Critical features identification for chemical chronic toxicity based on mechanistic forecast models
Wang, Xiaoqing1,3; Li, Fei1,4,5; Chen, Jingwen2; Teng, Yuefa1,3; Ji, Chenglong1,4; Wu, Huifeng1,4
发表期刊ENVIRONMENTAL POLLUTION
ISSN0269-7491
2022-08-15
卷号307页码:8
关键词Computational toxicology Machine learning Structural alerts Risk assessment Prioritization rank
DOI10.1016/j.envpol.2022.119584
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.
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/133337/31214
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
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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.
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