Quantitative pharmacophore

P. G. DeBeneditti, Theoretical Approaches to Quantitative Pharmacophore Modeling— Biological Activity Relationships, Elsevier, Amsterdam, The Netherlands, 1999. 

In Table 1, information about the origin and the characteristics of the different types of pharmacophores that can be generated is summarized. In their great majority, pharmacophore models are qualitative tools, but some methods can associate experimental activity values of the molecules in the building process to derive quantitative pharmacophore models. Examples of these are HASL, APEX, and HypoGen. Conformer generation is often a prerequisite, especially when working with flexible molecules. 

Risperidone (11) was also included among a a 1-adrenergic receptor antagonists to study a quantitative structure-activity relationship (99BMC2437). A pharmacophore model for atypical antipsychotics, including 11, was established (00MI41). An increased plasma level of 11 and 9-hydroxyrisperidone (12) was observed in combination with paroxetine (01 MI 13). The effect of vanlafaxine on the pharmacokinetics of 11 was reported (99MI13). 

These pharmacophore techniques are different in format from the traditional pharmacophore definitions. They can not be easily visualized and mapped to the molecular structures rather, they are encoded as keys or topological/topographical descriptors. Nonetheless, they capture the same idea as the classic pharmacophore concept. Furthermore, this formalism is quite useful in building quantitative predictive models that can be used to classify and predict biological activities. 

Ekins S, De Groot MJ, Jones JP. Pharmacophore and three-dimensional quantitative structure activity relationship methods for modeling cytochrome P450 active sites. Drug Metab Dispos 2001 29 936-44. 

Smith PA, Sorich MJ, McKinnon RA, Miners JO. Pharmacophore and quantitative structure-activity relationship modeling complementary approaches for the rationalization and prediction of UDP-glucuronosyltransferase 1A4 substrate selectivity. J Med Chem 2003 46 1617-26. 

The final part is devoted to a survey of molecular properties of special interest to the medicinal chemist. The Theory of Atoms in Molecules by R. F.W. Bader et al., presented in Chapter 7, enables the quantitative use of chemical concepts, for example those of the functional group in organic chemistry or molecular similarity in medicinal chemistry, for prediction and understanding of chemical processes. This contribution also discusses possible applications of the theory to QSAR. Another important property that can be derived by use of QC calculations is the molecular electrostatic potential. J.S. Murray and P. Politzer describe the use of this property for description of noncovalent interactions between ligand and receptor, and the design of new compounds with specific features (Chapter 8). In Chapter 9, H.D. and M. Holtje describe the use of QC methods to parameterize force-field parameters, and applications to a pharmacophore search of enzyme inhibitors. The authors also show the use of QC methods for investigation of charge-transfer complexes. 

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]. 

De Benedetti, P.G., Cocchi, M., Menziani, M.C. and Fanelli, F. (1993) Theoretical quantitative structure-activity analysis and pharmacophore modelling of selective non congeneric ala-adrenergic antagonists. Journal of Molecular Structure (Theochem), 280, 283-290. 

Quantitative Structure-Activity Relationship models are used increasingly in chemical data mining and combinatorial library design [5, 6]. For example, three-dimensional (3-D) stereoelectronic pharmacophore based on QSAR modeling was used recently to search the National Cancer Institute Repository of Small Molecules [7] to find new leads for inhibiting HIV type 1 reverse transcriptase at the nonnucleoside binding site [8]. A descriptor pharmacophore concept was introduced by us recently [9] on the basis of variable selection QSAR the descriptor pharmacophore is defined as a subset of... 

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