Identification of potent histone deacetylase 2 (HDAC2) inhibitors through combined structure and ligand-based designs and molecular modelling approach

Histone deacetylase 2 (HDAC2) is associated with various neuropathic degenerative diseases and is considered a novel target for Alzheimer's disease (AD). Elevated levels of HDAC2 trigger excitatory neurotransmission and reduce synaptic plasticity, synaptic number, and memory formation. In the current study, we identified HDAC2 inhibitors using an integrated structure and ligand-based approaches to drug design. Three pharmacophore models were generated by using different pharmacophoric features and validated using the Enrichment factor (EF), Guner-henry (GH) score, and percentage yield. The model of choice was used to screen a library of Zinc-15 compounds and interfering compounds were eliminated by using drug likeliness and PAINS filtering. Further, docking studies in three stages were carried out to obtain hits with good binding energies and were followed by ADMET studies yielding three virtual hits. The virtual hits, i.e. ZINC000008184553, ZINC0000013641114, and ZINC000032533141, were subjected to molecular dynamics simulation studies. Compound ZINC000008184553, identified as lead, was found to have optimal stability, low toxicity under simulated conditions, and may potentially inhibit HDAC2. [GRAPHICS] .