Visualizing substructural fingerprints.

Abstract

Substructural fingerprints have proven very useful for chemical library and diversity analysis, but their high dimensionality makes them poorly suited to principal components analysis and to standard nonlinear mapping methods. By using a combination of optimizable K-dissimilarity selection (OptiSim) and a modified stress function that suppresses effects of distances that fall beyond a characteristic horizon, it is possible to relax principal components analysis coordinates into more consistently meaningful projections from fingerprint space into two dimensions. The nonlinear maps so obtained are useful for characterizing combinatorial libraries, for comparing sublibraries, and for exploring the distribution of biological properties across structural space.