Virtual screening models

Virtual Screening Models and Focused Libraries 2D, 3D-QSAR, Pharmacophore based models Structure Based models (Docking)... 

The term virtual screening or in silica screening is defined as the selection of compounds by evaluating their desirability in a computational model [17]. The desirability comprises high potency, selectivity, appropriate pharmacokinetic properties, and favorable toxicology. 

Jorissen RN, Gilson MK. Virtual screening of molecular databases using a Support Vector Machine. J Chem Inf Model 2005 45 549-61. 

Schulz-Gasch T, Stahl M. Binding site characteristics in structure-based virtual screening evaluation of current docking tools. J Mol Model 2003 9 47-57. 

Bissantz C, Bernard P, Hibert M, Rognan D. Protein-based virtual screening of chemical databases. II. Are homology models of G-protein coupled receptors suitable targets Proteins Struct Funct Genet 2003 50 5-25. 

Evers A, Klebe G. Successful virtual screening for a submicromolar antagonist of the neurokinin-1 receptor based on a ligand-supported homology model. J Med Ghent 2004 47 5381-92. 

Flohr S, Kurz M, Kostenis E, Brkovich A, Fournier A, Klabunde T. Identification of nonpeptidic urotensin II receptor antagonists by virtual screening based on a pharmacophore model derived from structure-activity relationships and nuclear magnetic resonance studies on urotensin II. J Med Ghent 2002 45 1799-805. 

Pirard B, Brendel J, Peukert S. The discovery of Kvl.5 blockers as a case study for the application of virtual screening approaches. J Chem Inf Model 2005 45 477-85. 

Wolber G, Langer T LigandScout 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. / Chem Inf Model 2005 45 160-9. 

RP and 71.7% ANN. The key descriptors for the RP model are 12 topological indices that include six E-state indices, i.e. SssCH2, SssO, SaasC, SdO, SsssN and SssssC. The classifier that combines topological indices and decision trees may be used for the virtual screening of DA, SA and Dual antagonists. 

The atomic-level structural information encoded into the E-state generates a chemical space that can be efficient in QSAR modeling and in the virtual screening... 

Another group has evaluated self-organizing maps [63] and shape/ pharmacophore models [64]. They developed a new method termed SQUIRREL to compare molecules in terms of both shape and pharmacophore points. Thus from a commercial library of 199,272 compounds, 1926 were selected based on self-organizing maps trained on peroxisome proliferator-activated receptor a (PPARa) "activity islands." The compounds were further evaluated with SQUIRREL and 7 out of 21 molecules selected were found to be active in PPARa. Furthermore, a new virtual screening technique (PhAST) was developed based on representation of molecules as text strings that describe their pharmacophores [65]. 

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