomol Struct Dyn; 2018 Sep; 36(12):3218-3230. PubMed ID: 28938860
[TBL] [Abstract][Full Text] [Related]
14. Investigation of non-hydroxamate scaffolds against HDAC6 inhibition: A pharmacophore modeling, molecular docking, and molecular dynamics simulation approach.
Zeb A; Park C; Son M; Rampogu S; Alam SI; Park SJ; Lee KW
J Bioinform Comput Biol; 2018 Jun; 16(3):1840015. PubMed ID: 29945500
[TBL] [Abstract][Full Text] [Related]
15. Discovery of potent inhibitors for interleukin-2-inducible T-cell kinase: structure-based virtual screening and molecular dynamics simulation approaches.
Meganathan C; Sakkiah S; Lee Y; Narayanan JV; Lee KW
J Mol Model; 2013 Feb; 19(2):715-26. PubMed ID: 23015102
[TBL] [Abstract][Full Text] [Related]
16. Discovery of Potential Chemical Probe as Inhibitors of CXCL12 Using Ligand-Based Virtual Screening and Molecular Dynamic Simulation.
Haider S; Barakat A; Ul-Haq Z
Molecules; 2020 Oct; 25(20):. PubMed ID: 33092204
[TBL] [Abstract][Full Text] [Related]
17. Exploration of New and Potent Lead Molecules Against CAAX Prenyl Protease I of Leishmania donovani Through Pharmacophore Based Virtual Screening Approach.
Prabhu SV; Tiwari K; Suryanarayanan V; Dubey VK; Singh SK
Comb Chem High Throughput Screen; 2017; 20(3):255-271. PubMed ID: 28116998
[TBL] [Abstract][Full Text] [Related]
18. Lead optimization, pharmacophore development and scaffold design of protein kinase CK2 inhibitors as potential COVID-19 therapeutics.
Yadav S; Ahamad S; Gupta D; Mathur P
J Biomol Struct Dyn; 2023 Mar; 41(5):1811-1827. PubMed ID: 35014595
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Structure-based pharmacophore modeling, virtual screening and simulation studies for the identification of potent anticancerous phytochemical lead targeting cyclin-dependent kinase 2.
Sharma M; Sharma N; Muddassir M; Rahman QI; Dwivedi UN; Akhtar S
J Biomol Struct Dyn; 2022; 40(20):9815-9832. PubMed ID: 34151738
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
