Human Estrogen Receptor α Antagonists. Part 1: 3-D QSAR-Driven Rational Design of Innovative Coumarin-Related Antiestrogens as Breast Cancer Suppressants through Structure-Based and Ligand-Based Studies

Abstract

The estrogen receptor α (ERα) represents a 17β-estradiol-inducible transcriptional regulator that initiates the RNA polymerase II-dependent transcriptional machinery, pointed for breast cancer (BC) development via either genomic direct or genomic indirect (i.e., tethered) pathway. To develop innovative ligands, structure-based (SB) three-dimensional (3-D) quantitative structure–activity relationship (QSAR) studies have been undertaken from structural data taken from partial agonists, mixed agonists/antagonists (selective estrogen receptor modulators (SERMs)), and full antagonists (selective ERα downregulators (SERDs)) correlated with either wild-type or mutated ERα receptors. SB and ligand-based (LB) alignments allow us to rule out guidelines for the SB/LB alignment of untested compounds. 3-D QSAR models for ERα ligands, coupled with SB/LB alignment, were revealed to be useful tools to dissect the chemical determinants for ERα-based anticancer activity as well as to predict their potency. The herein developed protocol procedure was verified through the design and potency prediction of 12 new coumarin-based SERMs, namely, 3DQ-1a to 3DQ-1e, that upon synthesis turned to be potent ERα antagonists by means of either in vitro or in vivo assays (described in the second part of this study).

(Heteroarylmethyl)benzoic Acids as a New Class of Bacterial Cystathionine γ-Lyase Inhibitors: Synthesis, Biological Evaluation, and Molecular Modeling

“…To individuate the docking program able to reproduce the experimental binding poses, Plants and Smina docking software with their respective scoring functions were engaged as previously reported. , Although the docking assessment protocol used was previously shown to be effective, this time, no program/scoring function pair was able to reproduce the experimental ligand binding conformation with an acceptable root mean square deviation (RMSD) (Supporting Information Table S2–S1). At deeper analysis, the complexed experimental conformation of NL1, NL2, and NL3 overlapped by a structure-based alignment of the complexes revealed the lack of a common binding mode among the three NL derivatives.…”

Bovine Serum Amine Oxidase and Polyamine Analogues: Chemical Synthesis and Biological Evaluation Integrated with Molecular Docking and 3-D QSAR Studies

“…At last, modeling investigation of a ComBinE model was developed as a final selection tool for new potential BSAO substrates. As the method associated docking and SB 3-D QSAR, , it implies the highest level of investigation that could lead to the selection/design of new potential BSAO substrates. Inspection of the Py-ComBinE model derived on the PA Min3‑vinardo data set gave insights into the substrate/BSAO residue interactions to be avoided or maintained for future designed PA derivatives.…”

“…56 At last, modeling investigation of a ComBinE model was developed as a final selection tool for new potential BSAO substrates. As the method associated docking and SB 3-D QSAR, 57,58 it implies the highest level of investigation that could lead to the selection/design of…”

StackER: a novel SMILES-based stacked approach for the accelerated and efficient discovery of ERα and ERβ antagonists

“…To address the problem of resistance, researchers are exploring various computer-assisted approaches for drug design. These methods include quantitative structure–activity relationship (QSAR) 10 – 12 , machine learning (ML)-based models 13 – 15 , deep learning (DL)-based models 16 , molecular docking 10 , 17 , 18 , molecular dynamic simulations 18 , 19 , and pharmacophore analysis 18 , among others. It's important to note that most of these research endeavors primarily focus on targeting ERα rather than ERβ 20 .…”