SApredictor: An Expert System for Screening Chemicals Against Structural Alerts.
SApredictor
expert system
structural alerts
toxicity prediction
web-server
Journal
Frontiers in chemistry
ISSN: 2296-2646
Titre abrégé: Front Chem
Pays: Switzerland
ID NLM: 101627988
Informations de publication
Date de publication:
2022
2022
Historique:
received:
09
04
2022
accepted:
20
06
2022
entrez:
1
8
2022
pubmed:
2
8
2022
medline:
2
8
2022
Statut:
epublish
Résumé
The rapid and accurate evaluation of chemical toxicity is of great significance for estimation of chemical safety. In the past decades, a great number of excellent computational models have been developed for chemical toxicity prediction. But most machine learning models tend to be "black box", which bring about poor interpretability. In the present study, we focused on the identification and collection of structural alerts (SAs) responsible for a series of important toxicity endpoints. Then, we carried out effective storage of these structural alerts and developed a web-server named SApredictor (www.sapredictor.cn) for screening chemicals against structural alerts. People can quickly estimate the toxicity of chemicals with SApredictor, and the specific key substructures which cause the chemical toxicity will be intuitively displayed to provide valuable information for the structural optimization by medicinal chemists.
Identifiants
pubmed: 35910729
doi: 10.3389/fchem.2022.916614
pii: 916614
pmc: PMC9326022
doi:
Types de publication
Journal Article
Langues
eng
Pagination
916614Informations de copyright
Copyright © 2022 Hua, Cui, Liu, Shi, Guo, Zhang and Li.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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