Topological pharmacophore methods

In the topological pharmacophore methods [527 — 531], e.g. LOGON [527, 528, 530], LOGANA [528 — 530], and EVAL [531], Free Wilson-type indicator variables... 

An example may help to clarify this statement Figure 13.4 shows the 10 highest-ranking compounds that were retrieved from the COBRA database by a topological pharmacophore similarity search (CATS method, see below). The query structure was Haloperidol, a dopamine (D2) receptor antagonist. Not surprisingly classic variations of the query structure are found in ranks 1 and 2. These are not very... 

Franke, R. and Streich, W.J. (1985a). Topological Pharmacophores. New Methods and their Application to a Set of Antimalarials. Part 2 Results from LQGANA. Quant.Struct.-Act.Relat., 4, 51-63. 

Streich, W.J. and Eranke, R. (1985) Topological pharmacophores. New methods and their applications to a set of antimalarials. Part 1. The methods LOGANA and LOGON. Quant. Struct. -Act. Relat., 4, 13-18. 

Figure 12.10 Ligand-based virtual screening examples for ion channels, (a) Calcium antagonist clopimozid from topological pharmacophore searching, IC5o< 1 pM. (b) Pancreatic Kftjp channel openers obtained from three different methods EC50 >30 pM (left), 21 pM (middle), 15 pM (right).

Byvatov and Schneider compared the SVM-based and the Kolmogorov-Smirnov feature selection methods to characterize ligand-receptor interactions in focused compound libraries.Three datasets were used to compare the feature selection algorithms 226 kinase inhibitors and 4479 noninhibitors 227 factor Xa inhibitors and 4478 noninhibitors and 227 factor Xa inhibitors and 195 thrombin inhibitors. SVM classifiers with a degree 5 polynomial kernel were used for all computations, and the molecular structure was encoded into 182 MOE descriptors and 225 topological pharmacophores. In one test, both feature selection algorithms produced comparable results, whereas in all other cases, SVM-based feature selection had better predictions. 

Simon and his coworkers have developed (426) a quantitative 3D-QSAR approach, the minimal steric (topologic) difference (MTD) approach. Oprea et al. (452) compared MTD and CoMFA on affinity of steroids for their binding proteins and found similar results. Snyder and colleagues (453) developed an automated method for pharmacophore extraction that can provide a clear-cut distinction between agonist and antagonist pharmacophores. Klopman (404,454) developed a procedure for the automatic detection of common molecular structural features present in a training set of compounds. This has been used to produce candidate pharmacophores for a set of antiulcer compounds (404). Extensions (454)of this approach allow differentiation between substructures responsible for activity and those that modulate the activity. 

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