Abstract
A QSAR treatment has been applied to a data set which consists of 123 1-phenylbenzimidazoles as inhibitors of the PDGF receptor. Two models were obtained: multilinear regression and neural network for log
IC
50
This work is devoted to the development of quantitative structure–activity relationship (QSAR) models of the biological activity of 123 1-phenylbenzimidazoles as inhibitors of the PDGF receptor. The molecular features are represented by chemical descriptors that have been calculated on geometrical, topological, quantum mechanical, and electronic basis by using CODESSA PRO. The obtained models, linear (multilinear regression) and nonlinear (artificial neural network), are aimed to link the structures to their reported activity log
1/IC
50. The former model can be used for physico-chemical interpretation, while the latter possesses a superior predictive ability.