Development of a structural model for NF-κB inhibition of sesquiterpene lactones using self-organizing neural networks

A variety of sesquiterpene lactones (SLs) possess considerable anti-inflammatory activity. Several Studies have shown that they exert this effect in part by inhibiting the activation of the transcription factor NF-kappa B. In the present study we elaborated oil the investigation of a data set of 103 structurally diverse SLs for which we had previously developed several different QSAR equations dependent on the skeletal type. Use of 3D structure descriptors resulted in a single model for the entire data set. In particular. local radial distribution functions (L-RDF) were used that centered on the methylene-carbonyl substructure believed to be the site of attack of cysteine-38 of the p65/NF-kappa B subunit. The model was developed by using a counterpropagation neural network (CPGNN), attesting to the power of this method for establishing structure-activity-relationships. The investigations shed more light onto the influence of the chemical structure on NF-kappa B inhibitory activity.