Pharmacophore descriptors

There have been numerous efforts in library synthesis to develop compounds with central nervous system (CNS) activity [37]. Most recently, a QSAR model has been developed based on the activity of 2500 compounds against 180 assays using proprietary 3D pharmacophore descriptors [38]. The model successfully predicted 83% of a test... 

Decision Trees are also a well-known technique in the field [151]. They arrange a subset of the descriptor components in a hierarchical fashion (a binary tree) such that on a particular node in the tree a classification on a single descriptor component decides whether the left or the right branch underneath is followed. The leaves of the tree determine the overall classification label. Decision trees have been found useful, especially on large-scale descriptors like binary pharmacophore descriptors [152]. 

Pickett, S.D., Luttmann, C., Guerin, V., Laoui, a., and JAMES, E. DIVSEL and COM PEI B - strategies for the design and comparison of combinatorial libraries using pharmacophoric descriptors. 

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.

Mason and coworkers reported one of the first examples of a target class library design utilizing 3D pharmacophore descriptors (20). In this example, the authors designed a set of GPCR-targeted libraries based on Ugi chemistry... 

Other approaches that could be extended to target class library design include work by McGregor and Muskal (13,19). These utilized PharmPrint 3D pharmacophore descriptors (three-point pharmacophores) and partial least... 

A new versatile 3D pharmacophore descriptor was developed, and perhydropyrido[l,2-c]pyrimidines 46 and 47 were also used to develop this method (08JCI797) ... 

Figure 13.4 Results of a CATS similarity search. Similarity between the query structure (Haloperidol, a D2 antagonist upper left) and database compounds was defined in terms of a topological pharmacophore descriptor. The top 10 most-similar molecules found are shown.

Figure 13.12 A SOM-based pharmacophore road map. Different sets of ligands were projected onto a SOM that was generated by using the complete COBRA library. Black areas indicate the characteristic distributions of the compounds. Crosses indicate empty neurons in the map, i.e., areas of pharmacophore space that are not populated by the respective compound class. All molecules were encoded by a topological pharmacophore descriptor (CATS) [4], Note that each map forms a torus.

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