Identification of pharmacophore synergism for optimization of estrogen receptor beta binders for hormone dependent forms of breast cancer

• A structurally diverse dataset of estrogen receptor beta. • QSAR model has an excellent balance of mechanistic interpretations and external predictive ability. • Successful identification of reported and novel results. • Results are well supported by x-ray resolved structure of estrogen receptor beta binders. • Molecular docking and molecular dynamics are also executed, which provided consensus and complementary results. Certain forms of breast cancer, particularly influenced by estrogen hormone, prompt the investigation of estrogen receptors as potential targets for therapeutic interventions. The drug discovery pipeline against breast cancer requires the identification and retention of crucial pharmacophoric features of inhibitors using a multitude of inhibitors comprising diverse scaffolds. In the present study, our focus was on conducting an e-QSAR (easy, efficient, economical, ecofriendly, and explainable QSAR), molecular docking and molecular dynamics simulations analyses on a diverse range of inhibitors that target Estrogen Receptor beta. The newly developed QSAR model upholds a balance between predictive accuracy with R 2 tr = 0.799, Q 2 LMO = 0.792, and CCCex = 0.886 and also provides mechanistic insights, thus adhering to the guidelines set forth by the Organisation for Economic Co-operation and Development (OECD). The analyses reveal that atoms with sp 2 -hybridization, specifically carbon and nitrogen atoms, have a significant impact on the binding profile along with lipophilic atoms. Additionally, a specific combination of hydrogen bond donors and acceptors involving carbon, nitrogen, and ring sulfur atoms also plays a crucial role. These novel findings have the potential to greatly aid future drug development targeting estrogen receptor beta.