Robust modelling of acute toxicity towards fathead minnow (Pimephales promelas) using counter-propagation artificial neural networks and genetic algorithm
| Autor: | Š Župerl, F Como, Marjan Vračko, V Drgan, Marjana Novič |
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| Rok vydání: | 2016 |
| Předmět: |
Cyprinidae
Quantitative Structure-Activity Relationship Bioengineering 010501 environmental sciences Biology Risk Assessment 01 natural sciences Toxicology biology.animal Toxicity Tests Drug Discovery Genetic algorithm Animals Computer Simulation Organic Chemicals 0105 earth and related environmental sciences Artificial neural network Counter propagation General Medicine Minnow Acute toxicity 0104 chemical sciences 010404 medicinal & biomolecular chemistry Molecular Medicine Neural Networks Computer Biochemical engineering Pimephales promelas Risk assessment Algorithms Applicability domain |
| Zdroj: | SAR and QSAR in Environmental Research. 27:501-519 |
| ISSN: | 1029-046X 1062-936X |
| DOI: | 10.1080/1062936x.2016.1196388 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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