Chirality 4-point pharmacophore

The 4-point pharmacophore keys can encode information either on the presence/absence of the possible pharmacophores or on their occurrence frequency. In the latter case, the occurrence frequency of each pharmacophore is normalized by the conformational ensemble count [Good, Gho et al., 2004[. Moreover, to account for chirality, for all chiral 4-point pharmacophores, separate bins are set in the bit strings for the two enantiomers. Finally, the similarity/diversity measure between the two 4-PPP keys is based on the Tversky association coefficient. 

Using 4-point pharmacophores enables chirality to be handled and adds some elements of volume/shape linked to electronic properties, increasing separation in similarity and diversity studies. There is a large increase in the number of potential pharmacophores that need to be considered. For example, using six possible feature types for each point, and 10 distance ranges (bins) for each feature-feature distance, the number of potential pharmacophores increases from 33 000 for 3-point pharmacophores to 9.7 million for 4-point pharmacophores [14—16]. Reducing the number of distance bins to seven reduces these numbers to 9000 and 2.3 million respectively. 

The extension to four-point pharmacophores enables chirality to be handled and enables some elements of volume/shape linked to... 

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]. 

As is the case for structural keys, pharmacophore keys can be readily extended to account for multiple conformations. Additionally, because pharmacophores are two- and three-dimensional objects, they are able to capture information on molecular shape and chirality. Three-point pharmacophore keys also lend themselves well to visualization via three-dimensional scatter plots (see section Visualization without Dimensionality Reduction below). Sheridan s original work has been extended by a number of groups, most notably those at Chemical Design [27], Rhone-Poulenc [30], and Abbott [10]. Davies and Briant [31] have employed pharmacophore keys for similarity/diversity selection using an iterative procedure that takes into account the flexibility of the compounds and the amount of overlap between their respective keys (see section Boolean Logic). 

Some research groups have extended the atom-pair descriptors to three-point (triplets) and four-point (quartets) pharmacophore descriptors (35,37,76,81) as described in section 2. These descriptors have a potentially superior descriptive power, and a perceived advantage over atom pairs is the increased "shape" information (intrapharmacophore distance relationships) content of the individual descriptors (37a). The quartet (tetrahedral) four-point descriptors offer further potential 3D content by including information on volume and chirality (37a, 82), compared with the triplets that are components of the quartets and represent planes or "slices" through the 3D shapes. 

Applying supervised machine learning techniques, Li et al. [66] proposed a model that differentiates substrates from nonsubstrates of P-gp based on a simple tree using nine distinct pharmacophores. Four-point 3D pharmacophores were employed to increase the amount of shape information and resolution and possessed the ability to distinguish chirality. Relevant features were hydrogen-bond acceptors, hydrophobic-ity indices, and a cationic charge. 

The Bron-Kerbosh clique detection is used iteratively to find sets of common distances, i.e., cliques, obtaining thereby disco solutions, e.g., pharmacophore maps chirality is also considered by evaluating the sign of the torsion angle for quadruplets of non-coplanar points. In family only one pair of structures is composed at a time for grouping structures into families. The program compair makes similar pairwise comparisons employing a user-supplied reference structure each time. 

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