AN APPLICATION OF UNSUPERVISED NEURAL NETWORK METHODOLOGY KOHONEN TOPOLOGY-PRESERVING MAPPING) TO QSAR ANALYSIS

The concept and methodology of artificial neural networks is introduced. Like pattern recognition, the techniques can be classified as supervised (requiring a priori knowledge of class membership) and unsupervised (making no assumptions about class membership). An unsupervised neural network method. Kohonen Topology-Preserving Mapping, is applied to a wide matrix of physicochemical property data for a set of antifilarial antimycin analogues containing structural outliers. Principal component analysis failed to give a good 2D representation of the data set as a whole due to linear constraints in the model which gave undue influence to the outliers. Kohonen mapping compared favourably with non-linear unsupervised statistical pattern recognition methods for 2D representation of compound similarity and for classification based on antifilarial activity. It may prove a valuable technique for QSAR in situations where a linear method does not model the data well and a high throughput of test compounds is indicated.