PMID- 36475377
OWN - NLM
STAT- MEDLINE
DCOM- 20221221
LR  - 20230105
IS  - 1520-5851 (Electronic)
IS  - 0013-936X (Linking)
VI  - 56
IP  - 24
DP  - 2022 Dec 20
TI  - Machine Learning-Based Models with High Accuracy and Broad Applicability Domains 
      for Screening PMT/vPvM Substances.
PG  - 17880-17889
LID - 10.1021/acs.est.2c06155 [doi]
AB  - 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.
FAU - Zhao, Qiming
AU  - Zhao Q
AD  - Key Laboratory of Environment Remediation and Ecological Health, Ministry of 
      Education, College of Environmental and Resource Sciences, Zhejiang University, 
      Hangzhou310058, China.
FAU - Yu, Yang
AU  - Yu Y
AD  - Solid Waste and Chemicals Management Center, Ministry of Ecology and Environment 
      of the People's Republic of China, Beijing100029, China.
FAU - Gao, Yuchen
AU  - Gao Y
AD  - Key Laboratory of Environment Remediation and Ecological Health, Ministry of 
      Education, College of Environmental and Resource Sciences, Zhejiang University, 
      Hangzhou310058, China.
FAU - Shen, Lilai
AU  - Shen L
AD  - Key Laboratory of Environment Remediation and Ecological Health, Ministry of 
      Education, College of Environmental and Resource Sciences, Zhejiang University, 
      Hangzhou310058, China.
FAU - Cui, Shixuan
AU  - Cui S
AD  - Key Laboratory of Environment Remediation and Ecological Health, Ministry of 
      Education, College of Environmental and Resource Sciences, Zhejiang University, 
      Hangzhou310058, China.
AD  - Women's Reproductive Health Key Laboratory of Zhejiang Province, Women's 
      Hospital, School of Medicine, Zhejiang University, Hangzhou310006, China.
FAU - Gou, Yiyuan
AU  - Gou Y
AD  - Key Laboratory of Environment Remediation and Ecological Health, Ministry of 
      Education, College of Environmental and Resource Sciences, Zhejiang University, 
      Hangzhou310058, China.
FAU - Zhang, Chunlong
AU  - Zhang C
AD  - Department of Environmental Sciences, University of Houston-Clear Lake, 2700 Bay 
      Area Blvd., Houston, Texas77058, United States.
FAU - Zhuang, Shulin
AU  - Zhuang S
AUID- ORCID: 0000-0002-7774-7239
AD  - Key Laboratory of Environment Remediation and Ecological Health, Ministry of 
      Education, College of Environmental and Resource Sciences, Zhejiang University, 
      Hangzhou310058, China.
AD  - Women's Reproductive Health Key Laboratory of Zhejiang Province, Women's 
      Hospital, School of Medicine, Zhejiang University, Hangzhou310006, China.
FAU - Jiang, Guibin
AU  - Jiang G
AUID- ORCID: 0000-0002-6335-3917
AD  - State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research 
      Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 
      Beijing100085, China.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20221206
PL  - United States
TA  - Environ Sci Technol
JT  - Environmental science & technology
JID - 0213155
SB  - IM
MH  - *Algorithms
MH  - *Machine Learning
OTO - NOTNLM
OT  - PMT/vPvM substances
OT  - hazard classification
OT  - high-throughput screening
OT  - machine learning
OT  - risk management
EDAT- 2022/12/08 06:00
MHDA- 2022/12/22 06:00
CRDT- 2022/12/07 03:02
PHST- 2022/12/08 06:00 [pubmed]
PHST- 2022/12/22 06:00 [medline]
PHST- 2022/12/07 03:02 [entrez]
AID - 10.1021/acs.est.2c06155 [doi]
PST - ppublish
SO  - Environ Sci Technol. 2022 Dec 20;56(24):17880-17889. doi: 
      10.1021/acs.est.2c06155. Epub 2022 Dec 6.