Identification of Selective Receptor Modulators Using Pharmacoinformatics Approaches for Therapeutic Application in Estrogen Therapy

Abstract

Pharmacoinformatics strategies have been applied to explore promising selective estrogen receptor (ER) modulators (SERMs). A set of non-steroidal ligands was considered for both ERα and ERβ subtypes. Best pharmacophore models revealed with importance of hydrogen bond acceptor and hydrophobicity for both subtypes, along with an aromatic ring and hydrogen bond donor for α and β subtypes, respectively. Both models were validated, and further considered for virtual screening of National Cancer Institute database. Initial hits were sorted with a number of criteria, and finally the molecules have been proposed as promising SERMs. A molecular docking study explained that screened ligands formed a number of binding interactions with both ERs. The subtype receptors in complex with active and screened compounds were considered for molecular simulations to compare stability of the complexes. An analysis of binding energy found that screened ligands hold a strong affinity towards the selective receptor cavity. The proposed ligands might be promising leads for estrogen therapy after experimental validation tests.