Quantitative Descriptor-Based Investigation of Estradiol Derivatives and Their Interactions with Estrogen Receptor Amino Acids

Satyendra Singh¹, Akhilesh Singh², Manish Kumar Gupta³, Mahendra Kumar Mishra⁴, Vivek Singh⁵, Abhishek Singh⁶

---

ABSTRACT

Estradiol and its synthetic derivatives constitute the structural core of estrogen receptor (ER) ligands and play a pivotal role in estrogen receptor signaling as well as in the development of selective estrogen receptor modulators (SERMs) for hormone-dependent disorders [1-4]. In the present study, a comprehensive and integrated descriptor-based quantitative structure–activity relationship (QSAR) approach combined with molecular docking have been employed to elucidate the molecular determinants governing ligand binding and subtype selectivity toward estrogen receptor alpha (ERα) and estrogen receptor beta (ERβ) [5-9].

A structurally diverse dataset of estradiol derivatives with experimentally determined binding affinities (Ki) was curated, and biological activity was expressed as pKi=−log10 (Ki) [10, 11]. A wide range of physicochemical descriptors (lipophilicity log P, polar surface area, molecular volume), three-dimensional field descriptors derived from CoMFA and CoMSIA, and quantum chemical descriptors obtained from density functional theory (DFT) calculations—including frontier molecular orbital energies (EHOMO, ELUMO), energy gap ΔE=ELUMO−EHOMO, dipole moment (μ), and molecular electrostatic potential—were systematically evaluated [12-18]. Multivariate statistical modeling was performed using partial least squares (PLS) regression, and model robustness was assessed through internal cross-validation and external test set prediction [19-22]. The optimal QSAR models demonstrated strong statistical significance, with correlation coefficients exceeding R² > 0.85, cross-validated coefficients Q² > 0.75, and low standard errors of estimation, confirming both predictive reliability and absence of overfitting. Comparative descriptor contribution analysis revealed pronounced subtype-specific structure–activity relationships, reflecting distinct steric, electrostatic, and hydrophobic requirements imposed by the divergent ligand-binding pockets of ERα and ERβ [16-24, 44-47].

Molecular docking studies further substantiated the QSAR findings by providing atomistic insights into ligand–receptor interactions. Critical hydrogen-bonding and electrostatic interactions involving conserved residues Glu353, Arg394, and His524 in ERα, and the corresponding Glu305, Arg346, and His475 in ERβ have identified as key determinants of binding affinity and receptor selectivity. Differences in residue orientation, pocket volume, and local electrostatic potential were shown to modulate ligand accommodation and stabilization, thereby rationalizing subtype-dependent activity [30-38] trends observed in the QSAR models [16-24, 44-47].

Overall, this integrated QSAR–docking framework offers a quantitative and mechanistic understanding of estradiol derivative recognition by ER subtypes. The findings provide a rational basis for structure-guided SERM design, enabling the prediction and optimization of ERα/ERβ selectivity through targeted modulation of molecular descriptors. This study thus contributes valuable computational insights for the development of next-generation estrogen receptor modulators with improved efficacy and safety profiles.

Keywords: Estradiol derivatives, Selective estrogen receptor modulators (SERMs), QSAR, DFT, Binding affinity, Structure–activity relationship (SAR), Computational drug design.

How to cite this article: Singh S, Singh A, Gupta MK, Mishra MK, Singh V, Singh A. Quantitative Descriptor-Based Investigation of Estradiol Derivatives and Their Interactions with Estrogen Receptor Amino Acids. Int J Drug Deliv Technol. 2026;16(7s): 856-865; DOI: 10.25258/ijddt.16.7s.91

Source of support: Nil

Conflict of interest: None