By Barry A Bunin; et al

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For pharmacophore fingerprinting, pharmacophores are also calculated based on systematic conformational search and it is assumed that increasing overlap of pharmacophore patterns in fingerprints corresponds to increasing molecular similarity. For fingerprints and other similarity-based approaches, it is often discussed in the literature whether 2D or 3D methods (or descriptors) are generally superior. Intuitively, one might expect that 3D methods should be more realistic and perform better. However, in practice, this is not the case.

Such approaches have been shown to detect a number of drugs with reasonable accuracy. Another recent trend has been to distinguish drug-likeness from lead-likeness (Rishton 2003). This idea takes into consideration that VS might rarely be capable of identifying mature drug candidates. e. molecules that can be chemically optimized to ultimately become drugs. 5 shows a comparison of simple drug-like and leadlike features that provide some guidelines for compound selection. The comparison of these criteria was adapted from Rishton 2003.

Examples of reduced graphs. Nodes corresponding to aromatic rings (Ar), aliphatic rings (R), functional groups (F) and linking groups (L) are shown (adapted from Gillet et al. 2003) experimentally determined structures of ligands. Consequently, the majority of pharmacophore models used to identify similar compounds represent hypotheses of 3D features crucial for biological activity. For database searching, pharmacophores are best defined by all possible distances between chosen groups or features (pharmacophore points).

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