亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Unveiling first report on in silico modeling of aquatic toxicity of organic chemicals to Labeo rohita (Rohu) employing QSAR and q-RASAR

数量结构-活动关系 野鲮属 水生毒理学 毒性 生物信息学 急性毒性 环境毒理学 毒理 生物 化学 渔业 生物信息学 生物化学 有机化学 基因
作者
A Gallagher,Supratik Kar
出处
期刊:Chemosphere [Elsevier]
卷期号:349: 140810-140810
标识
DOI:10.1016/j.chemosphere.2023.140810
摘要

Labeo rohita, a fish species within the Carp family, holds significant dietary and aquacultural importance in South Asian countries. However, the habitats of L. rohita often face exposure to various harmful pesticides and organic compounds originating from industrial and agricultural runoff. It is challenging to individually investigate the effects of each potentially harmful compound. In such cases, in silico techniques like Quantitative Structure-Activity Relationship (QSAR) and quantitative Read-Across Structure-Activity Relationship (q-RASAR) can be employed to construct algorithmic models capable of simultaneously assessing the toxicity of numerous compounds. We utilized the US EPA's ToxValDB database to curate data regarding acute median lethal concentration (LC50) toxicity for L. rohita. The experimental variables included study type (mortality), study duration (ranging from 0.25 hours to 4 hours), exposure route (static, flowthrough, and renewal), exposure method (drinking water), and types of chemicals (industrial chemicals and pharmaceuticals). Using this dataset, we developed regression-based QSAR and q-RASAR models to predict chemical toxicity to L. rohita based on chemical descriptors. The key descriptors for predicting the toxicity of L. rohita in the regression-based QSAR model include F05[S–Cl], SpMax_EA(ri), s4_relPathLength_2, and SpDiam_AEA(ed). These descriptors can be employed to estimate the toxicity of untested compounds and aid in the development of compounds with lower toxicity based on the presence or absence of these descriptors. Both the QSAR and q-RASAR models serve as valuable tools for understanding the chemicals' structural features responsible for toxicity and for filling gaps in aquatic toxicity data by predicting the toxicity of newly untested compounds in relation to L. rohita. Finally, the developed best model was employed to predict 297 external chemicals, the most toxic substances to L. rohita were identified as cyhalothrin, isobornyl thiocyanatoacetate, and paclobutrzol, while the least toxic ones included ethyl acetate, ethylthiourea, and n-butyric acid.

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
情怀应助鳄鱼采纳,获得10
刚刚
ggg完成签到 ,获得积分10
2秒前
脑洞疼应助1073980795采纳,获得10
10秒前
小蘑菇应助爱笑梦易采纳,获得10
28秒前
haoliu完成签到,获得积分10
28秒前
29秒前
大个应助smm采纳,获得10
31秒前
35秒前
1073980795发布了新的文献求助10
41秒前
54秒前
1分钟前
1分钟前
smm发布了新的文献求助10
1分钟前
1分钟前
再也不拖完成签到,获得积分10
1分钟前
再也不拖发布了新的文献求助10
1分钟前
1分钟前
晨曦发布了新的文献求助10
1分钟前
朴素的山蝶完成签到 ,获得积分0
1分钟前
科研通AI6.3应助晨曦采纳,获得10
1分钟前
1分钟前
Orange应助WangY1263采纳,获得20
1分钟前
1分钟前
li发布了新的文献求助30
2分钟前
2分钟前
小二郎应助独特的师采纳,获得30
2分钟前
爱笑梦易发布了新的文献求助10
2分钟前
li完成签到,获得积分20
2分钟前
2分钟前
在水一方应助浪里白条采纳,获得10
2分钟前
英俊的铭应助科研通管家采纳,获得10
2分钟前
量子星尘发布了新的文献求助10
2分钟前
风止何安发布了新的文献求助10
2分钟前
2分钟前
2分钟前
2分钟前
浪里白条发布了新的文献求助10
2分钟前
2分钟前
morena发布了新的文献求助10
2分钟前
科研通AI6.1应助风止何安采纳,获得10
2分钟前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Handbook of pharmaceutical excipients, Ninth edition 5000
Aerospace Standards Index - 2026 ASIN2026 3000
Signals, Systems, and Signal Processing 610
Discrete-Time Signals and Systems 610
Principles of town planning : translating concepts to applications 500
Wearable Exoskeleton Systems, 2nd Edition 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 纳米技术 有机化学 物理 生物化学 化学工程 计算机科学 复合材料 内科学 催化作用 光电子学 物理化学 电极 冶金 遗传学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 6058490
求助须知:如何正确求助?哪些是违规求助? 7891115
关于积分的说明 16296855
捐赠科研通 5203303
什么是DOI,文献DOI怎么找? 2783887
邀请新用户注册赠送积分活动 1766516
关于科研通互助平台的介绍 1647099