Databases ligand-based screening

Figure 4.1 Ligand-based virtual screening methods. The figure shows different computational methods for screening compound databases that take either a local or a global view on molecular structure. Molecular similarity methods that operate on molecular descriptors, histogram representations, superposition or (reduced) molecular graphs evaluate molecular structure globally. By contrast, local structural features are explored by substructure and pharmacophore searching or QSAR modeling.

For the pharmacophore-based screening, a 3-D-pharmaco-phore feature is constmcted by structure-activity relationship analysis on a series of active componnds (26) or is dednced from the X-ray crystal stmcture of a ligand-receptor complex (27). Taking this 3-D-pharmacophore feature as a query structure, 3-D database search can be performed to select the molecnles from the available chemical databases, which contain the pharmacophore elements and may conform to the pharmacophore geometric constraints. Then the selected compounds are obtained either from commercial sonrces or from organic synthesis for the real pharmacologic assays (see Fig. 3). 

Figure 5.10. Ligand-based virtual screening/similarity analysis of a 150,000-compound database containing about 250 known melatonin antagonists, showingthe strong performance of the pharmacophore descriptors, and the complementarity of atom pairs and pharmacophore descriptors when combined.

Similarity searching is one of the simplest methodologies of ligand-based VS, when screening databases against a... 

FIGURE 2.2 Exploration of the chemical space includes the classification and analysis of databases. To generate structural models several methodologies have been developed, they can be classified into ligand-based and target-based methods. Models derived from these methods are frequently used as a source for virtual screening. 

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