208 related articles for article (PubMed ID: 30372617)
1. Drug Discovery Maps, a Machine Learning Model That Visualizes and Predicts Kinome-Inhibitor Interaction Landscapes.
Janssen APA; Grimm SH; Wijdeven RHM; Lenselink EB; Neefjes J; van Boeckel CAA; van Westen GJP; van der Stelt M
J Chem Inf Model; 2019 Mar; 59(3):1221-1229. PubMed ID: 30372617
[TBL] [Abstract][Full Text] [Related]
2. Comprehensive structure-activity-relationship of azaindoles as highly potent FLT3 inhibitors.
Grimm SH; Gagestein B; Keijzer JF; Liu N; Wijdeven RH; Lenselink EB; Tuin AW; van den Nieuwendijk AMCH; van Westen GJP; van Boeckel CAA; Overkleeft HS; Neefjes J; van der Stelt M
Bioorg Med Chem; 2019 Mar; 27(5):692-699. PubMed ID: 30661740
[TBL] [Abstract][Full Text] [Related]
3. Discovery of N
Gucký T; Řezníčková E; Radošová Muchová T; Jorda R; Klejová Z; Malínková V; Berka K; Bazgier V; Ajani H; Lepšík M; Divoký V; Kryštof V
J Med Chem; 2018 May; 61(9):3855-3869. PubMed ID: 29672049
[TBL] [Abstract][Full Text] [Related]
4. FMS-like tyrosine kinase 3 (FLT3) inhibitors: Molecular docking and experimental studies.
Mashkani B; Tanipour MH; Saadatmandzadeh M; Ashman LK; Griffith R
Eur J Pharmacol; 2016 Apr; 776():156-66. PubMed ID: 26896780
[TBL] [Abstract][Full Text] [Related]
5. Discovery and development of extreme selective inhibitors of the ITD and D835Y mutant FLT3 kinases.
Baska F; Sipos A; Őrfi Z; Nemes Z; Dobos J; Szántai-Kis C; Szabó E; Szénási G; Dézsi L; Hamar P; Cserepes MT; Tóvári J; Garamvölgyi R; Krekó M; Őrfi L
Eur J Med Chem; 2019 Dec; 184():111710. PubMed ID: 31614258
[TBL] [Abstract][Full Text] [Related]
6. Computer aided drug discovery of highly ligand efficient, low molecular weight imidazopyridine analogs as FLT3 inhibitors.
Frett B; McConnell N; Smith CC; Wang Y; Shah NP; Li HY
Eur J Med Chem; 2015 Apr; 94():123-31. PubMed ID: 25765758
[TBL] [Abstract][Full Text] [Related]
7. Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors.
Cichonska A; Ravikumar B; Parri E; Timonen S; Pahikkala T; Airola A; Wennerberg K; Rousu J; Aittokallio T
PLoS Comput Biol; 2017 Aug; 13(8):e1005678. PubMed ID: 28787438
[TBL] [Abstract][Full Text] [Related]
8. MZH29 is a novel potent inhibitor that overcomes drug resistance FLT3 mutations in acute myeloid leukemia.
Xu B; Zhao Y; Wang X; Gong P; Ge W
Leukemia; 2017 Apr; 31(4):913-921. PubMed ID: 27773927
[TBL] [Abstract][Full Text] [Related]
9. Discovery of novel Flt3 inhibitory chemotypes through extensive ligand-based and new structure-based pharmacophore modelling methods.
