These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
522 related articles for article (PubMed ID: 23507201)
21. 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]
22. Identification of non-resistant ROS-1 inhibitors using structure based pharmacophore analysis. Pathak D; Chadha N; Silakari O J Mol Graph Model; 2016 Nov; 70():85-93. PubMed ID: 27693947 [TBL] [Abstract][Full Text] [Related]
23. 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]
24. Ligand-based pharmacophore modeling and Bayesian approaches to identify c-Src inhibitors. Sakkiah S; Arullaperumal V; Hwang S; Lee KW J Enzyme Inhib Med Chem; 2014 Feb; 29(1):69-80. PubMed ID: 23432516 [TBL] [Abstract][Full Text] [Related]
25. Knowledge based identification of MAO-B selective inhibitors using pharmacophore and structure based virtual screening models. Boppana K; Dubey PK; Jagarlapudi SA; Vadivelan S; Rambabu G Eur J Med Chem; 2009 Sep; 44(9):3584-90. PubMed ID: 19321235 [TBL] [Abstract][Full Text] [Related]
26. Multiple receptor-ligand based pharmacophore modeling and molecular docking to screen the selective inhibitors of matrix metalloproteinase-9 from natural products. Gao Q; Wang Y; Hou J; Yao Q; Zhang J J Comput Aided Mol Des; 2017 Jul; 31(7):625-641. PubMed ID: 28623487 [TBL] [Abstract][Full Text] [Related]
27. 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]
28. Pharmacophore modeling, virtual screening, and molecular docking studies for discovery of novel Akt2 inhibitors. Fei J; Zhou L; Liu T; Tang XY Int J Med Sci; 2013; 10(3):265-75. PubMed ID: 23372433 [TBL] [Abstract][Full Text] [Related]
29. Pharmacophore modeling and virtual screening for designing potential PLK1 inhibitors. Wang HY; Cao ZX; Li LL; Jiang PD; Zhao YL; Luo SD; Yang L; Wei YQ; Yang SY Bioorg Med Chem Lett; 2008 Sep; 18(18):4972-7. PubMed ID: 18762425 [TBL] [Abstract][Full Text] [Related]
30. 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]
31. 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]
32. Exploration of Novel Inhibitors for Bruton's Tyrosine Kinase by 3D QSAR Modeling and Molecular Dynamics Simulation. Bavi R; Kumar R; Choi L; Woo Lee K PLoS One; 2016; 11(1):e0147190. PubMed ID: 26784025 [TBL] [Abstract][Full Text] [Related]
33. In-Silico Screening of Ligand Based Pharmacophore, Database Mining and Molecular Docking on 2, 5-Diaminopyrimidines Azapurines as Potential Inhibitors of Glycogen Synthase Kinase-3β. Mishra P; Kesar S; Paliwal SK; Chauhan M; Madan K Cent Nerv Syst Agents Med Chem; 2018; 18(2):150-158. PubMed ID: 29848281 [TBL] [Abstract][Full Text] [Related]
34. Discovery of novel CDK1 inhibitors by combining pharmacophore modeling, QSAR analysis and in silico screening followed by in vitro bioassay. Al-Sha'er MA; Taha MO Eur J Med Chem; 2010 Sep; 45(9):4316-30. PubMed ID: 20638755 [TBL] [Abstract][Full Text] [Related]
35. Discovery of potent inhibitor for matrix metalloproteinase-9 by pharmacophore based modeling and dynamics simulation studies. Kalva S; Azhagiya Singam ER; Rajapandian V; Saleena LM; Subramanian V J Mol Graph Model; 2014 Apr; 49():25-37. PubMed ID: 24473069 [TBL] [Abstract][Full Text] [Related]
36. Discovery of novel and highly potential inhibitors of glycogen synthase kinase 3-beta (GSK-3β) through structure-based pharmacophore modeling, virtual computational screening, docking and in silico ADMET analysis. Benghanem S; Mesli F; Fatima Zohra HA; Nacereddine C; Hadjer C; Abdellatif M J Biomol Struct Dyn; 2024 Sep; 42(14):7091-7106. PubMed ID: 37498130 [TBL] [Abstract][Full Text] [Related]
37. Identification of potential HIV-1 integrase strand transfer inhibitors: in silico virtual screening and QM/MM docking studies. Reddy KK; Singh SK; Tripathi SK; Selvaraj C SAR QSAR Environ Res; 2013; 24(7):581-95. PubMed ID: 23521430 [TBL] [Abstract][Full Text] [Related]
38. Pharmacophore modeling and in silico screening for new KDR kinase inhibitors. Yu H; Wang Z; Zhang L; Zhang J; Huang Q Bioorg Med Chem Lett; 2007 Apr; 17(8):2126-33. PubMed ID: 17306530 [TBL] [Abstract][Full Text] [Related]
39. A specific pharmacophore model of Aurora B kinase inhibitors and virtual screening studies based on it. Wang HY; Li LL; Cao ZX; Luo SD; Wei YQ; Yang SY Chem Biol Drug Des; 2009 Jan; 73(1):115-26. PubMed ID: 19152640 [TBL] [Abstract][Full Text] [Related]
40. Pharmacophore modeling of diverse classes of p38 MAP kinase inhibitors. Sarma R; Sinha S; Ravikumar M; Kishore Kumar M; Mahmood SK Eur J Med Chem; 2008 Dec; 43(12):2870-6. PubMed ID: 18406015 [TBL] [Abstract][Full Text] [Related] [Previous] [Next] [New Search]