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SApredictor: An Expert System for Screening Chemicals Against Structural Alerts
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”, whic...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9326022/ https://www.ncbi.nlm.nih.gov/pubmed/35910729 http://dx.doi.org/10.3389/fchem.2022.916614 |
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author | Hua, Yuqing Cui, Xueyan Liu, Bo Shi, Yinping Guo, Huizhu Zhang, Ruiqiu Li, Xiao |
author_facet | Hua, Yuqing Cui, Xueyan Liu, Bo Shi, Yinping Guo, Huizhu Zhang, Ruiqiu Li, Xiao |
author_sort | Hua, Yuqing |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9326022 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93260222022-07-28 SApredictor: An Expert System for Screening Chemicals Against Structural Alerts Hua, Yuqing Cui, Xueyan Liu, Bo Shi, Yinping Guo, Huizhu Zhang, Ruiqiu Li, Xiao Front Chem Chemistry 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. Frontiers Media S.A. 2022-07-13 /pmc/articles/PMC9326022/ /pubmed/35910729 http://dx.doi.org/10.3389/fchem.2022.916614 Text en Copyright © 2022 Hua, Cui, Liu, Shi, Guo, Zhang and Li. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Chemistry Hua, Yuqing Cui, Xueyan Liu, Bo Shi, Yinping Guo, Huizhu Zhang, Ruiqiu Li, Xiao SApredictor: An Expert System for Screening Chemicals Against Structural Alerts |
title | SApredictor: An Expert System for Screening Chemicals Against Structural Alerts |
title_full | SApredictor: An Expert System for Screening Chemicals Against Structural Alerts |
title_fullStr | SApredictor: An Expert System for Screening Chemicals Against Structural Alerts |
title_full_unstemmed | SApredictor: An Expert System for Screening Chemicals Against Structural Alerts |
title_short | SApredictor: An Expert System for Screening Chemicals Against Structural Alerts |
title_sort | sapredictor: an expert system for screening chemicals against structural alerts |
topic | Chemistry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9326022/ https://www.ncbi.nlm.nih.gov/pubmed/35910729 http://dx.doi.org/10.3389/fchem.2022.916614 |
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