Machine Learning-Based Models with High Accuracy and Broad Applicability Domains for Screening PMT/vPvM Substances
文献类型：期刊论文
| 作者 | Zhao, Qiming; Yu, Yang; Gao, Yuchen; Shen, Lilai; Cui, Shixuan; Gou, Yiyuan; Zhang, Chunlong; Zhuang, Shulin; Jiang, Guibin
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| 刊名 | ENVIRONMENTAL SCIENCE & TECHNOLOGY
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| 出版日期 | 2022 |
| 卷号 | 56期号:24页码:1-10 |
| 关键词 | PMT/vPvM substances high-throughput screening machine learning hazard classification risk management |
| ISSN号 | 0013-936X |
| 英文摘要 | Persistent, mobile, and toxic (PMT) substances and very persistent and very mobile (vPvM) substances can transport over long distances from various sources, increasing the public health risk. A rapid and high-throughput screening of PMT/vPvM substances is thus warranted to the risk prevention and mitigation measures. Herein, we construct a machine learning-based screening system integrated with five models for high-throughput classification of PMT/vPvM substances. The models are constructed with 44 971 substances by conventional learning, deep learning, and ensemble learning algorithms, among which, LightGBM and XGBoost outperform other algorithms with metrics exceeding 0.900. Good model interpretability is achieved through the number of free halogen atoms (fr_halogen) and the logarithm of partition coefficient (MolLogP) as the two most critical molecular descriptors representing the persistence and mobility of substances, respectively. Our screening system exhibits a great generalization capability with area under the receiver operating characteristic curve (AUROC) above 0.951 and is successfully applied to the persistent organic pollutants (POPs), prioritized PMT/vPvM substances, and pesticides. The screening system constructed in this study can serve as an efficient and reliable tool for high-throughput risk assessment and the prioritization of managing emerging contaminants. |
| 源URL | [https://ir.rcees.ac.cn/handle/311016/48501]  |
| 专题 | 生态环境研究中心_环境化学与生态毒理学国家重点实验室 |
| 作者单位 | 1.University of Houston Clear Lake 2.Research Center for Eco-Environmental Sciences (RCEES) 3.Chinese Academy of Sciences 4.Zhejiang University 5.University of Houston System 6.University of Houston |
| 推荐引用方式 GB/T 7714 | Zhao, Qiming,Yu, Yang,Gao, Yuchen,et al. Machine Learning-Based Models with High Accuracy and Broad Applicability Domains for Screening PMT/vPvM Substances[J]. ENVIRONMENTAL SCIENCE & TECHNOLOGY,2022,56(24):1-10. |
| APA | Zhao, Qiming.,Yu, Yang.,Gao, Yuchen.,Shen, Lilai.,Cui, Shixuan.,...&Jiang, Guibin.(2022).Machine Learning-Based Models with High Accuracy and Broad Applicability Domains for Screening PMT/vPvM Substances.ENVIRONMENTAL SCIENCE & TECHNOLOGY,56(24),1-10. |
| MLA | Zhao, Qiming,et al."Machine Learning-Based Models with High Accuracy and Broad Applicability Domains for Screening PMT/vPvM Substances".ENVIRONMENTAL SCIENCE & TECHNOLOGY 56.24(2022):1-10. |
入库方式： OAI收割
来源：生态环境研究中心
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