Niculescu, S. P. published the artcileUsing fragment chemistry data mining and probabilistic neural networks in screening chemicals for acute toxicity to the fathead minnow, Synthetic Route of 2447-79-2, the publication is SAR and QSAR in Environmental Research (2004), 15(4), 293-309, database is CAplus and MEDLINE.
The paper is illustrating how the general data mining methodol. may be adapted to provide solutions to the problem of high throughput virtual screening of organic chems. for possible acute toxicity to the fathead minnow fish. The present approach involves mining fragment information from chem. structures and is using probabilistic neural networks to model the relationship between structure and toxicity. Probabilistic neural networks implement a special class of multivariate non-linear Bayesian statistical models. The math. principles supporting their use for value prediction purposes are clarified and their peculiarities discussed. As part of the research phase of the data mining process, a dataset consisting of 800 structures and associated fathead minnow (Pimephales promelas) 96-h LC50 acute toxicity endpoint information is used for both the purpose of identifying an advantageous combination of fragment descriptors and for training the neural networks. As a result, two powerful models are generated. Model 1 implements the basic PNN with Gaussian kernel (statistical corrections included) while Model 2 implements the PNN with Gaussian kernel and separated variables. External validation is performed using a sep. dataset consisting of 86 structures and associated toxicity information. Both learning and generalization capabilities of the two models are investigated and their limitations discussed.
SAR and QSAR in Environmental Research 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, Synthetic Route of 2447-79-2.
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https://en.wikipedia.org/wiki/Amide,
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