Computational Study of the Binding Modes of Diverse DPN Analogues on Estrogen Receptors (ER) and the Biological Evaluation of a New Potential Antiestrogenic Ligand.

Estrogen (17β-estradiol) is essential for normal growth and differentiation in the mammary gland. In the last three decades, previous investigations have revealed that Estrogen Receptor Alpha (ERα) plays a critical role in breast cancer. More recently, observations regarding the widespread expression of ERβ-like proteins in normal and neoplastic mammary tissues have suggested that ERβ is also involved in the mentioned pathology.

Ligand recognition properties of the vasopressin V2 receptor studied under QSAR and molecular modeling strategies.

The design of new drugs that target vasopressin 2 receptor (V2R) is of vital importance to develop new therapeutic alternatives to treat diseases such as heart failure, polycystic kidney disease. To get structural insights related to V2R-ligand recognition, we have used a combined approach of docking, molecular dynamics simulations (MD) and quantitative structure-activity relationship (QSAR) to elucidate the detailed interaction of the V2R with 119 of its antagonists. The three-dimensional model of V2R was built by threading methods refining its structure through MD simulations upon which the 119 ligands were subjected to docking studies.