Critical features identification for chemical chronic toxicity based on mechanistic forecast models
文献类型：期刊论文
| 作者 | Wang, Xiaoqing3,5 ; Li, Fei1,2,5; Chen, Jingwen4; Teng, Yuefa3,5; Ji, Chenglong2,5; Wu, Huifeng2,5
|
| 刊名 | ENVIRONMENTAL POLLUTION
 |
| 出版日期 | 2022-08-15 |
| 卷号 | 307页码:8 |
| ISSN号 | 0269-7491 |
| 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(fli@yic.ac.cn) |
| 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. |
| WOS关键词 | IN-SILICO PREDICTION ; FRESH-WATER ROTIFER ; AQUATIC TOXICITY ; QSAR ; MARINE |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:000811640700002 |
| 资助机构 | National Natural Science Foun-dation of China ; Yantai Science and Technology Development Plan ; Youth Innovation Promo-tion Association CAS |
| 源URL | [http://ir.yic.ac.cn/handle/133337/31213]  |
| 专题 | 烟台海岸带研究所_中科院海岸带环境过程与生态修复重点实验室 烟台海岸带研究所_近岸生态与环境实验室 |
| 通讯作者 | Li, Fei |
| 作者单位 | 1.Yantai Inst Coastal Zone Res YIC China, Yantai, Peoples R China 2.Chinese Acad Sci, Ctr Ocean Mega Sci, Qingdao 266071, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Dalian Univ Technol, Sch Environm Sci & Technol, Key Lab Ind Ecol & Environm Engn, MOE, Linggong Rd 2, Dalian 116024, Peoples R China 5.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 |
| 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. |
入库方式： OAI收割
来源：烟台海岸带研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。