Identification of 1,5‐Benzothiazepine Derivatives as Potent Estrogen Receptor α Inhibitors via Integrated De Novo Fragment‐Based Designing and Pharmacophore Modeling
ABSTRACT Estrogen receptor alpha (ERα) signaling is a central driver of breast cancer (BC). Long‐term use of current anti‐estrogenic therapies is often compromised by the development of drug resistance and adverse effects. This highlights the urgent need for novel, safer molecules. This research explores 1,5‐benzothiazepine derivatives as potential ERα inhibitors, employing an integrated approach combining fragment‐based drug design (FBDD) and pharmacophore model‐based virtual screening. FBDD‐guided virtual screening of 2432 molecules revealed 8 FRAGHITs, whereas best pharmacophore model‐based virtual screening of 69,865 molecules yielded 9 hits comprising the 1,5‐benzothiazepine scaffold and desired pharmacophoric fingerprints. Findings from the docking score, binding mechanistic study, pharmacokinetic profiling, and binding free energy identified two promising candidates, HIT‐2 and FRAGHIT‐4. Among them, HIT‐2 demonstrated superior binding affinity, thermodynamic stability, drug‐likeness, and conformational robustness compared to both FRAGHIT4 and tamoxifen. Therefore, outcomes were further subjected to MD simulations and density functional theory (DFT) studies. Narrow RMSD, Rg, and RMSF indicated high stability and compactness of HIT‐2 compared to tamoxifen. DFT calculations further suggested high stability and bio‐feasibility. Activity prediction revealed significant antineoplastic (BC) potential for both HIT‐2 (Pa/Pi = 0.240/0.076) and FRAGHIT‐4 (Pa/Pi = 0.479/0.021) while reflecting safe oral toxicities. Findings suggested that HIT‐2 and FRAGHIT‐4 are novel potent anti‐estrogenic lead candidates in BC therapy.
Authors
- Vikas Kumar (ORCID: https://orcid.org/0000-0001-7516-7807)
- Agnidipta Das
- Vikas Jaitak (ORCID: https://orcid.org/0000-0002-2811-5251)
- Nidhi Bala
- Ashwini Pareek
- Kailash Jangid
Institutions
- Central University of Punjab (IN)
Publication Details
- Journal
- ChemistrySelect
- Published
- 2026-01-01
- DOI
- https://doi.org/10.1002/slct.202503946
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00