Abutayeh RF; Taha MO
J Mol Graph Model; 2019 May; 88():128-151. PubMed ID: 30703688
[TBL] [Abstract][Full Text] [Related]
10. Guided Docking as a Data Generation Approach Facilitates Structure-Based Machine Learning on Kinases.
Backenköhler M; Groß J; Wolf V; Volkamer A
J Chem Inf Model; 2024 May; 64(10):4009-4020. PubMed ID: 38751014
[TBL] [Abstract][Full Text] [Related]
11. Computer-guided design, synthesis, and biological evaluation of quinoxalinebisarylureas as FLT3 inhibitors.
Göring S; Bensinger D; Naumann EC; Schmidt B
ChemMedChem; 2015 Mar; 10(3):511-22. PubMed ID: 25677073
[TBL] [Abstract][Full Text] [Related]
12. Profile-QSAR: a novel meta-QSAR method that combines activities across the kinase family to accurately predict affinity, selectivity, and cellular activity.
Martin E; Mukherjee P; Sullivan D; Jansen J
J Chem Inf Model; 2011 Aug; 51(8):1942-56. PubMed ID: 21667971
[TBL] [Abstract][Full Text] [Related]
13. Discovery of 3-phenyl-1H-5-pyrazolylamine derivatives containing a urea pharmacophore as potent and efficacious inhibitors of FMS-like tyrosine kinase-3 (FLT3).
Lin WH; Hsu JT; Hsieh SY; Chen CT; Song JS; Yen SC; Hsu T; Lu CT; Chen CH; Chou LH; Yang YN; Chiu CH; Chen CP; Tseng YJ; Yen KJ; Yeh CF; Chao YS; Yeh TK; Jiaang WT
Bioorg Med Chem; 2013 Jun; 21(11):2856-67. PubMed ID: 23618709
[TBL] [Abstract][Full Text] [Related]
14. Pocketome of human kinases: prioritizing the ATP binding sites of (yet) untapped protein kinases for drug discovery.
Volkamer A; Eid S; Turk S; Jaeger S; Rippmann F; Fulle S
J Chem Inf Model; 2015 Mar; 55(3):538-49. PubMed ID: 25557645
[TBL] [Abstract][Full Text] [Related]
15. Prediction of kinase inhibitors binding modes with machine learning and reduced descriptor sets.
Abdelbaky I; Tayara H; Chong KT
Sci Rep; 2021 Jan; 11(1):706. PubMed ID: 33436888
[TBL] [Abstract][Full Text] [Related]
16. Unexpected off-targets and paradoxical pathway activation by kinase inhibitors.
Hantschel O
ACS Chem Biol; 2015 Jan; 10(1):234-45. PubMed ID: 25531586
[TBL] [Abstract][Full Text] [Related]
17. A broad activity screen in support of a chemogenomic map for kinase signalling research and drug discovery.
Gao Y; Davies SP; Augustin M; Woodward A; Patel UA; Kovelman R; Harvey KJ
Biochem J; 2013 Apr; 451(2):313-28. PubMed ID: 23398362
[TBL] [Abstract][Full Text] [Related]
18. FMS-like tyrosine kinase 3-internal tandem duplication tyrosine kinase inhibitors display a nonoverlapping profile of resistance mutations in vitro.
von Bubnoff N; Engh RA; Aberg E; Sänger J; Peschel C; Duyster J
Cancer Res; 2009 Apr; 69(7):3032-41. PubMed ID: 19318574
[TBL] [Abstract][Full Text] [Related]
19. Deep Learning Enhancing Kinome-Wide Polypharmacology Profiling: Model Construction and Experiment Validation.
Li X; Li Z; Wu X; Xiong Z; Yang T; Fu Z; Liu X; Tan X; Zhong F; Wan X; Wang D; Ding X; Yang R; Hou H; Li C; Liu H; Chen K; Jiang H; Zheng M
J Med Chem; 2020 Aug; 63(16):8723-8737. PubMed ID: 31364850
[TBL] [Abstract][Full Text] [Related]
20. Chemogenomic Analysis of the Druggable Kinome and Its Application to Repositioning and Lead Identification Studies.
Ravikumar B; Timonen S; Alam Z; Parri E; Wennerberg K; Aittokallio T
Cell Chem Biol; 2019 Nov; 26(11):1608-1622.e6. PubMed ID: 31521622
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
