Descriptive mining for the QSAR problem
Abstract
There are several approaches in trying to solve the Quantitative Structure-Activity (QSAR) problem. These approaches are based either on statistical methods or on predictive data mining using neural networks. Among the statistical methods, one should consider regression analysis, pattern recognition (such as cluster analysis, factor analysis and principal components analysis) or partial least squares. These approaches have a low explanatory capability or non at all. This paper attempts to establish a new approach in solving QSSAR problems using descriptive data mining. This way, the relationship between the chemical properties and the activity of a substance would be comprehensibly modeled.
