Identification of Potential Histone Deacetylase1 (HDAC1) Inhibitors using Multistep Virtual Screening Approach including SVM Model, Pharmacophore Modeling, Molecular Docking and Biological Evaluation.

Histone Deacetylases (HDACs) play a significant role in the regulation of gene expression by modifying histones and non-histone substrates. Since they are key regulators in the reversible epigenetic mechanism, they are considered as promising drug targets for the treatment of various cancers. In the present study, we have developed a workflow for identification of HDAC1 inhibitors using a multistage virtual screening approach from Maybridge and Chembridge chemical library. Initially, a support vector machine based classification model was generated, followed by generation of a zinc-binding group (ZBG) based pharmacophore model. The hits screened from these models were further subjected to molecular docking. Finally, a set of twenty-three molecules were selected from Maybridge and Chembridge library. The biological evaluation of these hits revealed that three out of the twenty-three tested compounds are showing HDAC1 inhibition along with the moderate anti-proliferative activity. It was found that the identified inhibitors are exerting chromosomal loss effect in growing yeast cells. Further, to extend the activity spectrum of the identified inhibitors, the optimization guidelines were drawn with the hydration site mapping approach by using in silico tool Watermap. Communicated by Ramaswamy H. Sarma