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. Design of new drugs both steroidal and nonsteroidal that target any of these receptors represents a promise to treat breast cancer, although it remains a challenge due to the sequence similarity between their catalytic domains. In this work we propose a new set of compounds that could effectively target the estrogen receptors ERα and ERβ. These ligands were designed based on the chemical structure of the ERβ-selective agonist diarylpropionitrile (DPN). The designed ligands were submitted to in silico ADMET studies, yielding in a filtered list of ligands that showed better drug-like properties. Molecular dynamics simulations and docking analysis were carried-out employing these compounds from which two of them were chosen considering their promising characteristics obtained from theoretical results. They were chemically synthetized and during the process, two precursor ligands were also obtained. These four ligands were subjected to biological studies, where it could be detected that ligand mol60b showed inhibitory activity and its ability to activate the transcription via an estrogenic mechanism of action was also determined. Interestingly, this result coincides well to the fact that the complex of ERβ-mol60b showed the highest ΔGbind value from the binding free energy calculations.

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. The theoretical results show that binding recognition of these ligands on V2R is diverse, but the main pharmacophore (electronic and π-π interactions) is maintained; thus, this information was validated under QSAR results. QSAR studies were performed using MLR analysis followed by ANN analysis to increase the model quality. The final equation was developed by choosing the optimal combination of descriptors after removing the outliers. The applicability domains of the constructed QSAR models were defined using the leverage and standardization approaches. The results suggest that the proposed QSAR models can reliably predict the reproductive toxicity potential of diverse chemicals, and they can be useful tools for screening new chemicals for safety assessment.

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