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
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
kyle完成签到,获得积分10
刚刚
感性的凉面完成签到,获得积分20
刚刚
刚刚
请叫我风吹麦浪应助末岛采纳,获得10
1秒前
Aprial发布了新的文献求助30
1秒前
dd发布了新的文献求助10
1秒前
传奇3应助科研小菜鸟采纳,获得10
1秒前
在水一方应助惠惠采纳,获得10
2秒前
3秒前
冷艳贵公子王少完成签到 ,获得积分10
3秒前
KatzeBaliey完成签到,获得积分10
3秒前
3秒前
3秒前
4秒前
zz发布了新的文献求助10
4秒前
4秒前
Twikky发布了新的文献求助10
5秒前
5秒前
小马甲应助芒果采纳,获得10
6秒前
6秒前
心想事成完成签到,获得积分10
8秒前
隐形曼青应助噔噔噔噔采纳,获得10
8秒前
wei发布了新的文献求助10
8秒前
Nature完成签到,获得积分10
8秒前
樱桃苏打水完成签到,获得积分10
9秒前
zhui发布了新的文献求助10
9秒前
金色热浪发布了新的文献求助10
9秒前
pinging应助讲你ing采纳,获得10
11秒前
小九完成签到 ,获得积分10
12秒前
华仔应助科研通管家采纳,获得10
13秒前
英俊的铭应助科研通管家采纳,获得10
13秒前
SciGPT应助科研通管家采纳,获得10
13秒前
ivy应助科研通管家采纳,获得10
14秒前
pluto应助科研通管家采纳,获得10
14秒前
喵酱完成签到,获得积分10
14秒前
14秒前
搜集达人应助科研通管家采纳,获得10
14秒前
科研通AI5应助科研通管家采纳,获得30
14秒前
敬老院N号应助科研通管家采纳,获得30
14秒前
Hello应助科研通管家采纳,获得10
14秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Social media impact on athlete mental health: #RealityCheck 1020
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527884
求助须知:如何正确求助?哪些是违规求助? 3108006
关于积分的说明 9287444
捐赠科研通 2805757
什么是DOI,文献DOI怎么找? 1540033
邀请新用户注册赠送积分活动 716904
科研通“疑难数据库(出版商)”最低求助积分说明 709794