Molecular Surfaces SURFCATS

The first application of a surface-based pharmacophore correlation vector was reported by Stiefl and Baumann in 2003 [18] with the MaP (mapping property distributions of molecular surfaces) descriptor. They applied their MaP descriptor for QSAR applications. To our knowledge, an application of this descriptor to similarity searching has not been reported. 

The surface points for the calculation of SURFCATS are taken from the contact surface (we usually employ the Gauss-Connolly function in MOE with a spacing of 2 A). Each surface point is then assigned to the PPP type of the nearest atom. Equation (1) is used to calculate the CV with surface points instead of atoms. In contrast to MaP, the surface points are not equally distributed on the surface of a molecule. The effect of this circumstance has not yet been analyzed 

Pharmacophore CV descriptors are highly modifiable. Several parameters can be altered and tweaked. Examples are the chemical definitions of the PPPs, the considered minimum and maximum distance between an atom pair, the bin- 

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]  

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