PMID- 35852446
OWN - NLM
STAT- MEDLINE
DCOM- 20221214
LR  - 20230214
IS  - 1747-0285 (Electronic)
IS  - 1747-0277 (Linking)
VI  - 101
IP  - 1
DP  - 2023 Jan
TI  - In silico prediction of boiling point, octanol-water partition coefficient, and 
      retention time index of polycyclic aromatic hydrocarbons through machine 
      learning.
PG  - 52-68
LID - 10.1111/cbdd.14121 [doi]
AB  - Polycyclic aromatic hydrocarbons (PAHs), a special class of persistent organic 
      pollutants (POPs) with two or more aromatic rings, have received extensive 
      attention owing to their carcinogenic, mutagenic, and teratogenic effects. 
      Quantitative structure-property relationship (QSPR) is powerful chemometric 
      method to correlate structural descriptors of PAHs with their physicochemical 
      properties. In this manuscript, a QSPR study of PAHs was performed to predict 
      their boiling point (bp), octanol-water partition coefficient (LogK(ow) ), and 
      retention time index (RI). In addition to traditional molecular descriptors, 
      structural fingerprints play an important role in the correlation of the above 
      properties. Three regression methods, partial least squares (PLS), multiple 
      linear regression (MLR), and genetic function approximation (GFA), were used to 
      establish QSPR models for each property of PAHs. The correlation coefficient 
      (R(2) (test) ) and root mean square error (RMSE) of best model were 0.980 and 
      24.39% (PLS), 0.979 and 35.80% (GFA), 0.926 and 22.90% (MLR) for bp, LogK(ow,) 
      and RI, respectively. The model proposed here can be used to estimate 
      physicochemical properties and inform toxicity prediction of environmental 
      chemicals.
CI  - (c) 2022 John Wiley & Sons Ltd.
FAU - Sun, Linkang
AU  - Sun L
AD  - Institute of Bioinformatics and Medical Engineering, School of Electrical and 
      Information Engineering, Jiangsu University of Technology, Changzhou, China.
FAU - Zhang, Min
AU  - Zhang M
AD  - School of Computer Engineering, Jiangsu University of Technology, Changzhou, 
      China.
FAU - Xie, Liangxu
AU  - Xie L
AD  - Institute of Bioinformatics and Medical Engineering, School of Electrical and 
      Information Engineering, Jiangsu University of Technology, Changzhou, China.
FAU - Gao, Qian
AU  - Gao Q
AD  - School of Computer Engineering, Jiangsu University of Technology, Changzhou, 
      China.
FAU - Xu, Xiaojun
AU  - Xu X
AD  - Institute of Bioinformatics and Medical Engineering, School of Electrical and 
      Information Engineering, Jiangsu University of Technology, Changzhou, China.
FAU - Xu, Lei
AU  - Xu L
AUID- ORCID: 0000-0002-4095-6539
AD  - Institute of Bioinformatics and Medical Engineering, School of Electrical and 
      Information Engineering, Jiangsu University of Technology, Changzhou, China.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20220728
PL  - England
TA  - Chem Biol Drug Des
JT  - Chemical biology & drug design
JID - 101262549
RN  - 0 (Polycyclic Aromatic Hydrocarbons)
RN  - 059QF0KO0R (Water)
RN  - 0 (Octanols)
SB  - IM
MH  - *Polycyclic Aromatic Hydrocarbons/chemistry
MH  - Water/chemistry
MH  - Quantitative Structure-Activity Relationship
MH  - Transition Temperature
MH  - Octanols
MH  - Machine Learning
OTO - NOTNLM
OT  - model evaluation and prediction
OT  - polycyclic aromatic hydrocarbons
OT  - quantitative structure-activity relationship
OT  - three regression methods
EDAT- 2022/07/20 06:00
MHDA- 2022/12/15 06:00
CRDT- 2022/07/19 10:01
PHST- 2022/07/14 00:00 [revised]
PHST- 2022/06/23 00:00 [received]
PHST- 2022/07/17 00:00 [accepted]
PHST- 2022/07/20 06:00 [pubmed]
PHST- 2022/12/15 06:00 [medline]
PHST- 2022/07/19 10:01 [entrez]
AID - 10.1111/cbdd.14121 [doi]
PST - ppublish
SO  - Chem Biol Drug Des. 2023 Jan;101(1):52-68. doi: 10.1111/cbdd.14121. Epub 2022 Jul 
      28.