Section: Ecotoxicology & Environmental Chemistry
Topic: Environmental sciences
Machine learning models based on molecular descriptors to predict human and environmental toxicological factors in continental freshwater
10.24072/pcjournal.90 - Peer Community Journal, Volume 2 (2022), article no. e15.
Get full text PDF Peer reviewed and recommended by PCI
It is a real challenge for life cycle assessment practitioners to identify all relevant substances contributing to 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 different tested machine learning algorithms show good performances on a learning database and the non-linear methods tend to outperform the linear ones. The cluster-then-predict approaches usually show the best performances, which suggests that these predicted models must be derived for somewhat similar compounds. Finally, predictions were derived from the validated model for compounds with missing toxicity/ecotoxicity factors.
Type: Research article
CC-BY 4.0
@article{10_24072_pcjournal_90,
author = {Servien, R\'emi and Latrille, Eric and Patureau, Dominique and H\'elias, Arnaud},
title = {Machine learning models based on molecular descriptors to predict human and environmental toxicological factors in continental freshwater},
journal = {Peer Community Journal},
eid = {e15},
publisher = {Peer Community In},
volume = {2},
year = {2022},
doi = {10.24072/pcjournal.90},
url = {https://peercommunityjournal.org/articles/10.24072/pcjournal.90/}
}
TY - JOUR AU - Servien, Rémi AU - Latrille, Eric AU - Patureau, Dominique AU - Hélias, Arnaud TI - Machine learning models based on molecular descriptors to predict human and environmental toxicological factors in continental freshwater JO - Peer Community Journal PY - 2022 VL - 2 PB - Peer Community In UR - https://peercommunityjournal.org/articles/10.24072/pcjournal.90/ DO - 10.24072/pcjournal.90 ID - 10_24072_pcjournal_90 ER -
%0 Journal Article %A Servien, Rémi %A Latrille, Eric %A Patureau, Dominique %A Hélias, Arnaud %T Machine learning models based on molecular descriptors to predict human and environmental toxicological factors in continental freshwater %J Peer Community Journal %D 2022 %V 2 %I Peer Community In %U https://peercommunityjournal.org/articles/10.24072/pcjournal.90/ %R 10.24072/pcjournal.90 %F 10_24072_pcjournal_90
Servien, Rémi; Latrille, Eric; Patureau, Dominique; Hélias, Arnaud. Machine learning models based on molecular descriptors to predict human and environmental toxicological factors in continental freshwater. Peer Community Journal, Volume 2 (2022), article no. e15. doi : 10.24072/pcjournal.90. https://peercommunityjournal.org/articles/10.24072/pcjournal.90/
Peer reviewed and recommended by PCI : 10.24072/pci.ecotoxenvchem.100001
Conflict of interest of the recommender and peer reviewers:
The recommender in charge of the evaluation of the article and the reviewers declared that they have no conflict of interest (as defined in the code of conduct of PCI) with the authors or with the content of the article.
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