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Biologically active compounds databases

While not convincing from a statishcal perspective, the results in this section are consistent with a trend high-activity molecules published in the past decade of medicinal chemistry literature are more likely to be found in the large, hydrophobic and poor solubility corner of chemical property space. These results are not consistent with, for example, cell-based [41] and median-based [42] partihoning of biologically active compounds however, such analyses were performed in the presence of inactive compounds selected from MDDR[41] or ACD [42], with quite probably unrelated chemotypes. ACD, the Available Chemicals Directory [43], and MDDR, the MDL Drug Data Report [43], are databases commonly used by the pharmaceuhcal industry. [Pg.32]

Biologically active compounds. A major application of VS techniques is the identification of novel active molecules in large compound databases. Figure 1.21 illustrates how partitioning methods are adapted for this purpose. [Pg.35]

Figure 5.7. Comparisons of the 3D four-point pharmacophore fingerprints exhibited by several sets [MDDR database of 62,000 biologically active compounds, a corporate registry database of 100,000 compormds used for screening, 100,000 compounds from combinatorial libraries (from a four-com-poilent Ugi condensation reaction), and 14,000 compound random subsets (MDDR, corporate) or indlividual libraries]. The four-point potential pharmacophores were calculated using 10 distance range bins and the standard six pharmacophore features. Figure 5.7. Comparisons of the 3D four-point pharmacophore fingerprints exhibited by several sets [MDDR database of 62,000 biologically active compounds, a corporate registry database of 100,000 compormds used for screening, 100,000 compounds from combinatorial libraries (from a four-com-poilent Ugi condensation reaction), and 14,000 compound random subsets (MDDR, corporate) or indlividual libraries]. The four-point potential pharmacophores were calculated using 10 distance range bins and the standard six pharmacophore features.
Chiral Synthon (CHIRON) was developed by S. Hannessian particularly for recognizing chiral substructures in a target molecule as well as their access from the chiral pool [110]. Version 5 includes a database of more than 200,000 compounds including commercially available compounds, 5000 selected literature data, and more than 1000 biologically active compounds. [Pg.230]

A module of the CHEM-X modelling system (see modelling section). Storage and retrieval of two- and three-dimensional structures with substructure-search capability. Available databases include Chapman Hall Dictionary of Drugs (15,000 compounds). Chapman Hall Dictionary of Fine Chemicals (120,000 small organics). Chapman Hall Dictionary of Natural Products (54,000), Derwent Standard Drug File (31,000 biologically active compounds), ChemReact (370,000 reaction types) and others. [Pg.229]


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Biological active compounds

Biological compounds

Biological databases

Biologically active compounds

Compound databases

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