An analysis of the redundancy of graph invariants used in chemoinformatics Original Research Article

Molecular descriptors play a decisive role for evaluating large virtual libraries and to predict biological or physicochemical properties of compounds. Topological indices are an important class of molecular descriptors, based on the graph of a molecule. A major problem is that many topological indices are considerably correlated, impeding data analysis and interpretation. Also, a size-dependent variance of topological indices adversely affects data processing by neural nets. Using random graphs as a model for molecules, we examine correlations and variance of an abstract topological index with independent vertex properties. We consider a random graph model making no assumptions on the distribution of graphs and a model on a fixed number of vertices in which edges are selected independently. We show that topological indices may be strongly correlated even for in