Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

Machine learning models based on molecular descriptors to predict human and environmental toxicological factors in continental freshwater

Abstract : It is a real challenge for life cycle assessment practitioners to identify all relevant substances contributing to the ecotoxicity. Once this identification has been made, the lack of corresponding ecotoxicity factors can make the results partial and difficult to interpret. So, it is a real and important challenge to provide ecotoxicity factors for a wide range of compounds. Nevertheless, obtaining such factors using experiments is tedious, time-consuming, and made at a high cost. A modeling method that could predict these factors from easy-to-obtain information on each chemical would be of great value. Here, we present such a method, based on machine learning algorithms, that used molecular descriptors to predict two specific endpoints in continental freshwater for ecotoxicological and human impacts. The method shows good performances on a learning database. Then, predictions were derived from the validated model for compounds with missing toxicity/ecotoxicity factors.
Document type :
Preprints, Working Papers, ...
Complete list of metadata

https://hal.inrae.fr/hal-03109818
Contributor : Rémi Servien <>
Submitted on : Thursday, January 14, 2021 - 8:57:43 AM
Last modification on : Monday, April 12, 2021 - 4:37:15 PM
Long-term archiving on: : Thursday, April 15, 2021 - 6:18:06 PM

File

version_hal.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-03109818, version 1

Citation

Rémi Servien, Eric Latrille, Dominique Patureau, Arnaud Hélias. Machine learning models based on molecular descriptors to predict human and environmental toxicological factors in continental freshwater. 2021. ⟨hal-03109818⟩

Share

Metrics

Record views

141

Files downloads

38