Zorn, Kimberley M. published the artcileMachine Learning Models for Estrogen Receptor Bioactivity and Endocrine Disruption Prediction, Computed Properties of 637-07-0, the publication is Environmental Science & Technology (2020), 54(19), 12202-12213, database is CAplus and MEDLINE.
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 this data to construct math. models of ER agonist and antagonist pathways to prioritize chems. for endocrine disruption testing. However, math. models require in vitro data prior to predicting estrogenic activity, but machine learning methods are capable of prospective prediction from mol. 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 chems. with agonist activity classifications were compared to previous math. model publications. Training datasets were subjected to addnl. 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 published by the EPA. When assessing six addnl. algorithms for the training datasets, Assay CentralTM performed similarly at a reduced computational cost. This study demonstrates machine learning can prioritize chems. for future in vitro and in vivo testing of ER agonism.
Environmental Science & Technology published new progress about 637-07-0. 637-07-0 belongs to chlorides-buliding-blocks, auxiliary class Inhibitor,Cell Cycle,PPAR, name is Ethyl 2-(4-chlorophenoxy)-2-methylpropanoate, and the molecular formula is C3H8N2S, Computed Properties of 637-07-0.
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