Unlike 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 their respective values of RMSD, RMSF, H-bonds, the radius of gyration, and SASA graphs. To analyse ligand-protein interactions throughout the entire 120 ns trajectory, a more advanced MM/PBSA method is utilized, where six selected ligands (CMR1, CMR2, CMR3, CMR4, CMR5 and CMR9) illustrate promising results for inhibition of the ERα receptor as assessed through MM/BBSA analysis. The CMR9 has the highest MM/BBSA binding free energy (-14.46 kcal/mol). The ADMET analysis reveals that CMR4 has maximum intestinal absorption (6.68) and clearance rate (0.1). All the compounds are non-toxic and safe to use. These findings indicate the potential of involving different computational techniques to design the ligand structures and to study the ligand-protein interactions for better understanding and achieving more potent synthetic inhibitors for breast cancer.

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http://dx.doi.org/10.1016/j.compbiolchem.2024.108020DOI Listing

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