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Current Topics in Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 1568-0266
ISSN (Online): 1873-4294

Review Article

Molecular Dynamics Simulations of HDAC-ligand Complexes Towards the Design of New Anticancer Compounds

Author(s): Varun Dewaker and Yenamandra S. Prabhakar*

Volume 23, Issue 29, 2023

Published on: 28 September, 2023

Page: [2743 - 2764] Pages: 22

DOI: 10.2174/0115680266250924230920042845

Price: $65

Abstract

Quantitative Structure-activity Relationship (QSAR) studies gained a foothold in the mid-1960s to rationalise the biological activity of medicinally important compounds. Since then, the advancements in computer hardware and software added many new techniques and areas to this field of study. Molecular dynamics (MD) simulations are one such technique in direct drug design approaches. MD simulations have a special place in drug design studies because they decode the dynamics of intermolecular interactions between a biological target and its potential ligands/inhibitors. The trajectories from MD simulations provide different non-bonding interaction parameters to assess the compatibility of the protein-ligand complex and thereby facilitate the design of prospective compounds prior to their wet-lab exploration. Histone deacetylases (HDACs) play a key role in epigenetics and they are promising drug targets for cancer and various other diseases. This review attempts to shed some light on the modelling studies of HDAC inhibitors as anticancer agents. In view of the advantages of MD simulations in direct drug design, this review also discusses the fragment-based approach in designing new inhibitors of HDAC8 and HDAC2, starting from the interaction energies of ligand fragments obtained from the MD simulations of respective protein-ligand complexes. Here, the design of new anticancer compounds from largazole thiol, trichostatin A, vorinostat, and several other prototype compounds are reviewed. These studies may stimulate the interest of medicinal chemists in MD simulations as a direct drug design approach for new drug development.

Graphical Abstract

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