Ivanciuc, Ovidiu published the artcileSupport vector machines prediction of the mechanism of toxic action from hydrophobicity and experimental toxicity against Pimephales promelas and Tetrahymena pyriformis, Application of 2,4-Dichlorobenzamide, the publication is Internet Electronic Journal of Molecular Design (2004), 3(12), 802-821, database is CAplus.
Motivation: The prediction of the mechanism of action (MOA) using structural descriptors has major applications in selecting the appropriate quant. structure-activity relationships (QSAR) model, to identify chems. with similar toxicity mechanism, and in extrapolating toxic effects between different species and exposure regimes. Method: The SVM (support vector machines) algorithm was recently proposed as an efficient and flexible classification method for various bioinformatics and cheminformatics applications. In this study we have investigated the application of SVM for the classification of 337 organic compounds from eight MOA classes (nonpolar narcosis, polar narcosis, ester narcosis, amine narcosis, weak acid respiratory uncoupling, electrophilicity, proelectrophilicity, and nucleophilicity). The MOA classification was based on three indexes, namely: log Kow, the octanol-water partition coefficient; log 1/IGC50, the 50% inhibitory growth concentration against Tetrahymena pyriformis; log 1/LC50, the 50% lethal concentration against Pimephales promelas. The prediction power of each SVM model was evaluated with a leave – 5/% – out cross – validation procedure. Results: In order to find classification models with good predictive power, we have investigated a large number of SVM models obtained with the dot, polynomial, radial basis function, neural, and anova kernels. The MOA classification performances of SVM models depend strongly on the kernel type and various parameters that control the kernel shape. The discrimination between nonpolar narcotic compounds and the other chems. can be obtained with radial and anova SVM models, with a prediction accuracy of 0.80. The separation of less reactive compounds (polar, ester, and amine narcotics) from more reactive compounds (electrophiles, proelectrophiles, and nucleophiles) is obtained with a slightly higher error (prediction accuracy 0.71, obtained with radial SVM models). Conclusions: SVM models that use as input parameters hydrophobicity and exptl. toxicity against Pimephales promelas and Tetrahymena pyriformis represent an effective MOA classification method for a large diversity of organic compounds This approach can be used to predict the aquatic toxicity mechanism and to select the appropriate QSAR model for new chem. compounds
Internet Electronic Journal of Molecular Design published new progress about 2447-79-2. 2447-79-2 belongs to amides-buliding-blocks, auxiliary class Chloride,Amine,Benzene,Amide, name is 2,4-Dichlorobenzamide, and the molecular formula is C7H5Cl2NO, Application of 2,4-Dichlorobenzamide.
Referemce:
https://en.wikipedia.org/wiki/Amide,
Amide – an overview | ScienceDirect Topics