The Index of Ideality of Correlation: QSAR Model of Acute Toxicity for Zebrafish (Danio rerio) Embryo View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-04

AUTHORS

Andrey Andreevich Toropov, Alla Petrovna Toropova, Emilio Benfenati

ABSTRACT

Acute aquatic toxicity is a complex phenomenon. Experimental measurement of endpoints related to the phenomenon is expensive and takes time. However, data on the above endpoints are needed to solve practical tasks of ecology in cooperation with industry and agriculture control. Optimal descriptors calculated with simplified molecular input-line entry system were used to build up quantitative structure–activity relationships for acute toxicity of zebrafish embryo, expressed via negative decimal logarithm of molar concentration of the dose leading to death in 50% organisms (pLC50). The index of ideality of correlation has been used to improve the predictive potential of the model. Mechanistic interpretation of the model in terms of promoters (molecular alerts) of increase or decrease of the endpoint is suggested. The average statistical characteristics of the model for the external validation sets are the following: average correlation coefficient equal to 0.697; and average root mean squared error equal to 0.93. The predictive potential of the model has been confirmed with three random splits into the training and validation sets. The Monte Carlo method is used to build up predictive models for zebrafish toxicity;The CORAL software available on the Internet used for corresponding calculations;The structural alerts related to the toxicity are suggested;The index of ideality of correlation applied to improve CORAL models. The Monte Carlo method is used to build up predictive models for zebrafish toxicity; The CORAL software available on the Internet used for corresponding calculations; The structural alerts related to the toxicity are suggested; The index of ideality of correlation applied to improve CORAL models. More... »

PAGES

1-8

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s41742-019-00183-y

DOI

http://dx.doi.org/10.1007/s41742-019-00183-y

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https://app.dimensions.ai/details/publication/pub.1112987951


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