Assessment for the identification of better HDAC inhibitor class through binding energy calculations and descriptor analysis

Abstract

Histone Deacetylase (HDAC) inhibitors represent a budding class of targeted anti-cancer agents. This structurally diverse group of molecules can induce growth arrest, differentiation, apoptosis, and autophagocytic cell death of cancer cells. Of the different classes of HDAC the class I and Class II are considered the main targets for cancer. For the two classes of HDAC, only a few compounds have emerged as preferential inhibitors and even fewer are able to discriminate efficiently among HDACs in the same class. This limitation has diminutive relevance to the use of HDAC inhibitors as potential anti-tumor drugs. Hence, the four HDACs of class I was modeled and about twelve known inhibitors which are currently under the phase I/II trials were docked using an efficient shape-based search algorithm and the AScore scoring function, to each of the class I HDAC members in order to identify the inhibitor or group with better pharmacological action. The molecular descriptors study and the drug score, drug likeness prediction helped in the identification of potential compounds targeting specific enzymes of HDAC family. The ranking of various groups of ligands helped in the identification of potential groups and better compound that can better target class I HDAC in an effective way.