Modeling steric and electronic effects in 3D- and 4D-QSAR schemes: predicting benzoic pK(a) values and steroid CBG binding affinities.

We conducted a systematic study of the performance of the 3D- and 4D-QSAR schemes in modeling steric and electronic effects. In particular, we compared the CoMFA and Hopfinger's 4D-QSAR schemes, which apply completely different concepts for the generation of the molecular data used for modeling QSAR. Hence, we attempted to predict the pK(a) values of (o-, m-, and p-)benzoic acids which were divided into three subseries in order to simulate different levels of steric and electronic control. The steroids binding to CBG were used as a benchmark series where biological activity is limited by shape factors. Although individual models differ depending upon the individual scheme, generally, both CoMFA and 4D-QSAR appeared to provide comparable results, irrespective of the differences in the coding schemes used for the description. Moreover, a new 4D-QSAR scheme involving a self-organizing neural network was designed. Generally, the SOM scheme that we designed performs comparably to the grid scheme; however, it provides better results for the charge type descriptors, and the robust neuron architecture allows for the decrease of the influence of the molecular superimposition mode.