Accurate partitioning of compounds belonging to diverse activity classes.

Diverse sets of compounds were classified according to biological activity by use of a partitioning approach based on principal component analysis in conjunction with a genetic algorithm for molecular descriptor evaluation. Combinations of 236 molecular property and structural key descriptors were explored for their performance in classifying 317 molecules belonging to 21 distinct biological activity classes from various sources. Preferred descriptor combinations were further explored by complete factorial analysis. In these calculations, compounds having similar specific activity were predicted with greater than 80% accuracy.