Liu, Ruifeng published the artcileLocally Weighted Learning Methods for Predicting Dose-Dependent Toxicity with Application to the Human Maximum Recommended Daily Dose, Category: benzothiophene, the main research area is drug toxicity prediction QSAR learning method.
Toxicol. experiments in animals are carried out to determine the type and severity of any potential toxic effect associated with a new lead compound The collected data are then used to extrapolate the effects on humans and determine initial dose regimens for clin. trials. The underlying assumption is that the severity of the toxic effects in animals is correlated with that in humans. However, there is a general lack of toxic correlations across species. Thus, it is more advantageous to predict the toxicol. effects of a compound on humans directly from the human toxicol. data of related compounds However, many popular quant. structure-activity relationship (QSAR) methods that build a single global model by fitting all training data appear inappropriate for predicting toxicol. effects of structurally diverse compounds because the observed toxicol. effects may originate from very different and mostly unknown mol. mechanisms. In this article, we demonstrate, via application to the human maximum recommended daily dose data that locally weighted learning methods, such as k-nearest neighbors, are well suited for predicting toxicol. effects of structurally diverse compounds We also show that a significant flaw of the k-nearest neighbor method is that it always uses a constant number of nearest neighbors in making prediction for a target compound, irresp. of whether the nearest neighbors are structurally similar enough to the target compound to ensure that they share the same mechanism of action. To remedy this flaw, we proposed and implemented a variable number nearest neighbor method. The advantages of the variable number nearest neighbor method over other QSAR methods include (1) allowing more reliable predictions to be achieved by applying a tighter mol. distance threshold and (2) automatic detection for when a prediction should not be made because the compound is outside the applicable domain.
Chemical Research in Toxicology published new progress about Drug screening. 40180-04-9 belongs to class benzothiophene, name is 2-(2,3-Dichloro-4-(thiophene-2-carbonyl)phenoxy)acetic acid, and the molecular formula is C13H8Cl2O4S, Category: benzothiophene.
Referemce:
Benzothiophene – Wikipedia,
Benzothiophene | C8H6S – PubChem