Computational strategies to address therapeutic resistance in estrogen receptor‐positive breast cancer: A narrative review of molecular docking, molecular dynamics simulations, and emerging therapeutic targets

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Abstract Background Estrogen receptor‐positive (ER+) breast cancer represents the most common molecular subtype of breast cancer and remains highly responsive to endocrine therapy. However, therapeutic efficacy is frequently compromised by acquired or intrinsic endocrine resistance, particularly through ESR1 mutations, altered receptor conformation, co‐regulatory mechanisms, and activation of compensatory signaling pathways such as PI3K/AKT/mTOR. Conventional drug‐discovery approaches are often slow, resource‐intensive, and insufficiently equipped to address the structural and molecular heterogeneity underlying treatment resistance. Objective This review evaluates the contribution of computationally aided drug discovery, with particular emphasis on molecular docking, molecular dynamics (MD) simulations, and translational clinical targeting, to the development of more effective therapeutic strategies for ER+ breast cancer. Methods A narrative review approach was used to synthesize evidence concerning computational drug‐design strategies in ER+ breast cancer. Emphasis was placed on studies applying molecular docking to predict ligand receptor interactions and binding modes, MD simulations to evaluate structural stability, conformational dynamics, and ligand persistence, and integrative computational frameworks incorporating genomic, molecular, pharmacological, and clinical information. Results Computational approaches have substantially expanded the capacity to identify, characterise, and optimise therapeutic candidates for ER+ breast cancer. Molecular docking enables rapid structural screening and prioritisation of compounds according to predicted receptor interactions and binding configurations, while MD simulations provide complementary information regarding ligand stability, receptor flexibility, conformational transitions, and mutation‐associated alterations in ERα behaviour. Conclusion The integration of molecular docking, MD simulations, and translational clinical targeting provides a powerful framework for accelerating therapeutic discovery in ER+ breast cancer. Beyond reducing the time and cost associated with conventional drug development, computational approaches enable mechanistically informed targeting of receptor mutations, adaptive signaling networks, and molecular determinants of endocrine resistance.