Sesquiterpene lactones are the active components of a variety of medicinal plants from the Asteraceae family. They possess biological activities such as the inhibition of NF-kappaB and the release inhibition of the vasoactive serotonin. On the basis of a data set of 54 SLs, we report the development of a quantitative model for the prediction of serotonin release inhibition. Comparing this model with a previous investigation of the target NF-kappaB, structural features necessary for specific compounds could be acquired. Atomic properties encoded by radial distribution function and molecular surface potentials encoded by autocorrelation were used as descriptors. Whereas some descriptors describe the structural requirements for both activities, other descriptors can be used to decide whether an SL is more active to NF-kappaB or to serotonin release. Again, counter propagation neural networks proved to be a valuable tool to establish structure-activity relationships that are necessary for the search for and optimization of lead structures.