Modeling acute toxicity of chemicals to Daphnia magna: a probabilistic neural network approach was written by Kaiser, Klaus L. E.;Niculescu, Stefan P.. And the article was included in Environmental Toxicology and Chemistry in 2001.Related Products of 2272-40-4 The following contents are mentioned in the article:
A methodol. based on probabilistic neural networks (PNNs) is applied to model the acute toxicity (48-h LC50) of a set of 700 highly diverse chems. to Daphnia magna. First, cross-validation experiments confirming the potential use of the PNN as modeling tool for the problem at hand were performed. Next, various approaches to construct-improved models are presented. The resulting four models are then validated using an external test set of 76 addnl. compounds Input to the PNNs is derived solely from simple mol. descriptors and structural fragments and excludes bulk property parameters, such as the water solubility or the octanol/water partition coefficient This study involved multiple reactions and reactants, such as 4,6-Dichloro-N-phenyl-1,3,5-triazin-2-amine (cas: 2272-40-4Related Products of 2272-40-4).
4,6-Dichloro-N-phenyl-1,3,5-triazin-2-amine (cas: 2272-40-4) belongs to organic chlorides. Organic chlorides can cause corrosion in pipelines, valves and condensers, and cause catalyst poisoning. The hydrocarbon processing industry (HPI) and others are affected by damage caused by these substances. Organochlorine compounds are lipophylic, meaning they are more soluble in fat than in water. This gives them a high tenancy to accumulate in the food chain (biomagnification).Related Products of 2272-40-4
Referemce:
Chloride – Wikipedia,
Chlorides – an overview | ScienceDirect Topics