CATS descriptor

Lipoxygenase AnalytiCon Discovery db, Similarity based on 2D CATS descriptors 18 hits/430 tested [68]... 

The derivation of the topological distance matrix from the molecular graph is followed by the assignment of PPPs to the nodes of the graph. The following list provides chemical definitions of the five PPP types that are implemented in the CATS descriptor. The upper-case letter in parentheses is the abbreviation of each PPP type. Additionally, a functional group description is paired with its corresponding SMARTS in square brackets ... 

Fig. 3.2 Schematic of the CATS descriptor calculation, (a) The hydrogen-depleted two-dimensional molecular graph provides the input, (b) The graph is simplified for the distance matrix computation different bond orders are not considered (unweighted graph) and all element types are disregarded. The algorithm starts at an arbitrary chosen atom and visits all nodes of the graph in a breadth-first manner, thereby building up the distance matrix. The numbers at the vertices are used to reference individual atoms in the distance matrix.

A particular property of the topological CATS descriptor is its speed of calculation. Thereby, the program qualifies for applications that deal with very large numbers of compounds, e.g. virtual screening campaigns in early stages of the drug discovery process. 

In a subsequent study, we examined the influence of seven similarity indices on the enrichment of actives using the topological CATS descriptor and the 12 COBRA datasets [31]. In particular, we evaluated to what extent different similarity measures complement each other in terms of the retrieved active compounds. Retrospective screening experiments were carried out with seven similarity measures Manhattan distance, Euclidian distance, Tanimoto coefficient, Soergel distance, Dice coefficient, cosine coefficient, and spherical distance. Apart from the GPCR dataset, considerable enrichments were achieved. Enrichment factors for the same datasets but different similarity measures differed only slightly. For most of the datasets the Manhattan and the Soergel distance... 

Which influence do different scaling methods have on the performance of the topological CATS descriptor We addressed this question with a comparison of three different ways of scaling the correlation vector descriptor [38] ... 

As stated previously for the topological CATS descriptor [31], the influence of different similarity metrics on the overall enrichment is marginal. For the full... 

Fig. 3.8 P rospective screening examples using the CATS descriptor. CATS2 is a similar approach using a slightly different definition of PPPs. TOPAS was used in two cases for the de novo assembly of molecules.

CATS3D was not only successful in scaffold hopping on the basis of the definition above (Meqi). We also observed a substructure hopping , which might be seen as an equivalent to more traditional bioisosteric replacement strategies. It seems that the CATS descriptor family represents molecules in a way that allows a combination of scaffold hopping and substructure hopping at once. This can result in a selection of molecules which would not be considered similar by other methods such as the MACCS keys. 

Substructure searching is often used in drug design and needs no further clarification. Similarity searching is also a very well known technique described in more detail elsewhere [52], We usually use M ACCS keys, Unity fingerprints, CATS descriptors, and feature trees for similarity searching [53], Each technique has its own strengths and weaknesses, so we favor parallel application of two or three of them. 

Figure 2.1 Typical example of scaffold hopping, obtained by similarity screening with CATS descriptors. Starting from the left-hand reference T-channel blocker mibefradil, the right-hand compound clopimozid, a submicromolar T-channel blocker, is found among the 12 top ranking analogs.

Atom-type autocorrelations have been used to derive some —> substructure descriptors such as —> atom pairs, —> CATS descriptors, and related descriptors. 

CATS descriptors substructure descriptors (0 pharmacophore-based descriptors)... 

CATS descriptors are very similar to the PPP pair descriptors, the main difference being the topological distance betvsreen any pair of pharmacophore point types used in place of the geometrical distance [Schneider, Neidhart et al., 1999 Fechner, Franke ef al., 2003]. Moreover, tvhereas PPP pair descriptors are bit strings, CATS descriptors are —> holographic vectors where each bin encodes the number of times a PPP pair occurs in the molecule. 

CATS descriptors defined above are better named CATS2D descriptors because they are based on topological distances. 

Applications of CATS descriptors discussed in literature are [Zuegge, Fechner et al., 2002 Byvatov, Fechner et al., 2003 Fechner and Schneider, 2004a, 2007 Merkwirth, Mauser et al, 2004 Evers, Messier et al, 2005 Fechner, Paetz et al, 2005 Renner, Ludwig et al, 2005 Schneider and Fechner, 2005 Noeske, Sasse et al, 2006 Franke, Schwarz et al, 2007]. 

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