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Frontiers of Environmental Science & Engineering

ISSN 2095-2201

ISSN 2095-221X(Online)

CN 10-1013/X

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Front. Environ. Sci. Eng.    2021, Vol. 15 Issue (2) : 24    https://doi.org/10.1007/s11783-020-1316-z
RESEARCH ARTICLE
Developing the QSPR model for predicting the storage lipid/water distribution coefficient of organic compounds
Miao Li, Jian Li, Yuchen Lu, Cenyang Han, Xiaoxuan Wei, Guangcai Ma, Haiying Yu()
College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China
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Abstract

• A predictive model for storage lipid/water distribution coefficient was developed.

• The model yields outstanding fitting performance, robustness, and predictive ability.

• Hydrophobic and electrostatic interactions and molecular size dominate log Klip/w.

• The model can be used in a wide application domain to predict log Klip/w values.

The distribution of organic compounds in stored lipids affects their migration, transformation, bioaccumulation, and toxicity in organisms. The storage lipid/water distribution coefficient (log Klip/w) of organic chemicals, which quantitatively determines such distribution, has become a key parameter to assist their ecological security and health risk. Due to the impossibility to measure Klip/w values for a huge amount of chemicals, it is necessary to develop predictive approaches. In this work, a quantitative structure-property relationship (QSPR) model for estimating log Klip/w values of small organic compounds was constructed based on 305 experimental log Klip/w values. Quantum chemical descriptors and n-octanol/water partitioning coefficient were employed to characterize the intermolecular interactions that dominate log Klip/w values. The hydrophobic and electrostatic interactions and molecular size have been found to play important roles in governing the distribution of chemicals between lipids and aqueous phases. The regression (R2 = 0.959) and validation (Q2 = 0.960) results indicate good fitting performance and robustness of the developed model. A comparison with the predictive performance of other commercial software further proves the higher accuracy and stronger predictive ability of the developed Klip/w predictive model. Thus, it can be used to predict the Klip/w values of cycloalkanes, long-chain alkanes, halides (with fluorine, chlorine, and bromine as substituents), esters (without phosphate groups), alcohols (without methoxy groups), and aromatic compounds.
Keywords Storage lipid/water distribution coefficient      log Klip/w      Organic compounds      QSPR      Quantum chemical descriptors     
Corresponding Author(s): Haiying Yu   
Issue Date: 01 September 2020
 Cite this article:   
Miao Li,Jian Li,Yuchen Lu, et al. Developing the QSPR model for predicting the storage lipid/water distribution coefficient of organic compounds[J]. Front. Environ. Sci. Eng., 2021, 15(2): 24.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-020-1316-z
https://academic.hep.com.cn/fese/EN/Y2021/V15/I2/24
Descriptor Description of the descriptor Unit
µ Dipole moment Debye
α The average molecular polarizability a.u.a
EHOMO The energy of the highest occupied molecular orbital eV
ELUMO The energy of the lowest unoccupied molecular orbital eV
σ Softness eV
ŋ Hardness eV
Vs,max The most positive electrostatic potential on the molecular surface eV
Vs,min The most negative electrostatic potential on the molecular surface eV
Vs+ The average value of positive electrostatic potential on the molecular surface eV
Vs− The average value of negative electrostatic potential on the molecular surface eV
π The average dispersion of electrostatic potential on the molecular surface eV
τ Equilibrium constants of the electrostatic potential on the molecular surface
Mw Molecular mass a.u.
V Molecular volume cm3/mol
log Kow n-octanol/water partition coefficient
Tab.1  All molecular structure descriptors for developing the QSPR model
Fig.1  The fitting plot of the experimental and predictive log Klip/w calculated by Model (3).
N R2 Q2 RMSE BIAS MAE MPE MNE
Model (2) 305 0.955 0.955 0.468 -0.035 0.375 1.445 -1.636
Model (3) 302 0.959 0.959 0.443 -0.036 0.363 1.313 -1.082
Training set 211 0.958 0.958 0.445 0.000 0.363 1.344 -1.081
Test set 91 0.962 0.960 0.444 0.097 0.349 1.149 -0.895
Tab.2  The statistical performance of model regression and validation a
Fig.2  The Williams plot of Model (3).
Models N R2 Q2 RMSE BIAS MAE MPE MNE
QSPR Model 302 0.959 0.959 0.443 -0.036 0.363 1.313 -1.082
ABSOLVa 304 0.932 0.923 0.610 0.191 0.500 1.540 -1.650
SPARC v 4.5a 302 0.950 0.940 0.540 -0.017 0.368 3.840 -1.290
COSMOtherma 304 0.963 0.949 0.498 -0.185 0.357 3.660 -1.500
KOWWINa 305 0.931 0.925 0.600 0.092 0.464 1.940 -1.680
Tab.3  Statistical parameters for predicting log Klip/w values of organic compounds by different methods
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