274 related articles for article (PubMed ID: 32131468)
1. Identifying Novel ATX Inhibitors via Combinatory Virtual Screening Using Crystallography-Derived Pharmacophore Modelling, Docking Study, and QSAR Analysis.
Ren JX; Zhang RT; Zhang H
Molecules; 2020 Mar; 25(5):. PubMed ID: 32131468
[TBL] [Abstract][Full Text] [Related]
2. In silico approaches to identify novel myeloid cell leukemia-1 (Mcl-1) inhibitors for treatment of cancer.
Ren JX; Li CP; Zhou XL; Cao XS; Xie Y
J Biomol Struct Dyn; 2018 Jul; 36(9):2424-2435. PubMed ID: 28714799
[TBL] [Abstract][Full Text] [Related]
3. Identification of novel VP35 inhibitors: Virtual screening driven new scaffolds.
Ren JX; Zhang RT; Zhang H; Cao XS; Liu LK; Xie Y
Biomed Pharmacother; 2016 Dec; 84():199-207. PubMed ID: 27657828
[TBL] [Abstract][Full Text] [Related]
4. Pharmacophore Based 3D-QSAR, Virtual Screening and Docking Studies on Novel Series of HDAC Inhibitors with Thiophen Linker as Anticancer Agents.
Patel P; Singh A; Patel VK; Jain DK; Veerasamy R; Rajak H
Comb Chem High Throughput Screen; 2016; 19(9):735-751. PubMed ID: 27487787
[TBL] [Abstract][Full Text] [Related]
5. Prediction of Novel Anoctamin1 (ANO1) Inhibitors Using 3D-QSAR Pharmacophore Modeling and Molecular Docking.
Lee YH; Yi GS
Int J Mol Sci; 2018 Oct; 19(10):. PubMed ID: 30336555
[TBL] [Abstract][Full Text] [Related]
6. Autotaxin inhibition: development and application of computational tools to identify site-selective lead compounds.
Norman DD; Ibezim A; Scott WE; White S; Parrill AL; Baker DL
Bioorg Med Chem; 2013 Sep; 21(17):5548-60. PubMed ID: 23816044
[TBL] [Abstract][Full Text] [Related]
7. Structural Investigation of Vinca Domain Tubulin Binders by Pharmacophore, Atom based QSAR, Docking and Molecular Dynamics Simulations.
Athar M; Lone MY; Khedkar VM; Radadiya A; Shah A; Jha PC
Comb Chem High Throughput Screen; 2017; 20(8):682-695. PubMed ID: 28486912
[TBL] [Abstract][Full Text] [Related]
8. Ligand-based pharmacophore modelling and virtual screening for the identification of amyloid-beta diagnostic molecules.
Marondedze EF; Govender KK; Govender PP
J Mol Graph Model; 2020 Dec; 101():107711. PubMed ID: 32898834
[TBL] [Abstract][Full Text] [Related]
9. Combined pharmacophore modeling, 3D-QSAR and docking studies to identify novel HDAC inhibitors using drug repurposing.
Liu J; Zhu Y; He Y; Zhu H; Gao Y; Li Z; Zhu J; Sun X; Fang F; Wen H; Li W
J Biomol Struct Dyn; 2020 Feb; 38(2):533-547. PubMed ID: 30938574
[TBL] [Abstract][Full Text] [Related]
10. Identification of potential tumour-associated carbonic anhydrase isozyme IX inhibitors: atom-based 3D-QSAR modelling, pharmacophore-based virtual screening and molecular docking studies.
Kumar A; Rathi E; Kini SG
J Biomol Struct Dyn; 2020 Apr; 38(7):2156-2170. PubMed ID: 31179854
[TBL] [Abstract][Full Text] [Related]
11. Virtual screening of B-Raf kinase inhibitors: A combination of pharmacophore modelling, molecular docking, 3D-QSAR model and binding free energy calculation studies.
Zhang W; Qiu KX; Yu F; Xie XG; Zhang SQ; Chen YJ; Xie HD
Comput Biol Chem; 2017 Oct; 70():186-190. PubMed ID: 28892749
[TBL] [Abstract][Full Text] [Related]
12. Discovery of Novel DPP-IV Inhibitors as Potential Candidates for the Treatment of Type 2
Musoev A; Numonov S; You Z; Gao H
Molecules; 2019 Aug; 24(16):. PubMed ID: 31394858
[TBL] [Abstract][Full Text] [Related]
13. Identification of potential inhibitors for HCV NS5b of genotype 4a by combining dynamic simulation, protein-ligand interaction fingerprint, 3D pharmacophore, docking and 3D QSAR.
El-Hassab MAE; El-Bastawissy EE; El-Moselhy TF
J Biomol Struct Dyn; 2020 Sep; 38(15):4521-4535. PubMed ID: 31647392
[TBL] [Abstract][Full Text] [Related]
14. 3D QSAR, Docking, Molecular Dynamics Simulations and MM-GBSA studies of Extended Side Chain of the Antitubercular Drug (6S) 2-Nitro-6- {[4-(trifluoromethoxy) benzyl] oxy}-6,7-dihydro-5H-imidazo[2,1-b] [1,3] oxazine.
Chaudhari HK; Pahelkar A
Infect Disord Drug Targets; 2019; 19(2):145-166. PubMed ID: 30324898
[TBL] [Abstract][Full Text] [Related]
15. Pharmacophore- based virtual screening, 3D- QSAR, molecular docking approach for identification of potential dipeptidyl peptidase IV inhibitors.
Shah BM; Modi P; Trivedi P
J Biomol Struct Dyn; 2021 Apr; 39(6):2021-2043. PubMed ID: 32242496
[TBL] [Abstract][Full Text] [Related]
16. 3D QSAR, pharmacophore and molecular docking studies of known inhibitors and designing of novel inhibitors for M18 aspartyl aminopeptidase of Plasmodium falciparum.
Kumari M; Chandra S; Tiwari N; Subbarao N
BMC Struct Biol; 2016 Aug; 16():12. PubMed ID: 27534744
[TBL] [Abstract][Full Text] [Related]
17. Ligand-based autotaxin pharmacophore models reflect structure-based docking results.
Mize CD; Abbott AM; Gacasan SB; Parrill AL; Baker DL
J Mol Graph Model; 2011 Nov; 31():76-86. PubMed ID: 21967734
[TBL] [Abstract][Full Text] [Related]
18. In silico exploration of c-KIT inhibitors by pharmaco-informatics methodology: pharmacophore modeling, 3D QSAR, docking studies, and virtual screening.
Chaudhari P; Bari S
Mol Divers; 2016 Feb; 20(1):41-53. PubMed ID: 26416560
[TBL] [Abstract][Full Text] [Related]
19. 3D-QSAR, Virtual Screening, Docking and Design of Dual PI3K/mTOR Inhibitors with Enhanced Antiproliferative Activity.
Oluić J; Nikolic K; Vucicevic J; Gagic Z; Filipic S; Agbaba D
Comb Chem High Throughput Screen; 2017 Aug; 20(4):292-303. PubMed ID: 28460621
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]
