Concomitant prediction of environmental fate and toxicity of chemical compounds.
biodegradation
prediction
web server
Journal
Biology methods & protocols
ISSN: 2396-8923
Titre abrégé: Biol Methods Protoc
Pays: England
ID NLM: 101693064
Informations de publication
Date de publication:
2020
2020
Historique:
received:
03
09
2020
revised:
09
11
2020
accepted:
10
11
2020
entrez:
30
12
2020
pubmed:
31
12
2020
medline:
31
12
2020
Statut:
epublish
Résumé
The environmental fate of many functional molecules that are produced on a large scale as precursors or as additives to specialty goods (plastics, fibers, construction materials, etc.), let alone those synthesized by the pharmaceutical industry, is generally unknown. Assessing their environmental fate is crucial when taking decisions on the manufacturing, handling, usage, and release of these substances, as is the evaluation of their toxicity in humans and other higher organisms. While this data are often hard to come by, the experimental data already available on the biodegradability and toxicity of many unusual compounds (including genuinely xenobiotic molecules) make it possible to develop machine learning systems to predict these features. As such, we have created a predictor of the "risk" associated with the use and release of any chemical. This new system merges computational methods to predict biodegradability with others that assess biological toxicity. The combined platform, named
Identifiants
pubmed: 33376807
doi: 10.1093/biomethods/bpaa025
pii: bpaa025
pmc: PMC7750720
doi:
Types de publication
Journal Article
Langues
eng
Pagination
bpaa025Informations de copyright
© The Author(s) 2020. Published by Oxford University Press.
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