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Research Article

In silico package models for deriving values of solute parameters in linear solvation energy relationships

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Pages 21-37 | Received 25 Sep 2022, Accepted 20 Dec 2022, Published online: 10 Jan 2023

References

  • B.C. Kelly, M.G. Ikonomou, J.D. Blair, A.E. Morin, and F.A.P.C. Gobas, Food web-specific biomagnification of persistent organic pollutants, Science 317 (2007), pp. 236–239. doi:10.1126/science.1138275.
  • P.R. Duchowicz, QSPR studies on water solubility, octanol-water partition coefficient and vapour pressure of pesticides, SAR QSAR Environ. Res. 31 (2020), pp. 135–148. doi:10.1080/1062936X.2019.1699602.
  • H. Modarresi, H. Modarress, and J.C. Dearden, Henry’s law constant of hydrocarbons in air–water system: The cavity ovality effect on the non-electrostatic contribution term of solvation free energy, SAR QSAR Environ. Res. 16 (2005), pp. 461–482. doi:10.1080/10659360500319869.
  • S. Zhang, J. Chen, Q. Zhao, Q. Xie, and X. Wei, Unveiling self-sensitized photodegradation pathways by DFT calculations: A case of sunscreen p-aminobenzoic acid, Chemosphere 163 (2016), pp. 227–233. doi:10.1016/j.chemosphere.2016.08.028.
  • M.D. David, N.J. Fendinger, and V.C. Hand, Determination of Henry’s law constants for organosilicones in actual and simulated wastewater, Environ. Sci. Technol. 34 (2000), pp. 4554–4559. doi:10.1021/es991204m.
  • J.C. Dearden, P. Rotureau, and G. Fayet, QSPR prediction of physico-chemical properties for reach, SAR QSAR Environ. Res. 24 (2013), pp. 279–318. doi:10.1080/1062936X.2013.773372.
  • M.J. Kamlet, R.M. Doherty, J. Abboud, M.H. Abraham, and R.W. Taft, Solubility – A new look, Chemtech 16 (1986), pp. 566–576.
  • M.H. Abraham, A. Ibrahim, and A.M. Zissimos, Determination of sets of solute descriptors from chromatographic measurements, J. Chromatogr. A 1037 (2004), pp. 29–47. doi:10.1016/j.chroma.2003.12.004.
  • M.H. Abraham, R.E. Smith, R. Luchtefeld, A.J. Boorem, R. Luo, and W.E. Acree, Prediction of solubility of drugs and other compounds in organic solvents, J. Pharm. Sci.-US 99 (2010), pp. 1500–1515. doi:10.1002/jps.21922.
  • T.N. Brown, QSPRs for predicting equilibrium partitioning in solvent–air systems from the chemical structures of solutes and solvents, J. Solut. Chem. 45 (2022), pp. 255–268.
  • X. Jin, Z. Fu, X. Li, and J. Chen, Development of polyparameter linear free energy relationship models for octanol-air partition coefficients of diverse chemicals, Environ. Sci. Proc. Imp. 19 (2017), pp. 300–306.
  • M.J. Kamlet, R.M. Doherty, P.W. Carr, D. Mackay, M.H. Abraham, and R.W. Taft, Linear solvation energy relationships. 44. Parameter estimation rules that allow accurate prediction of octanol/water partition coefficients and other solubility and toxicity properties of polychlorinated biphenyls and polycyclic aromatic hydrocarbons, Environ. Sci. Technol. 22 (1988), pp. 503–509. doi:10.1021/es00170a003.
  • Y. Wang, J. Chen, X. Wei, A.J. Hernandez Maldonado, and Z. Chen, Unveiling adsorption mechanisms of organic pollutants onto carbon nanomaterials by density functional theory computations and linear free energy relationship modeling, Environ. Sci. Technol. 51 (2017), pp. 11820–11828. doi:10.1021/acs.est.7b02707.
  • A. Stenzel, K. Goss, and S. Endo, Experimental determination of polyparameter linear free energy relationship (pp-LFER) substance descriptors for pesticides and other contaminants: New measurements and recommendations, Environ. Sci. Technol. 47 (2013), pp. 14204–14214. doi:10.1021/es404150e.
  • J.D. Weckwerth, M.F. Vitha, and P.W. Carr, The development and determination of chemically distinct solute parameters for use in linear solvation energy relationships, Fluid Phase Equilibr. 183 (2001), pp. 143–157. doi:10.1016/S0378-3812(01)00428-9.
  • N. Ulrich, S. Endo, T.N. Brown, N. Watanabe, G. Bronner, M.H. Abraham, and K.U. Goss, UFZ-LSER database, 2017; available at http://www.ufz.de/lserd.
  • ACD/percepta. Advanced Chemistry Development Inc, Toronto, Canada, 2015; software available at https://www.acdlabs.com.
  • J.A. Platts, D. Butina, M.H. Abraham, and A. Hersey, Estimation of molecular linear free energy relation descriptors using a group contribution approach, J. Chem. Inf. Comp. Sci. 39 (1999), pp. 835–845. doi:10.1021/ci980339t.
  • N. Ulrich and A. Ebert, Can deep learning algorithms enhance the prediction of solute descriptors for linear solvation energy relationship approaches?, Fluid Phase Equilibr. 555 (2022), pp. 113349. doi:10.1016/j.fluid.2021.113349.
  • A.M. Zissimos, M.H. Abraham, A. Klamt, F. Eckert, and J. Wood, A comparison between the two general sets of linear free energy descriptors of Abraham and Klamt, J. Chem. Inf. