Three-point pharmacophore limitation

McGregor et al. (112) have recently published a version of pharmacophore fingerprinting (the PharmPrint method) applied to QSAR and focused library design that uses a limited basis set of 10,549 three-point pharmacophores. They included the usual six phar-... 

As shown in this review, the complexity of pharmacophores can range from very simple objects (two- or three-point pharmacophores) to more sophisticated objects by the addition of more pharmacophoric features, different types of geometric constraints, shape, or excluded regions information. 2D (substructure) as well as ID (relational data) information can also be added to a 3D pharmacophore. The nature of the pharmacophoric points (feature vs. substructure) will directly affect the overall performance of a database search. In general, an overspecification of the pharmacophoric points will result in hit lists with limited structural diversity. However, the use of pharmacophores is an efficient procedure since it eliminates quickly molecules that do not possess the required features. Unfortunately, all the retrieved hits are not always active as expected since the presence of the pharmacophoric groups is only one of the multiple components that account for the activity of a molecule. Other properties (physicochemical, ADME, and toxicological properties) are other components of the multidimensional approach that is used to turn a hit into a drug. 

A limitation of the three-point pharmacophore is that it is planar and, hence, has limited ability to describe shape and chirality. Recently, there has been interest in four-point pharmacophores, which are based on four features and their associated distances. Four-point pharmacophores provide increased resolution over three-point pharmacophores and can provide a better representation of shape and chirality, which is important in ligand receptor interactions [21]. However, the number of potential pharmacophores increases enormously in going from three-point to four-point pharmacophores. For example, when distances are divided into seven bins and there are six features, then there are over 9000 possible three-point pharmacophores and 2.3 million four-point pharmacophores [19]. 

Fig. 1. Schematic illustrating three- and four-point 3D pharmacophores. Three-point 3D pharmacophores encode three functional group types and the three distances separating them, and four-point 3D pharmacophores encode four functional group types and the six distances separating them. Functional group types commonly included are acids, bases, hydrophobes, H-bond acceptors, H-bond donors, and aromatic systems. Distances are assigned to bins (e.g., 2.5-4.0 A) to limit the individual 3D pharmacophore descriptors to a tractable number, and to aid in comparing the individual 3D pharmacophores.

If one does not retrieve the structure but chooses to search in the file for certain geometrical (or other attributes), for example, the proper dimension of a pharmacophore to make three-point contact with a receptor (using a program such as MACCS marketed by Molecular Design Limited), the availability of but a single... 

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]. 

These techniques often involve statistical treatment of data in a largely retrospective manner which is further limited by the arbitrary choice of common substructures, pharmacophores, binding points, and molecular overlays. In classical QSAR the limitations can be quite severe in that regressions are confined to a series of closely related structures often differing in relatively minor ways. More recent innovations Involve molecular shape analysis (13) and distance geometry methods (14). These techniques represent significant steps toward a true three-dimensional SAR with some predictive... 

---