A novel two-step QSAR modeling work flow to predict selectivity and activity of HDAC inhibitors

Abstract

A two-step modeling approach was employed to study the selectivity and activity of histone deacetylase inhibitors. First, according to the activity difference against HDAC1 and HDAC6, a binary classification model was established to classify two kinds of inhibitors. Then two continuous models were built for each subclass to predict the activity value of HDAC1 and HDAC6 inhibitors. The three models were all built with the GA-kNN method combined with dragon descriptors. They were external validated by using external prediction set and Y-randomization test. The highly predictive models were generated for all three data sets. For the classification model, the classification accuracies of the models were as high as 100% for the external test set. For HDAC1 and HDAC6 inhibitor consecutive models, external R(2) values are 0.947 and 0.911, respectively. The results proved the reliability of these models. All models were used to screen 1000 compounds included in PubMed dataset. Virtual screening resulted in 8 and 13 structurally unique consensus hits that were considered novel putative HDAC1 and HDAC6 inhibitors, respectively.