Clustering and Rule-Based Classifications of Chemical Structures Evaluated in the Biological Activity Space

Classification methods for data sets of molecules according to their chemical structure were evaluated for
their biological relevance, including rule-based, scaffold-oriented classification methods and clustering based
on molecular descriptors. Three data sets resulting from uniformly determined in vitro biological profiling
experiments were classified according to their chemical structures, and the results were compared in a Pareto
analysis with the number of classes and their average spread in the profile space as two concurrent objectives
which were to be minimized. It has been found that no classification method is overall superior to all other
studied methods, but there is a general trend that rule-based, scaffold-oriented methods are the better choice
if classes with homogeneous biological activity are required, but a large number of clusters can be tolerated.
On the other hand, clustering based on chemical fingerprints is superior if fewer and larger classes are
required, and some loss of homogeneity in biological activity can be accepted.