Lei, Tailong published the artcileADMET Evaluation in Drug Discovery. Part 17: Development of Quantitative and Qualitative Prediction Models for Chemical-Induced Respiratory Toxicity, SDS of cas: 16332-06-2, the publication is Molecular Pharmaceutics (2017), 14(7), 2407-2421, database is CAplus and MEDLINE.
As a dangerous end point, respiratory toxicity can cause serious adverse health effects and even death. Meanwhile, it is a common and traditional issue in occupational and environmental protection. Pharmaceutical and chem. industries have a strong urge to develop precise and convenient computational tools to evaluate the respiratory toxicity of compounds as early as possible. Most of the reported theor. models were developed based on the respiratory toxicity data sets with one single symptom, such as respiratory sensitization, and therefore these models may not afford reliable predictions for toxic compounds with other respiratory symptoms, such as pneumonia or rhinitis. Here, based on a diverse data set of mouse i.p. respiratory toxicity characterized by multiple symptoms, a number of quant. and qual. predictions models with high reliability were developed by machine learning approaches. First, a four-tier dimension reduction strategy was employed to find an optimal set of 20 mol. descriptors for model building. Then, six machine learning approaches were used to develop the prediction models, including relevance vector machine (RVM), support vector machine (SVM), regularized random forest (RRF), extreme gradient boosting (XGBoost), naïve Bayes (NB), and linear discriminant anal. (LDA). Among all of the models, the SVM regression model shows the most accurate quant. predictions for the test set (q2ext = 0.707), and the XGBoost classification model achieves the most accurate qual. predictions for the test set (MCC of 0.644, AUC of 0.893, and global accuracy of 82.62%). The application domains were analyzed, and all of the tested compounds fall within the application domain coverage. We also examined the structural features of the compounds and important fragments with large prediction errors. In conclusion, the SVM regression model and the XGBoost classification model can be employed as accurate prediction tools for respiratory toxicity.
Molecular Pharmaceutics published new progress about 16332-06-2. 16332-06-2 belongs to ethers-buliding-blocks, auxiliary class Amine,Aliphatic hydrocarbon chain,Amide,Ether, name is 2-Methoxyacetamide, and the molecular formula is C3H7NO2, SDS of cas: 16332-06-2.
Referemce:
https://en.wikipedia.org/wiki/Ether,
Ether | (C2H5)2O – PubChem