Cr-doped TiO 2 /SiO 2 nanostructured materials were prepared employing a layer-by-layer assembly technique. TiO 2 colloids were synthesized by a sol-gel method using TiCl 4 as a precursor. The experimental results showed that sphere-type TiO 2 particles on SiO 2 exhibited uniform shape and a narrow size distribution. The amount of Ti (wt %) increased as a function of the number of the coating layers. The coating layers was composed of anatase titania nanocrystals at 550 °C. The onset of band-gap transition for Cr-doped TiO 2 /SiO 2 showed a red shift compared with that for the undoped TiO 2 /SiO 2. And the photocata-lytic activity of Cr-doped TiO 2 /SiO 2 was higher than that of undoped sample.
Xenobiotic chemicals and their metabolites are mainly excreted out of our bodies by the urinary tract through the urine. Chemical-induced urinary tract toxicity is one of the main reasons that cause failure during drug development, and it is a common adverse event for medications, natural supplements, and environmental chemicals. Despite its importance, there are only a few in silico models for assessing urinary tract toxicity for a large number of compounds with diverse chemical structures. Here, we developed a series of qualitative and quantitative structure-activity relationship (QSAR) models for predicting urinary tract toxicity. In our study, the recursive feature elimination method incorporated with random forests (RFE-RF) was used for dimension reduction, and then eight machine learning approaches were used for QSAR modeling, i.e., relevance vector machine (RVM), support vector machine (SVM), regularized random forest (RRF), C5.0 trees, eXtreme gradient boosting (XGBoost), AdaBoost.M1, SVM boosting (SVMBoost), and RVM boosting (RVMBoost). For building classification models, the synthetic minority oversampling technique was used to handle the imbalance data set problem. Among all the machine learning approaches, SVMBoost based on the RBF kernel achieves both the best quantitative (q = 0.845) and qualitative predictions for the test set (MCC of 0.787, AUC of 0.893, sensitivity of 89.6%, specificity of 94.1%, and global accuracy of 90.8%). The application domains were then analyzed, and all of the tested chemicals fall within the application domain coverage. We also examined the structure features of the chemicals with large prediction errors. In brief, both the regression and classification models developed by the SVMBoost approach have reliable prediction capability for assessing chemical-induced urinary tract toxicity.
Androgen receptor (AR) plays important roles in the development of prostate cancer (PCa). The antagonistic drugs, which suppress the activity of AR, are widely used in the treatment of PCa. However, the molecular mechanism of antagonism about how ligands affect the structures of AR remains elusive. To better understand the conformational variability of ARs bound with agonists or antagonists, we performed long time unbiased molecular dynamics (MD) simulations and enhanced sampling simulations for the ligand binding domain of AR (AR-LBD) in complex with various ligands. Based on the simulation results, we proposed an allosteric pathway linking ligands and helix 12 (H12) of AR-LBD, which involves the interactions among the ligands and the residues W741, H874, and I899. The interaction pathway provides an atomistic explanation of how ligands affect the structure of AR-LBD. A repositioning of H12 was observed, but it is facilitated by the C-terminal of H12, instead of by the loop between helix 11 (H11) and H12. The bias-exchange metadynamics simulations further demonstrated the above observations. More importantly, the free energy profiles constructed by the enhanced sampling simulations revealed the transition process between the antagonistic form and agonistic form of AR-LBD. Our results would be helpful for the design of more efficient antagonists of AR to combat PCa.
Androgen receptor (AR) is closely associated with a group of hormone‐related diseases including the cancers of prostate, breast, ovary, pancreas, etc and anabolic deficiencies such as muscle atrophy and osteoporosis. Depending on the specific type and stage of the diseases, AR ligands including not only antagonists but also agonists and modulators are considered as potential therapeutics, which makes AR an extremely interesting drug target. Here, we at first review the current understandings on the structural characteristics of AR, and then address why and how AR is investigated as a drug target for the relevant diseases and summarize the representative antagonists and agonists targeting five prospective small molecule binding sites at AR, including ligand‐binding pocket, activation function‐2 site, binding function‐3 site, DNA‐binding domain, and N‐terminal domain, providing recent insights from a target and drug development view. Further comprehensive studies on AR and AR ligands would bring fruitful information and push the therapy of AR relevant diseases forward.
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