Category: Triazoles. In 2020.0 ENVIRON SCI TECHNOL published article about THROUGHPUT SCREENING ASSAYS; DRUG DISCOVERY; BINDING AFFINITIES; TOXCAST CHEMICALS; QSAR; IDENTIFICATION; CLASSIFICATION; AGREEMENT; SET in [Zorn, Kimberley M.; Foil, Daniel H.; Lane, Thomas R.; Ekins, Sean] Collaborat Pharmaceut Inc, Raleigh, NC 27606 USA; [Russo, Daniel P.] Rutgers State Univ, Ctr Computat & Integrat Biol, Camden, NJ 08102 USA; [Hillwalker, Wendy; Feifarek, David J.; Jones, Frank; Klaren, William D.; Brinkman, Ashley M.] SC Johnson & Son Inc, Global Prod Safety, Racine, WI 53404 USA in 2020.0, Cited 60.0. The Name is 1H-1,2,4-Triazol-5-amine. Through research, I have a further understanding and discovery of 61-82-5.
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 published by the EPA. When assessing six additional algorithms for the training data sets, Assay Central performed similarly at a reduced computational cost. This study demonstrates that machine learning can prioritize chemicals for future in vitro and in vivo testing of ER agonism.
Category: Triazoles. Welcome to talk about 61-82-5, If you have any questions, you can contact Zorn, KM; Foil, DH; Lane, TR; Russo, DP; Hillwalker, W; Feifarek, DJ; Jones, F; Klaren, WD; Brinkman, AM; Ekins, S or send Email.
Reference:
Article; Safari, Niloufar; Shirini, Farhad; Tajik, Hassan; Journal of Molecular Structure; vol. 1201; (2020);,
1,2,3-Triazole – Wikipedia,
Triazoles – an overview | ScienceDirect Topics