Identification of Descriptors Capturing Compound Class-Specific Features by Mutual Information Analysis

Abstract

The identification of molecular descriptors that contain compound class-specific information is of high relevance in chemoinformatics. A generally applicable way to identify such descriptors is to determine and compare their information content in a given compound activity class and in large databases where the vast majority of compounds do not have the desired activity. For this purpose, the Shannon entropy concept from information theory can in principle be employed. However, previous adaptations of this concept for descriptor profiling are insufficient to select discriminatory descriptors for data sets that dramatically differ in size. Therefore, we introduce a methodology to reliably select such descriptors by transforming the previously introduced differential Shannon entropy formalism into mutual information analysis, another concept from information theory. The newly introduced approach is evaluated by descriptor ranking and correlation analysis on 168 compound activity classes.