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The interleukin-1 receptor like ST2 has emerged as a potential drug discovery target since it was identified as the receptor of the novel cytokine IL-33, which is involved in many inflammatory and autoimmune diseases. For the treatment of such IL-33-related disorders, efforts have been made to discover molecules that can inhibit the protein–protein interactions (PPIs) between IL-33 and ST2, but to date no drug has been approved. Although several anti-ST2 antibodies have entered clinical trials, the exploration of small molecular inhibitors is highly sought-after because of its advantages in terms of oral bioavailability and manufacturing cost. The aim of this study was to discover ST2 receptor inhibitors based on its PPIs with IL-33 in crystal structure (PDB ID: 4KC3) using virtual screening tools with pharmacophore modeling and molecular docking. From an enormous chemical space ZINC, a potential series of compounds has been discovered with stronger binding affinities than the control compound from a previous study. Among them, four compounds strongly interacted with the key residues of the receptor and had a binding free energy?<????20 kcal/mol. By intensive calculations using data from molecular dynamics simulations, ZINC59514725 was identified as the most potential candidate for ST2 receptor inhibitor in this study.

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ABCG2 is an ABC membrane protein reverse transport pump, which removes toxic substances such as medicines out of cells. As a result, drug bioavailability is an unexpected change and negatively influences the ADMET (absorption, distribution, metabolism, excretion, and toxicity), leading to multi-drug resistance (MDR). Currently, in spite of promising studies, screening for ABCG2 inhibitors showed modest results. The aim of this study was to search for small molecules that could inhibit the ABCG2 pump. We first used the WISS MODEL automatic server to build up ABCG2 homology protein from 655 amino acids. Pharmacophore models, which were con-structed based on strong ABCG2 inhibitors (IC50 < 1 μM), consist of two hydrophobic (Hyd) groups, two hydrogen bonding acceptors (Acc2), and an aromatic or conjugated ring (Aro|PiR). Using molecular docking method, 714 substances from the DrugBank and 837 substances from the TCM with potential to inhibit the ABCG2 were obtained. These chemicals maybe favor synthesized or extracted and bioactivity testing.  相似文献   
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The human P-glycoprotein (P-gp) efflux pump is of great interest for medicinal chemists because of its important role in multidrug resistance (MDR). Because of the high polyspecificity as well as the unavailability of high-resolution X-ray crystal structures of this transmembrane protein, ligand-based, and structure-based approaches which were machine learning, homology modeling, and molecular docking were combined for this study. In ligand-based approach, individual two-dimensional quantitative structure–activity relationship models were developed using different machine learning algorithms and subsequently combined into the Ensemble model which showed good performance on both the diverse training set and the validation sets. The applicability domain and the prediction quality of the developed models were also judged using the state-of-the-art methods and tools. In our structure-based approach, the P-gp structure and its binding region were predicted for a docking study to determine possible interactions between the ligands and the receptor. Based on these in silico tools, hit compounds for reversing MDR were discovered from the in-house and DrugBank databases through virtual screening using prediction models and molecular docking in an attempt to restore cancer cell sensitivity to cytotoxic drugs.  相似文献   
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