36 related articles for article (PubMed ID: 16564691)
1. Discovery of a Novel Chemo-Type for TAAR1 Agonism via Molecular Modeling.
Grossi G; Scarano N; Musumeci F; Tonelli M; Kanov E; Carbone A; Fossa P; Gainetdinov RR; Cichero E; Schenone S
Molecules; 2024 Apr; 29(8):. PubMed ID: 38675561
[TBL] [Abstract][Full Text] [Related]
2. Discovery of Guanfacine as a Novel TAAR1 Agonist: A Combination Strategy through Molecular Modeling Studies and Biological Assays.
Cichero E; Francesconi V; Casini B; Casale M; Kanov E; Gerasimov AS; Sukhanov I; Savchenko A; Espinoza S; Gainetdinov RR; Tonelli M
Pharmaceuticals (Basel); 2023 Nov; 16(11):. PubMed ID: 38004497
[TBL] [Abstract][Full Text] [Related]
3. Auto In Silico Ligand Directing Evolution to Facilitate the Rapid and Efficient Discovery of Drug Lead.
Wu F; Zhuo L; Wang F; Huang W; Hao G; Yang G
iScience; 2020 Jun; 23(6):101179. PubMed ID: 32498019
[TBL] [Abstract][Full Text] [Related]
4. Chemoinformatics Profiling of the Chromone Nucleus as a MAO-B/A2AAR Dual Binding Scaffold.
Cruz-Monteagudo M; Borges F; Cordeiro MNDS; Helguera AM; Tejera E; Paz-Y-Mino C; Sanchez-Rodriguez A; Perera-Sardina Y; Perez-Castillo Y
Curr Neuropharmacol; 2017 Nov; 15(8):1117-1135. PubMed ID: 28093976
[TBL] [Abstract][Full Text] [Related]
5. Pharmacophore and 3D-QSAR characterization of 6-arylquinazolin-4-amines as Cdc2-like kinase 4 (Clk4) and dual specificity tyrosine-phosphorylation-regulated kinase 1A (Dyrk1A) inhibitors.
Pan Y; Wang Y; Bryant SH
J Chem Inf Model; 2013 Apr; 53(4):938-47. PubMed ID: 23496085
[TBL] [Abstract][Full Text] [Related]
6. Ligand and structure-based models for the prediction of ligand-receptor affinities and virtual screenings: Development and application to the beta(2)-adrenergic receptor.
Vilar S; Karpiak J; Costanzi S
J Comput Chem; 2010 Mar; 31(4):707-20. PubMed ID: 19569204
[TBL] [Abstract][Full Text] [Related]
7. Exploring the binding features of rimonabant analogues and acyclic CB1 antagonists: docking studies and QSAR analysis.
Cichero E; Menozzi G; Spallarossa A; Mosti L; Fossa P
J Mol Model; 2008 Dec; 14(12):1131-45. PubMed ID: 18696129
[TBL] [Abstract][Full Text] [Related]
8. Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands.
Costanzi S; Tikhonova IG; Harden TK; Jacobson KA
J Comput Aided Mol Des; 2009 Nov; 23(11):747-54. PubMed ID: 18483766
[TBL] [Abstract][Full Text] [Related]
9. Application of QSAR and shape pharmacophore modeling approaches for targeted chemical library design.
Ebalunode JO; Zheng W; Tropsha A
Methods Mol Biol; 2011; 685():111-33. PubMed ID: 20981521
[TBL] [Abstract][Full Text] [Related]
10. Search for new antagonist ligands for adenosine receptors from QSAR point of view. How close are we?
González MP; Terán C; Teijeira M
Med Res Rev; 2008 May; 28(3):329-71. PubMed ID: 17668454
[TBL] [Abstract][Full Text] [Related]
11. The application of a 3D-QSAR (autoMEP/PLS) approach as an efficient pharmacodynamic-driven filtering method for small-sized virtual library: application to a lead optimization of a human A3 adenosine receptor antagonist.
Moro S; Bacilieri M; Cacciari B; Bolcato C; Cusan C; Pastorin G; Klotz KN; Spalluto G
Bioorg Med Chem; 2006 Jul; 14(14):4923-32. PubMed ID: 16564691
[TBL] [Abstract][Full Text] [Related]
12. Autocorrelation of molecular electrostatic potential surface properties combined with partial least squares analysis as new strategy for the prediction of the activity of human A(3) adenosine receptor antagonists.
Moro S; Bacilieri M; Cacciari B; Spalluto G
J Med Chem; 2005 Sep; 48(18):5698-704. PubMed ID: 16134938
[TBL] [Abstract][Full Text] [Related]
13. Combined target-based and ligand-based drug design approach as a tool to define a novel 3D-pharmacophore model of human A3 adenosine receptor antagonists: pyrazolo[4,3-e]1,2,4-triazolo[1,5-c]pyrimidine derivatives as a key study.
Moro S; Braiuca P; Deflorian F; Ferrari C; Pastorin G; Cacciari B; Baraldi PG; Varani K; Borea PA; Spalluto G
J Med Chem; 2005 Jan; 48(1):152-62. PubMed ID: 15634009
[TBL] [Abstract][Full Text] [Related]
14. Linear and nonlinear 3D-QSAR approaches in tandem with ligand-based homology modeling as a computational strategy to depict the pyrazolo-triazolo-pyrimidine antagonists binding site of the human adenosine A2A receptor.
Michielan L; Bacilieri M; Schiesaro A; Bolcato C; Pastorin G; Spalluto G; Cacciari B; Klotz KN; Kaseda C; Moro S
J Chem Inf Model; 2008 Feb; 48(2):350-63. PubMed ID: 18215030
[TBL] [Abstract][Full Text] [Related]
15. Combining selectivity and affinity predictions using an integrated Support Vector Machine (SVM) approach: An alternative tool to discriminate between the human adenosine A(2A) and A(3) receptor pyrazolo-triazolo-pyrimidine antagonists binding sites.
Michielan L; Bolcato C; Federico S; Cacciari B; Bacilieri M; Klotz KN; Kachler S; Pastorin G; Cardin R; Sperduti A; Spalluto G; Moro S
Bioorg Med Chem; 2009 Jul; 17(14):5259-74. PubMed ID: 19501513
[TBL] [Abstract][Full Text] [Related]
16.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
17.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
18.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
19.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
20.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
[Next] [New Search]