Topological descriptors pharmacophore

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.

The work from Wagener and Lommerse [26] detailed a new ligand-based topological pharmacophore descriptor specifically for the identification of bioisosteres and can be seen as an approach to alleviate the issues of sensitivity to heteroatom replacement observed by Schuffenhauer et al. The descriptors applied in this work used an atom pair representation similar to that reported by Carhart et al. [28]. These descriptors are extracted from databases of known molecules by shredding the molecules at all deavable bonds with the attachment point being retained as a distinct atom type, X. 

These pharmacophore techniques are different in format from the traditional pharmacophore definitions. They can not be easily visualized and mapped to the molecular structures rather, they are encoded as keys or topological/topographical descriptors. Nonetheless, they capture the same idea as the classic pharmacophore concept. Furthermore, this formalism is quite useful in building quantitative predictive models that can be used to classify and predict biological activities. 

The work of Schneider et al. [6] first focused on the scaffold-hopping ability of autocorrelation descriptors, in this case topological pharmacophores. The general description of the atoms with pharmacophore atom types in combination with the decomposition of molecules into atom pairs was shown to be especially successful in finding new molecules with significant different molecular scaffolds, maintaining the desired biological effect. 

Schneider et al. [125] developed and applied CATS descriptors as topological pharmacophores for scaffold hopping. This approach led to the prediction of novel cardiac T-type Ca channel blocking agents using mibefradil as starting structure. 

Naerum et al. [133] used topological pharmacophores to search for novel glycogen synthase kinase-3 inhibitors at Novo Nordisk. Using virtual screening based on the CATS descriptor, a novel chemotype with activity against GSK-3 was identified. After parallel synthesis around the identified motifs, interesting inhibitors with GSK-3 activity <1 [xM were found (Figure 12.11a). 

Ligand-based topological pharmacophores are a class of descriptors that attempt to simulate three-dimensional (3D) pharmacophoric representations by atomic abstraction and the application of through-graph distances as an adjunct for geometric distance through space. 

The R-group descriptor from Holliday et al. [20] is a further example of a topological pharmacophore fingerprint. However, this approach characterizes a distribution of pharmacophoric properties at topological distances from an attachment point. 

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