PMID- 38387592
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20240309
IS  - 1879-1026 (Electronic)
IS  - 0048-9697 (Linking)
VI  - 921
DP  - 2024 Apr 15
TI  - POPs identification using simple low-code machine learning.
PG  - 171143
LID - S0048-9697(24)01282-8 [pii]
LID - 10.1016/j.scitotenv.2024.171143 [doi]
AB  - Effectively identifying persistent organic pollutants (POPs) with extensive 
      organic chemical datasets poses a formidable challenge but is of utmost 
      importance. Leveraging machine learning techniques can enhance this process, but 
      previous models often demanded advanced programming skills and high-end computing 
      resources. In this study, we harnessed the simplicity of PyCaret, a Python-based 
      package, to construct machine-learning models for POP screening based on 2D 
      molecular descriptors. We compared the performance of these models against a deep 
      convolutional neural network (DCNN) model. Utilising minimal Python code, we 
      generated several models that exhibited superior or comparable performance to the 
      DCNN. The most outstanding performer, the Light Gradient Boosting Machine (LGBM), 
      achieved an accuracy of 96.20 %, an AUC of 97.70 %, and an F1 score of 82.58 %. 
      This model outshone the DCNN model. Furthermore, it excelled in identifying POPs 
      within the REACH PBT and compiled industrial chemical lists. Our findings 
      highlight the accessibility and simplicity of PyCaret, requiring only a few lines 
      of code, rendering it suitable for non-computing professionals in environmental 
      sciences. The ability of low code machine learning tools (e.g. PyCaret) to 
      facilitate model comparison and interpretation holds promise, encouraging prompt 
      assessment and management of chemical substances.
CI  - Copyright (c) 2024 Elsevier B.V. All rights reserved.
FAU - Xin, Lei
AU  - Xin L
AD  - School of Environment, MOE Key Laboratory of Theoretical Chemistry of 
      Environment, South China Normal University, Guangzhou 510006, China; 
      Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical 
      Pollution and Environmental Safety, South China Normal University, Guangzhou 
      510006, China.
FAU - Yu, Haiying
AU  - Yu H
AD  - College of Geography and Environmental Sciences, Zhejiang Normal University, 
      Jinhua 321004, China.
FAU - Liu, Sisi
AU  - Liu S
AD  - School of Environment, MOE Key Laboratory of Theoretical Chemistry of 
      Environment, South China Normal University, Guangzhou 510006, China; 
      Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical 
      Pollution and Environmental Safety, South China Normal University, Guangzhou 
      510006, China.
FAU - Ying, Guang-Guo
AU  - Ying GG
AD  - School of Environment, MOE Key Laboratory of Theoretical Chemistry of 
      Environment, South China Normal University, Guangzhou 510006, China; 
      Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical 
      Pollution and Environmental Safety, South China Normal University, Guangzhou 
      510006, China.
FAU - Chen, Chang-Er
AU  - Chen CE
AD  - School of Environment, MOE Key Laboratory of Theoretical Chemistry of 
      Environment, South China Normal University, Guangzhou 510006, China; 
      Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical 
      Pollution and Environmental Safety, South China Normal University, Guangzhou 
      510006, China. Electronic address: changer.chen@m.scnu.edu.cn.
LA  - eng
PT  - Journal Article
DEP - 20240220
PL  - Netherlands
TA  - Sci Total Environ
JT  - The Science of the total environment
JID - 0330500
SB  - IM
OTO - NOTNLM
OT  - Chemical management
OT  - Classification
OT  - Machine learning
OT  - Persistent organic pollutants (POPs)
OT  - PyCaret
OT  - Risk assessment
COIS- Declaration of competing interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2024/02/23 00:42
MHDA- 2024/02/23 00:43
CRDT- 2024/02/22 19:14
PHST- 2023/12/26 00:00 [received]
PHST- 2024/02/19 00:00 [revised]
PHST- 2024/02/19 00:00 [accepted]
PHST- 2024/02/23 00:43 [medline]
PHST- 2024/02/23 00:42 [pubmed]
PHST- 2024/02/22 19:14 [entrez]
AID - S0048-9697(24)01282-8 [pii]
AID - 10.1016/j.scitotenv.2024.171143 [doi]
PST - ppublish
SO  - Sci Total Environ. 2024 Apr 15;921:171143. doi: 10.1016/j.scitotenv.2024.171143. 
      Epub 2024 Feb 20.