Performance versus learning models

Although models are usually built on the basis of a whole bunch of descriptors in machine learning techniques, in most cases simple similarity searches first rank the compounds according to single descriptors (for the combination of results with different descriptors, see below). To support the selection of the most appropriate descriptors, many studies have been published assessing the performance of different descriptors or descriptor combinations in similarity searches. Selected publications can be found in the reference list [16, 30, 41-47]. Most of them discuss the behavior of descriptors on individual test data sets in retrospective studies. They often focus on the comparison of 2D versus 3D descriptors and/or on the potential to detect new chemotypes. A detailed discussion of all these studies is beyond the scope of this contribution, but some general observations are presented. 

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