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Life sciences · Journal article
Sar and Qsar in Environmental Research · September 7, 2026
Raises a question worth testing. It does not answer one.
This is a computational drug design study using molecular docking, molecular dynamics simulation, and binding free energy calculations to identify quinoxaline derivatives as potential ERα modulators for breast cancer. No experimental or clinical data are provided; the findings represent in silico predictions only and require experimental validation.
In silico structure-based virtual screening and molecular modelling study. Intervention: Computational screening and structure-based refinement of quinoxaline derivatives as ERα modulators. Compared with: Tamoxifen (reference ligand for docking score comparison).
Top-ranked quinoxaline candidates exhibited more favourable predicted docking scores than reference ligand tamoxifen MD trajectory analyses indicated stable complex formation with sustained predicted interactions at key ERα binding site residues LIG3 identified as most promising lead with MM-PBSA binding free energy ΔG = −34.15 kcal/mol
ADMET profiling predicted favourable pharmacokinetic behaviour and low toxicity for prioritized compounds
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Computer-aided drug design study identifying candidate compounds through virtual screening and molecular modelling; no experimental validation, synthesis, or biological testing reported.
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Oestrogen receptor alpha (ERα) is a driver of hormone-dependent breast cancer, yet current therapies are often hindered by drug resistance and adverse effects. In this study, we developed a structure-based virtual screening workflow to identify novel quinoxaline derivatives as computationally prioritized potential ERα modulators. The quinoxaline analogues obtained from PubChem are screened through validated docking model of ERα. The top-ranked candidates were further refined by MD simulations in GROMACS for 300 ns to assess complex stability and interaction persistence, followed by MM-PBSA binding free energy calculations to quantify binding energetics. The prioritized quinoxaline hits exhibited more favourable predicted docking scores than reference ligand, tamoxifen, while MD trajectory analyses indicated stable complex formation with sustained predicted interactions involving key ERα binding site residues. MM-PBSA highlights LIG3 as the most promising lead with a favourable energy (ΔG = -34.15 kcal/mol). Complementary ADMET profiling predicted favourable pharmacokinetic behaviour and low toxicity for the prioritized compounds. This integrated in silico strategy demonstrates that quinoxaline scaffolds, specifically LIG3, represent promising computationally prioritized templates for the further development and experimental evaluation of ERα targeted ligands. These findings provide a reliable computational framework for the prioritization and structural optimization of next-generation anti-breast cancer agents.
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