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https://www.selleckchem.com/products/ca77-1.html Flavin-dependent "ene"-reductases can generate stabilized alkyl radicals when irradiated with visible light; however, they are not known to form unstabilized radicals. Here, we report an enantioselective radical cyclization using alkyl iodides as precursors to unstabilized nucleophilic radicals. Evidence suggests this species is accessed by photoexcitation of a charge-transfer complex that forms between flavin and substrate within the protein active site. Stereoselective delivery of a hydrogen atom from the flavin semiquinone to the prochiral radical formed after cyclization provides high levels of enantioselectivity across a variety of substrates. Overall, this transformation demonstrates that photoenzymatic catalysis can address long-standing selectivity challenges in the radical literature.The U.S. Environmental Protection Agency (EPA) periodically releases in vitro data across a variety of targets, including the estrogen receptor (ER). In 2015, the EPA used these data to construct mathematical models of ER agonist and antagonist pathways to prioritize chemicals for endocrine disruption testing. However, mathematical models require in vitro data prior to predicting estrogenic activity, but machine learning methods are capable of prospective prediction from the molecular structure alone. The current study describes the generation and evaluation of Bayesian machine learning models grouped by the EPA's ER agonist pathway model using multiple data types with proprietary software, Assay Central. External predictions with three test sets of in vitro and in vivo reference chemicals with agonist activity classifications were compared to previous mathematical model publications. Training data sets were subjected to additional machine learning algorithms and compared with rank normalized scores of internal five-fold cross-validation statistics. External predictions were found to be comparable or superior to previous studies

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