Toward novel universal descriptors: charge fingerprints.

Although there are a myriad of molecular descriptors for QSAR described in the literature, many descriptors contain similar information as others or are information poor. Recent work has suggested that it may be possible to discover a relatively small pool of 'universal' descriptors from which subsets can be drawn to build a diverse variety of models. We describe a new type of descriptor of this type, the charge fingerprint. This descriptor family can build good QSAR models of a diverse range of physicochemical and biological properties and can be calculated quickly and easily. It appears to be useful for modeling large data sets and has potential for screening large virtual libraries.

Broad-based quantitative structure-activity relationship modeling of potency and selectivity of farnesyltransferase inhibitors using a Bayesian regularized neural network.

Inhibitors of the enzyme farnesyltransferase show potential as novel anticancer agents. There are many known inhibitors, but efforts to build predictive SAR models have been hampered by the structural diversity and flexibility of inhibitors. We have undertaken for the first time a QSAR study of the potency and selectivity of a large, diverse data set of farnesyltransferase inhibitors. We used novel molecular descriptors based on binned atomic properties and invariants of molecular matrices and a robust, nonlinear QSAR mapping paradigm, the Bayesian regularized neural network. We have built robust QSAR models of farnesyltransferase inhibition, geranylgeranyltransferase inhibition, and in vivo data. We have derived a novel selectivity index that allows us to model potency and selectivity simultaneously and have built robust QSAR models using this index that have the potential to discover new potent and selective inhibitors.