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Drgan, V.; Župerl, Š.; Vračko, M.; Como, F.; Novič, M. Robust modelling of acute toxicity towards fathead minnow (Pimephales promelas) using counter-propagation artificial neural networks and genetic algorithm. SAR QSAR Environ. Res. 2016, 27, 501–519.

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Drgan, V.; Župerl, Š.; Vračko, M.; Como, F.; Novič, M. Robust modelling of acute toxicity towards fathead minnow (Pimephales promelas) using counter-propagation artificial neural networks and genetic algorithm. SAR QSAR Environ. Res. 2016, 27, 501–519.

QDB archive DOI: 10.15152/QDB.238   DOWNLOAD

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Property pLC50: 96-h Fathead minnow toxicity as log(1/LC50) [mmol/L]

Model.2: Model with 18x18 neurons

Counter-propagation neural network (regression)

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Name Type n

R2

σ

Training set training 340 0.866 0.499
Validation set external validation 226 0.504 1.014
Model.8: Model with 25x25 neurons

Counter-propagation neural network (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 340 0.866 0.510
Validation set external validation 226 0.530 0.982

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Title: Drgan, V.; Župerl, Š.; Vračko, M.; Como, F.; Novič, M. Robust modelling of acute toxicity towards fathead minnow (Pimephales promelas) using counter-propagation artificial neural networks and genetic algorithm. SAR QSAR Environ. Res. 2016, 27, 501–519.
Abstract: Large worldwide use of chemicals has caused great concern about their possible adverse effects on human health, flora and fauna. Increased production of new chemicals has also increased demand for their risk assessment. Traditionally, results from animal tests have been used to assess toxicity of chemicals. However, such methods are ethically questionable since they involve killing and causing suffering of the test animals. Therefore, new in silico methods are being sought to replace the traditional in vivo and in vitro testing methods. In this article we report on one method that can be used to build robust models for the prediction of compounds’ properties from their chemical structure. The method has been developed by combining a genetic algorithm, a counter-propagation artificial neural network and cross-validation. It has been tested using existing data on toxicity to fathead minnow (Pimephales promelas). The results show that the method may give reliable results for chemicals belonging to the applicability domain of the developed models. Therefore, it can aid the risk assessment of chemicals and consequently reduce demand for animal tests.
URI: http://hdl.handle.net/10967/238
http://dx.doi.org/10.15152/QDB.238
Date: 2020-10-26


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