Virtual screening for aryl hydrocarbon receptor binding prediction.

Abstract

The overall goal of this study has been to validate computational models for predicting aryl hydrocarbon receptor (AhR) binding. Due to the unavailability of the AhR X-ray crystal structure we have decided to use QSARs models for the binding prediction virtual screening. We have built up CoMFA, Volsurf, and HQSAR models using as a training set 84 AhR ligands. Additionally, we have built a hybrid model combining two of the final selected models in order to give a single operational system. The results show that CoMFA, VolSurf, HQSAR, and the hybrid models gives good results (R(2) equal to 0.91, 0.79, 0.85, and 0.82 and q(2) 0.62, 0.58, 0.62, and 0.70, respectively). Since the techniques analyzed show a good correlation and good prediction also for an external test set, particularly the HQSAR and the hybrid model, we can conclude that these models can be used for predicting AhR binding in virtual screening.