Pharmacophoric points

Fig. 12.27 The generation of 3-centre pharmacophore keys, illustrated using benperidol. Two different conformations are shown, together with two different combinations of three pharmacophore points.

Figure 13.3 Potential pharmacophore points can be generated with MOE s site detection algorithm. The white and red dots are the antomatically generated site points, and the ligand structure comes from the X-ray structure of the complex. See color plate.

Another group has evaluated self-organizing maps [63] and shape/ pharmacophore models [64]. They developed a new method termed SQUIRREL to compare molecules in terms of both shape and pharmacophore points. Thus from a commercial library of 199,272 compounds, 1926 were selected based on self-organizing maps trained on peroxisome proliferator-activated receptor a (PPARa) "activity islands." The compounds were further evaluated with SQUIRREL and 7 out of 21 molecules selected were found to be active in PPARa. Furthermore, a new virtual screening technique (PhAST) was developed based on representation of molecules as text strings that describe their pharmacophores [65]. 

Figure 6.6 Mapping of high-affinity ai binder onto class II adrenergic aiA pharmacophore model. All pharmacophoric points are mapped. The alignment suggests that removal of the chlorine substituent within the 4-phenyl piperidine will reduce the unfavorable side affinity on a1A.

Linear representations are by far the most frequently used descriptor type. Apart from the already mentioned structural keys and hashed fingerprints, other types of information are stored. For example, the topological distance between pharmacophoric points can be stored [179, 180], auto- and cross-correlation vectors over 2-D or 3-D information can be created [185, 186], or so-called BCUT [187] values can be extracted from an eigenvalue analysis of the molecular adjacency matrix. 

Robust peptide-derived approaches aim to identify a small drug-like molecule to mimic the peptide interactions. The primary peptide molecule is considered in these approaches as a tool compound to demonstrate that small molecules can compete with a given interaction. A variety of chemical, 3D structural and molecular modeling approaches are used to validate the essential 3D pharmacophore model which in turn is the basis for the design of the mimics. The chemical approaches include in addition to N- and C-terminal truncations a variety of positional scanning methods. Using alanine scans one can identify the key pharmacophore points D-amino-acid or proline scans allow stabilization of (i-turn structures cyclic scans bias the peptide or portions of the peptide in a particular conformation (a-helix, (i-turn and so on) other scans, like N-methyl-amino-acid scans and amide-bond-replacement (depsi-peptides) scans aim to improve the ADME properties." ... 

The representation of pharmacophores varies from one package to another and includes the nature of the pharmacophore points (fragments, chemical features) and the geometric constraints connecting these points (distances, torsions, three-dimensional coordinate location constraints). 

DISCO considers three-dimensional conformations of compounds not as coordinates but as sets of interpoint distances, an approach similar to a distance geometry conformational search. Points are calculated between the coordinates of heavy atoms labeled with interaction functions such as HBD, HBA or hydrophobes. One atom can carry more than one label. The atom types are considered as far as they determine which interaction type the respective atom would be engaged in. The points of the hypothetical locations of the interaction counterparts in the receptor macromolecule also participate in the distance matrix. These are calculated from the idealized projections of the lone pairs of participating heavy atoms or H-bond forming hydrogens. The hydrophobic points are handled in a way that the hydrophobic matches are limited to, e.g., only one atom in a hydrophobic chain and there is a differentiation between aliphatic and aromatic hydrophobes. A minimum constraint on pharmacophore point of a certain type can be set, e.g. if a certain feature is known to be required for activity [53, 54]. 

The pharmacophore points in the Tripos implementation of DISCO, currently marketed under the name DISCOtech , can be represented as Tripos UNITY [56] query features and the models can be used directly for UNITY database searches or in combination with 3D QSAR such as CoMFA as described in [57]. 

The pharmacophore identification process as implemented in the Catalyst package involves 3D structure generation, followed by conformational search and definition of the pharmacophore points consistent with the training set. 

Similarly to other software packages such as DISCO and Catalyst, Phase uses chemical features (hydrophobic, H-bond acceptors, H-bond donors, negative charge, positive charge, aromatic ring) to define the pharmacophore points called sites. These features are encoded in SMARTS and can be edited. H-bond-ing features are vectorized features (their directionality is considered). 

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