Classification of biologically active compounds by median partitioning

The median partitioning (MP) method was originally developed for the selection of diverse subsets from compound databases. Following this approach, property descriptors are used id subsequent steps to divide compounds into defined partitions from which representative molecules are selected. For descriptor analysis, MP was coupled to a genetic algorithm. MP subset selection does not depend on pairwise comparison of molecules and is therefore applicable to very large compound pools. Here the MP approach was evaluated for the classification of molecules according to biological activity. A total of 317 molecules belonging to 21 different activity classes were studied. MP compound classification calculations were carried out both in the presence and absence of 2000 randomly selected "background" molecules. The performance of MP was compared to cell-based partitioning and found to be at least comparable, with up to approximately 82% of active molecules occurring in "pure" partitions consisting only of molecules sharing the same activity. Different from cell-based methods, MP classification is based on "direct" and "sequential" contributions of molecular property descriptors. Our results suggest that MP in not only an effective method for the selection of diverse subsets but also for the classification of active compounds and searching for molecules with desired activity.