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Pharmacophore molecular descriptor

The quantitative comparison of the optimized 3D structure of a selected set of ligands allows the development of their minimal 3D structural requirements for the recognition and activation of the biological target, that is, the pharmacophore hypothesis, and gives a sound 3D rationale to the available SARs [21]. A more complete and mechanistically relevant approach to the development of the 3D pharmacophore consists in its translation into a numerical molecular descriptor that quantifies the molecular-pharmacophore similarity-diversity for computational QSAR modeling [21,41]. [Pg.159]

A relevant aspect in QSAR studies and pharmacophore modeling is the choice of the most appropriate molecular descriptors with respect to both the molecular series... [Pg.170]

Schuffenhauer et al. have used Similog keys in molecular complexity studies,1301 and other groups have used pharmacophore-based descriptors.1281... [Pg.46]

Molecular descriptors and chemical spaces. The majority of chemoinformatics methods depend on the generation of chemical reference spaces into which molecular data sets are projected and where analysis or design is carried out. The definition of chemical spaces critically depends on the use of computational descriptors of molecular structure, physical or chemical properties, or pharmacophores. Essentially, any comparison of molecular characteristics that goes beyond simple structural comparison requires the calculation of property values and the application... [Pg.4]

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. 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.
This consideration emphasizes the analogy between pharmacophore identification and variable selection QSAR. On the basis of this analogy, we now expand the notion of chemical pharmacophoreto that of the more general descriptor pharmacophore. We shall define descriptor pharmacophore as a special subset of molecular descriptors (of any nature, not only chemical functional groups) optimized in the process of variable selection QSAR, to achieve the most significant correlation between descriptor values and biological activity. [Pg.61]


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Pharmacophore descriptor

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