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J Chem Inf Comput Sci ; 43(2): 513-8, 2003.
Article in English | MEDLINE | ID: mdl-12653515

ABSTRACT

The need for general reliable models for predicting toxicity has led to the use of artificial intelligence. We applied neural and fuzzy-neural networks with the QSAR approach. We underline how the networks have to be tuned on the data sets generally involved in modeling toxicity. This study was conducted on 562 organic compounds in order to establish models for predictive the acute toxicity in fish.


Subject(s)
Fuzzy Logic , Neural Networks, Computer , Organic Chemicals/toxicity , Toxicity Tests/methods , Animals , Cyprinidae , Data Interpretation, Statistical , Models, Biological , Models, Chemical , Quantitative Structure-Activity Relationship
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