1024 related articles for article (PubMed ID: 26416560)
1. 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]
2. Pharmacophore and Docking Guided Virtual Screening Study for Discovery of Type I Inhibitors of VEGFR-2 Kinase.
Bhojwani HR; Joshi UJ
Curr Comput Aided Drug Des; 2017; 13(3):186-207. PubMed ID: 27981900
[TBL] [Abstract][Full Text] [Related]
3. Discovery of Camptothecin Based Topoisomerase I Inhibitors: Identification Using an Atom Based 3D-QSAR, Pharmacophore Modeling, Virtual Screening and Molecular Docking Approach.
Dev S; Dhaneshwar SR; Mathew B
Comb Chem High Throughput Screen; 2016; 19(9):752-763. PubMed ID: 27515040
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. 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]
6. Identification of potential Gly/NMDA receptor antagonists by cheminformatics approach: a combination of pharmacophore modelling, virtual screening and molecular docking studies.
Ugale VG; Bari SB
SAR QSAR Environ Res; 2016; 27(2):125-45. PubMed ID: 26911562
[TBL] [Abstract][Full Text] [Related]
7. Receptor-guided 3D-QSAR approach for the discovery of c-kit tyrosine kinase inhibitors.
Almerico AM; Tutone M; Lauria A
J Mol Model; 2012 Jul; 18(7):2885-95. PubMed ID: 22127610
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. In silico studies on potential MCF-7 inhibitors: a combination of pharmacophore and 3D-QSAR modeling, virtual screening, molecular docking, and pharmacokinetic analysis.
Badhani B; Kakkar R
J Biomol Struct Dyn; 2017 Jul; 35(9):1950-1967. PubMed ID: 27401212
[TBL] [Abstract][Full Text] [Related]
10. QSAR based docking studies of marine algal anticancer compounds as inhibitors of protein kinase B (PKBβ).
Davis GD; Vasanthi AH
Eur J Pharm Sci; 2015 Aug; 76():110-8. PubMed ID: 25936945
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Pharmacophore-Based 3D-QSAR Modeling, Virtual Screening and Molecular Docking Analysis for the Detection of MERTK Inhibitors with Novel Scaffold.
Zhou S; Zhou L; Cui R; Tian Y; Li X; You R; Zhong L
Comb Chem High Throughput Screen; 2016; 19(1):73-96. PubMed ID: 26632441
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. 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]
15. 3D QSAR studies, molecular docking and ADMET evaluation, using thiazolidine derivatives as template to obtain new inhibitors of PIM1 kinase.
Aouidate A; Ghaleb A; Ghamali M; Ousaa A; Choukrad M; Sbai A; Bouachrine M; Lakhlifi T
Comput Biol Chem; 2018 Jun; 74():201-211. PubMed ID: 29635214
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. Jak2 inhibitor--a jackpot for pharmaceutical industries: a comprehensive computational method in the discovery of new potent Jak2 inhibitors.
Singh KhD; Naveena Q; Karthikeyan M
Mol Biosyst; 2014 Aug; 10(8):2146-59. PubMed ID: 24874539
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. 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]
20. QSAR modeling, pharmacophore-based virtual screening, and ensemble docking insights into predicting potential epigallocatechin gallate (EGCG) analogs against epidermal growth factor receptor.
Bommu UD; Konidala KK; Pabbaraju N; Yeguvapalli S
J Recept Signal Transduct Res; 2019 Feb; 39(1):18-27. PubMed ID: 31223050
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
