QSAR modeling the toxicity of pesticides against Americamysis bahia
Copyright © 2020 Elsevier Ltd. All rights reserved..
The widespread use of pesticides has received increasing attention in regulatory agencies because their extensive overuse and various adverse effects on all living organisms. Organizations such as EPA and ECHA have published laws that pesticides should be fully evaluated before bring them to market. In the present study, we evaluated the pesticides toxicity using the Quantitative Structural-Activity Relationship (QSAR) method. The models for the single class pesticides (herbicides, insecticides and fungicides) as well as the general class pesticides (the combined dataset plus some microbicides, molluscicides, etc.) were developed using the Genetic Algorithm and Multiple Linear Regression method. The internal and external validation results suggested that all the obtained models were stable and predictive. According to the modeling descriptors, the lipophilic descriptors contributed positively while all the electrotopological state descriptors showed a negative contribution, their presences in every model verified the conspicuous influence of molecular lipophilicity and hydrophilicity on the pesticides toxicity. However, the influence of topological structure descriptors was different and varies with the physiochemical information they encode. Finally, the models presented in this paper would help assess the pesticides toxicity against Americamysis bahia, shorten test time, and reduce the cost of pesticides risk assessment.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:258 |
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| Enthalten in: |
Chemosphere - 258(2020) vom: 15. Nov., Seite 127217 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Yang, Lu [VerfasserIn] |
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| Links: |
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| Themen: |
Americamysis bahia |
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| Anmerkungen: |
Date Completed 24.09.2020 Date Revised 24.09.2020 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.chemosphere.2020.127217 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM311145582 |
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| 500 | |a Date Revised 24.09.2020 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2020 Elsevier Ltd. All rights reserved. | ||
| 520 | |a The widespread use of pesticides has received increasing attention in regulatory agencies because their extensive overuse and various adverse effects on all living organisms. Organizations such as EPA and ECHA have published laws that pesticides should be fully evaluated before bring them to market. In the present study, we evaluated the pesticides toxicity using the Quantitative Structural-Activity Relationship (QSAR) method. The models for the single class pesticides (herbicides, insecticides and fungicides) as well as the general class pesticides (the combined dataset plus some microbicides, molluscicides, etc.) were developed using the Genetic Algorithm and Multiple Linear Regression method. The internal and external validation results suggested that all the obtained models were stable and predictive. According to the modeling descriptors, the lipophilic descriptors contributed positively while all the electrotopological state descriptors showed a negative contribution, their presences in every model verified the conspicuous influence of molecular lipophilicity and hydrophilicity on the pesticides toxicity. However, the influence of topological structure descriptors was different and varies with the physiochemical information they encode. Finally, the models presented in this paper would help assess the pesticides toxicity against Americamysis bahia, shorten test time, and reduce the cost of pesticides risk assessment | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Americamysis bahia | |
| 650 | 4 | |a Pesticides | |
| 650 | 4 | |a QSAR | |
| 650 | 4 | |a Toxicity | |
| 650 | 7 | |a Fungicides, Industrial |2 NLM | |
| 650 | 7 | |a Herbicides |2 NLM | |
| 650 | 7 | |a Insecticides |2 NLM | |
| 650 | 7 | |a Pesticides |2 NLM | |
| 700 | 1 | |a Wang, Yinghuan |e verfasserin |4 aut | |
| 700 | 1 | |a Chang, Jing |e verfasserin |4 aut | |
| 700 | 1 | |a Pan, Yifan |e verfasserin |4 aut | |
| 700 | 1 | |a Wei, Ruojin |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Jianzhong |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Huili |e verfasserin |4 aut | |
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