Critical features identification for chemical chronic toxicity based on mechanistic forecast models

doi:10.1016/j.envpol.2022.119584

	Critical features identification for chemical chronic toxicity based on mechanistic forecast models
	Wang, Xiaoqing1,3 ; Li, Fei1,4,5 ; Chen, Jingwen 2; Teng, Yuefa 1,3; Ji, Chenglong1,4 ; Wu, Huifeng1,4
发表期刊	ENVIRONMENTAL POLLUTION
ISSN	0269-7491
	2022-08-15
卷号	307 页码:8
关键词	Computational toxicology Machine learning Structural alerts Risk assessment Prioritization rank
DOI	10.1016/j.envpol.2022.119584
通讯作者	Li, Fei([email protected])
英文摘要	Facing billions of tons of pollutants entering the ocean each year, aquatic toxicity is becoming a crucial endpoint for evaluating chemical adverse effects on ecosystems. Notably, huge amount of toxic chemicals at environmental relevant doses can cause potential adverse effects. However, chronic aquatic toxicity effects of chemicals are much scarcer, especially at population level. Rotifers are highly sensitive to toxicants even at chronic lowdoses and their communities are usually considered as effective indicators for assessing the status of aquatic ecosystems. Therefore, the no observed effect concentration (NOEC) for population abundance of rotifers were selected as endpoints to develop machine learning models for the prediction of chemical aquatic chronic toxicity. In this study, forty-eight binary models were built by eight types of chemical descriptors combined with six machine learning algorithms. The best binary model was 1D & 2D molecular descriptors - random trees model (RT) with high balanced accuracy (BA) (0.83 for training and 0.83 for validation set), and Matthews correlation coefficient (MCC) (0.72 for training set and 0.67 for validation set). Moreover, the optimal model identified the primary factors (SpMAD_Dzp, AMW, MATS2v) and filtered out three high alerting substructures [c1cc(Cl)cc1, CNCO, CCOP(=S)(OCC)O] influencing the chronic aquatic toxicity. These results showed that the compounds with low molecular volume, high polarity and molecular weight could contribute to adverse effects on rotifers, facilitating the deeper understanding of chronic toxicity mechanisms. In addition, forecast models had better performances than the common models embedded into ECOSAR software. This study provided insights into structural features responsible for the toxicity of different groups of chemicals and thereby allowed for the rational design of green and safer alternatives.
资助机构	National Natural Science Foun-dation of China ; Yantai Science and Technology Development Plan ; Youth Innovation Promo-tion Association CAS
收录类别	SCI
语种	英语
关键词[WOS]	IN-SILICO PREDICTION ; FRESH-WATER ROTIFER ; AQUATIC TOXICITY ; QSAR ; MARINE
研究领域[WOS]	Environmental Sciences & Ecology
WOS记录号	WOS:000811640700002
引用统计
文献类型	期刊论文
条目标识符	http://ir.yic.ac.cn/handle/133337/37562
专题	中国科学院海岸带环境过程与生态修复重点实验室中国科学院海岸带环境过程与生态修复重点实验室_海岸带环境过程实验室
通讯作者	Li, Fei
作者单位	1.Chinese Acad Sci, Yantai Inst Coastal Zone Res YIC, CAS Key Lab Coastal Environm Proc & Ecol Remediat, Shandong Key Lab Coastal Environm Proc,YICCAS, Yantai 264003, Peoples R China 2.Dalian Univ Technol, Sch Environm Sci & Technol, Key Lab Ind Ecol & Environm Engn, MOE, Linggong Rd 2, Dalian 116024, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Ctr Ocean Mega Sci, Qingdao 266071, Peoples R China 5.Yantai Inst Coastal Zone Res YIC China, Yantai, Peoples R China
推荐引用方式 GB/T 7714	Wang, Xiaoqing,Li, Fei,Chen, Jingwen,et al. Critical features identification for chemical chronic toxicity based on mechanistic forecast models[J]. ENVIRONMENTAL POLLUTION,2022,307:8.
APA	Wang, Xiaoqing,Li, Fei,Chen, Jingwen,Teng, Yuefa,Ji, Chenglong,&Wu, Huifeng.(2022).Critical features identification for chemical chronic toxicity based on mechanistic forecast models.ENVIRONMENTAL POLLUTION,307,8.
MLA	Wang, Xiaoqing,et al."Critical features identification for chemical chronic toxicity based on mechanistic forecast models".ENVIRONMENTAL POLLUTION 307(2022):8.