PMID- 32484664
OWN - NLM
STAT- MEDLINE
DCOM- 20201112
LR  - 20201112
IS  - 1520-5851 (Electronic)
IS  - 0013-936X (Linking)
VI  - 54
IP  - 13
DP  - 2020 Jul 7
TI  - Identification of Potential PBT/POP-Like Chemicals by a Deep Learning Approach 
      Based on 2D Structural Features.
PG  - 8221-8231
LID - 10.1021/acs.est.0c01437 [doi]
AB  - Identifying potential persistent organic pollutants (POPs) and persistent, 
      bioaccumulative, and toxic (PBT) substances from industrial chemical inventories 
      are essential for chemical risk assessment, management, and pollution control. 
      Inspired by the connections between chemical structures and their properties, a 
      deep convolutional neural network (DCNN) model was developed to screen potential 
      PBT/POP-like chemicals. For each chemical, a two-dimensional molecular descriptor 
      representation matrix based on 2424 molecular descriptors was used as the model 
      input. The DCNN model was trained via a supervised learning algorithm with 1306 
      PBT/POP-like chemicals and 9990 chemicals currently known as non-POPs/PBTs. The 
      model can achieve an average prediction accuracy of 95.3 +/- 0.6% and an 
      F-measurement of 79.3 +/- 2.5% for PBT/POP-like chemicals (positive samples only) 
      on external data sets. The DCNN model was further evaluated with 52 
      experimentally determined PBT chemicals in the REACH PBT assessment list and 
      correctly recognized 47 chemicals as PBT/non-PBT chemicals. The DCNN model 
      yielded a total of 4011 suspected PBT/POP like chemicals from 58 079 chemicals 
      merged from five published industrial chemical lists. The proportions of 
      PBT/POP-like substances in the chemical inventories were 6.9-7.8%, higher than a 
      previous estimate of 3-5%. Although additional PBT/POP chemicals were identified, 
      no new family of PBT/POP-like chemicals was observed.
FAU - Sun, Xiangfei
AU  - Sun X
AUID- ORCID: 0000-0003-2704-9261
AD  - Guangdong Key Laboratory of Environmental Pollution and Health, School of 
      Environment, Jinan University, Guangzhou 511443, China.
FAU - Zhang, Xianming
AU  - Zhang X
AUID- ORCID: 0000-0002-5301-7899
AD  - Department of Physical and Environmental Sciences, University of Toronto, 1265 
      Military Trail, Toronto, Ontario Canada, M1C 1A4.
AD  - Environment and Climate Change Canada, Aquatic Contaminants Research Division, 
      867 Lakeshore Road, Burlington, Ontario Canada L7S 1A.
FAU - Muir, Derek C G
AU  - Muir DCG
AUID- ORCID: 0000-0001-6631-9776
AD  - Guangdong Key Laboratory of Environmental Pollution and Health, School of 
      Environment, Jinan University, Guangzhou 511443, China.
AD  - Environment and Climate Change Canada, Aquatic Contaminants Research Division, 
      867 Lakeshore Road, Burlington, Ontario Canada L7S 1A.
FAU - Zeng, Eddy Y
AU  - Zeng EY
AUID- ORCID: 0000-0002-0859-7572
AD  - Guangdong Key Laboratory of Environmental Pollution and Health, School of 
      Environment, Jinan University, Guangzhou 511443, China.
AD  - Research Center of Low Carbon Economy for Guangzhou Region, Jinan University, 
      Guangzhou 510632, China.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20200616
PL  - United States
TA  - Environ Sci Technol
JT  - Environmental science & technology
JID - 0213155
RN  - 0 (Environmental Pollutants)
SB  - IM
MH  - *Deep Learning
MH  - Environmental Monitoring
MH  - *Environmental Pollutants/analysis
MH  - Environmental Pollution
MH  - Risk Assessment
EDAT- 2020/06/03 06:00
MHDA- 2020/11/13 06:00
CRDT- 2020/06/03 06:00
PHST- 2020/06/03 06:00 [pubmed]
PHST- 2020/11/13 06:00 [medline]
PHST- 2020/06/03 06:00 [entrez]
AID - 10.1021/acs.est.0c01437 [doi]
PST - ppublish
SO  - Environ Sci Technol. 2020 Jul 7;54(13):8221-8231. doi: 10.1021/acs.est.0c01437. 
      Epub 2020 Jun 16.