Computational prediction for designing novel ketonic derivatives as potential inhibitors for breast cancer: A trade-off between drug likeness and inhibition potency

Comput Biol Chem. 2024 Jan 17;109:108020. doi: 10.1016/j.compbiolchem.2024.108020. Online ahead of print.ABSTRACTUnlike simple molecular screening, a combined hybrid computational methodology has been applied which includes quantum chemical methods, molecular docking, and molecular dynamics simulations to design some novel ketonic derivatives. The current study contains the derivatives of an experimental ligand which are designed as a trade-off between drug likeness and inhibition strength. We investigate the interaction of various newly designed ketonic compounds with the breast cancer receptor known as the Estrogen Receptor Alpha (ERα). The molecular structures of all newly designed ligands were studied quantum chemically in terms of their fully optimized structures, 3-D molecular orbital distributions, global chemical descriptors, molecular electrostatic potentials and energies of frontier molecular orbitals (FMOs). All ligands under study show good binding affinities with the ERα protein. The ligands CMR2 and CMR4 exhibit improved molecular docking interactions. The intermolecular interactions indicate that CMR4 demonstrates better hydrophobic and hydrogen bonding interactions with protein (ERα). Furthermore, molecular dynamics simulations were conducted on ligands and reference drugs interacting with the ERα protein over a time span of 120 nanoseconds. The molecular dynamics results are interpreted in terms of ligand-protein stability and flexible behaviour based on ...
Source: Computational Biology and Chemistry - Category: Bioinformatics Authors: Source Type: research