AIMC Topic: Toxicity Tests

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Prediction of the aquatic toxicity of aromatic compounds to tetrahymena pyriformis through support vector regression.

Oncotarget Jul 25, 2017
Toxicity evaluation is an extremely important process during drug development. It is usually initiated by experiments on animals, which is time-consuming and costly. To speed up such a process, a quantitative structure-activity relationship (QSAR) st...
Algorithms Reproducibility of Results Support Vector Machine Quantitative Structure-Activity Relationship Toxicity Tests Hydrocarbons, Aromatic Tetrahymena pyriformis
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Development of QSAR models using artificial neural network analysis for risk assessment of repeated-dose, reproductive, and developmental toxicities of cosmetic ingredients.

The Journal of toxicological sciences Apr 1, 2015
Use of laboratory animals for systemic toxicity testing is subject to strong ethical and regulatory constraints, but few alternatives are yet available. One possible approach to predict systemic toxicity of chemicals in the absence of experimental da...
Neural Networks, Computer Female Animals Male Risk Assessment Rats Reproduction Cosmetics No-Observed-Adverse-Effect Level Quantitative Structure-Activity Relationship Toxicity Tests
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[Application of support vector machine approach in studying nephron toxicity of Chinese medicinal materials].

Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica Mar 1, 2015
On the basis of web databases, 111 compounds with nephrotoxicity and 90 compounds without nephrotoxicity were collected as data set of nephrotoxicity discrimination model, 39 compounds with tubular necrosis and 39 compounds without tubular necrosis w...
Support Vector Machine Drugs, Chinese Herbal Toxicity Tests Nephrons
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