Pharmacophoric patterns

Gund P. Three-dimensional pharmacophoric pattern searching. Prog Mol Subcell Biol 1977 5 117-43. 

Jakes SE, Willett P. Pharmacophoric pattern matching in files of 3-D chemical structures selection of inter-atomic distance screens. / Mol Graph 1986 4 12-20. 

Clark DE, Willett P, Kenny PW. Pharmacophoric pattern matching in files of three-dimensional chemical structures use of smoothed bounded-distance matrices for the representation and searching of conformationally-fiexible molecules. J Mol Graph 1992 10 194-204. 

Gund P. Three-dimensional pharmacophore pattern searching. In Hahn FE, editor, Progress in molecular and subcellular biology. Berlin Springer-Verlag, 1977. p.117-43. 

The Novartis group used the X-ray structure of a Grb2-peptide complex [68] as the structural basis for a design attempt that yielded entirely new non-peptide SH2 domain ligands [164]. As mentioned several times throughout this contribution, the interaction of the pTyr sidechain and the Asn sidechain in pTyr+2 position of the peptide ligand have been identified as key elements for molecular recognition (see Fig. 10). The obvious relevance of these two sidechain functionalities allowed the definition of a minimal pharmacophore pattern that... 

Figure 16.4 General pharmacophoric pattern of drugs at the verapamil-binding site of P-gp.

Gund, P., Wipke, W.T., Iangridge, R. Computer searching of a molecular structure file for pharmacophoric patterns. Comput. Chem. Res. Educ. Technol. 1974, 3, 5-21. 

ComPharm explicitly monitors pharmacophore patterns in calculating the intensities of six empirical pharmacophore fields described by Gaussian functions of the distance to their sources, which are the functional groups of the corresponding pharmacophore type. 

As a tool that delimits, out of the entire structural space, the zones most likely to harbor a higher density in active compounds ( Activity Zones ), all based on variations around a common pharmacophore pattern. 

As a tool that discriminates between the actives and inactives within the Activity zones, for example, to predict which of the local pharmacophore pattern variations will enhance and which will decrease activity. 

However, hypothesis models did clearly outperform similarity-based scoring with respect to the entire set. This advantage does not, as previously shown, stem from a better prediction of the subtle differences between the actives and inactives within the pharmacophoric Activity Zone . It may be ascribed to the ability of hypothesis models to correctly recognize actives that hide the key elements asked for by the model within a globally different pharmacophore pattern, which prevents them from being top ranked by the similarity search. 

The standard screening approach when several active molecules have been identified is pharmacophore mapping followed by 3D database searching. This approach assumes that the active molecules have a common mode of action and that features that are common to all of the molecules describe the pharmacophoric pattern responsible for the observed bioactivity. This is a powerful technique but one that may not be applicable to the structurally heterogeneous hits that characterize typical HTS experiments or sets of competitor compounds drawn from the public literature. In such cases, it is appropriate to consider approaches based on 2D similarity searching and we present here a comparison of approaches for combining the structural information that can be gleaned from a small set of reference structures. 

Depending on which face it puts forward, a single dmg molecule may interact with more than one receptor and thus may have more than one pharmacophoric pattern. For example, one bioactive face of acetylcholine permits interaction with a muscarinic receptor, while another bioactive face of acetylcholine permits interaction with a nicotinic receptor (section 4.2). Similarly, the excitatory neurotransmitter glutamate may bind to a range of different receptors, such as the NMDA and AMPA receptors (section 4.7), depending upon the pharmacophoric pattern displayed by the glutamate molecule toward the receptor with which it is interacting. 

The identification of the pharmacophore is a logical corollary of a QSAR calculation. If the minimum number of descriptors that differentiate activity from inactivity is known, it is possible to deduce the bioactive face of the molecule — that part of the molecule around which all of the relevant descriptors are focused. This bioactive face logically defines the pharmacophoric pattern of the bioactive molecules. 

Taken together, the examples shown above illustrate typically some pre-computer attempts to elucidate pharmacophoric patterns usable as guides for the design of new drugs. They prepared the minds for Garland Marshall s seminal publications (see references in [31, 32]) on computer-aided pharmacophore identification and all the derived applications that will be presented in the following chapters. 

Catalyst [67] was launched 1992 by BioCAD (now Accelrys) as a tool for automated pharmacophore pattern recognition in a collection of compounds based on chemical features correlated with three-dimensional structure and biological activity data. 

Alignment-free Pharmacophore Patterns -A Correlation-vector Approach ... 

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