Ecotoxicological QSTR and QSTTR Modeling for the Prediction of Acute Oral Toxicity of Pesticides against Multiple Avian Species

杀虫剂 急性毒性 环境化学 毒性 生物 生态学 化学 有机化学
作者
Rajendra Kumar Mukherjee,Vinay Kumar,Kunal Roy
出处
期刊:Environmental Science & Technology [American Chemical Society]
卷期号:56 (1): 335-348 被引量:38
标识
DOI:10.1021/acs.est.1c05732
摘要

The ever-increasing use of pesticides in response to the rising agricultural demand has threatened the existence of nontarget organisms like avian species, disrupting the global ecological integrity. Therefore, it is critical to protect and restore different endangered bird species from the perspective of ecosystem safety. In the present work, we have developed regression-based two-dimensional quantitative structure toxicity relationship (2D QSTR) and quantitative structure toxicity–toxicity relationship (QSTTR) models to estimate the toxicity of pesticides on five different avian species following the Organization for Economic Co-operation and Development (OECD) guidelines. Rigorous validation has been performed using different statistical internal and external validation parameters to ensure the robustness and interpretability of the developed models. From the developed models, it can be stated that the presence of electronegative and lipophilic features greatly enhance pesticide toxicity, whereas the hydrophilic characters are shown to have a detrimental impact on the toxicity of pesticides. Moreover, the developed QSTTR models have been employed to the in silico toxicity prediction of 124, 154, and 250 pesticides against bobwhite quail, ring-necked pheasant, and mallard duck species, respectively, extracted from the Office of Pesticides Program (OPP) Pesticide Ecotoxicity Database. The information obtained from the modeled descriptors might be used for pesticide risk assessment in the future, with the added benefit of providing an early caution of their possible negative impact on birds for regulatory purposes.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
司南应助科研通管家采纳,获得10
刚刚
Ava应助科研通管家采纳,获得10
刚刚
CodeCraft应助科研通管家采纳,获得10
刚刚
慕青应助科研通管家采纳,获得30
1秒前
1秒前
李健应助科研通管家采纳,获得10
1秒前
共享精神应助科研通管家采纳,获得10
1秒前
司南应助科研通管家采纳,获得10
1秒前
852应助科研通管家采纳,获得10
1秒前
小马甲应助科研通管家采纳,获得10
1秒前
gsx应助科研通管家采纳,获得10
1秒前
嗯哼应助科研通管家采纳,获得10
1秒前
乐乐应助科研通管家采纳,获得10
1秒前
司南应助科研通管家采纳,获得10
1秒前
SciGPT应助科研通管家采纳,获得10
2秒前
xjcy应助科研通管家采纳,获得10
2秒前
2秒前
2秒前
司南应助科研通管家采纳,获得10
2秒前
2秒前
3秒前
3秒前
姽婳wy发布了新的文献求助10
4秒前
QI发布了新的文献求助10
6秒前
xiaoshi完成签到,获得积分10
6秒前
科研通AI2S应助莫小烦采纳,获得10
7秒前
XZY发布了新的文献求助10
10秒前
12秒前
13秒前
传奇3应助姽婳wy采纳,获得10
13秒前
宁诺发布了新的文献求助10
13秒前
夏天的西瓜完成签到,获得积分10
14秒前
狮子座关注了科研通微信公众号
15秒前
16秒前
yyyy发布了新的文献求助10
18秒前
Dr大壮发布了新的文献求助10
18秒前
Akim应助liuzelin采纳,获得30
20秒前
20秒前
21秒前
Lucas应助super chan采纳,获得10
22秒前
高分求助中
Rock-Forming Minerals, Volume 3C, Sheet Silicates: Clay Minerals 2000
The late Devonian Standard Conodont Zonation 2000
Nickel superalloy market size, share, growth, trends, and forecast 2023-2030 2000
The Lali Section: An Excellent Reference Section for Upper - Devonian in South China 1500
The Healthy Socialist Life in Maoist China 600
The Vladimirov Diaries [by Peter Vladimirov] 600
A new species of Coccus (Homoptera: Coccoidea) from Malawi 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3267870
求助须知:如何正确求助?哪些是违规求助? 2907285
关于积分的说明 8341469
捐赠科研通 2577939
什么是DOI,文献DOI怎么找? 1401436
科研通“疑难数据库(出版商)”最低求助积分说明 655037
邀请新用户注册赠送积分活动 634076