Database mining applied to central nervous system (CNS) activity.

Abstract

A data set of 389 compounds, active in the central nervous system (CNS) and divided into eight classes according to the receptor type, was extracted from the RBI database and analyzed by Self-Organizing Maps (SOM), also known as Kohonen Artificial Neural Networks. This method gives a 2D representation of the distribution of the compounds in the hyperspace derived from their molecular descriptors. As SOM belongs to the category of unsupervised techniques, it has to be combined with another method in order to generate classification models with predictive ability. The fuzzy clustering (FC) approach seems to be particularly suitable to delineate clusters in a rational way from SOM and to get an automatic objective map interpretation. Maps derived by SOM showed specific regions associated with a unique receptor type and zones in which two or more activity classes are nested. Then, the modeling ability of the proposed SOM/FC Hybrid System tools applied simultaneously to eight activity classes was validated after dividing the 389 compounds into a training set and a test set, including 259 and 130 molecules, respectively. The proper experimental activity class, among the eight possible ones, was predicted simultaneously and correctly for 81% of the test set compounds.