Structure-based computational evaluation of novel benzimidazole derivatives as potential inhibitors of mutant oestrogen receptor alpha (ERα)

Abstract

Objective: To evaluate, by structure-based computational methods, eleven novel benzimidazole derivatives (C1–C11) for their potential to bind and inhibit a mutant form of oestrogen receptor alpha (ERα), and to prioritise lead candidates for downstream computational and experimental validation.

Methods: The benzimidazole library (C1–C11) was constructed in ChemDraw and pre-optimised using AM1, followed by full geometry optimisations with DFT (B3LYP/6-31G). Optimised conformers were docked into the prepared ERα binding pocket using MOE (2019) following the authors’ established receptor preparation and docking workflow (including selected active-site waters) to generate multiple poses per ligand. Docking outputs (binding energy components, RMSD, interaction fingerprints) were tabulated and analysed to rank ligands and to characterise the molecular interactions that stabilise top poses.

Results: All eleven compounds produced energetically favourable docking poses with negative predicted binding energies and acceptable pose stabilities. A consistent ranking placed compounds C7 and C11 as top candidates, followed by C4 and C9. Top compounds combined directional hydrogen-bond anchoring with a dense network of aromatic/hydrophobic contacts to residues lining the ERα pocket, patterns that rationalise their superior predicted affinities. The reference ligand displayed fewer anchoring interactions, consistent with its lower predicted affinity.

Conclusions: Within the limits of docking-only workflows, C7 and C11 emerge as prioritised lead candidates for further computational refinement (redocking, MM-GBSA rescoring, MD simulations) and experimental testing. The benzimidazole scaffold remains a promising template for ERα-directed lead optimisation.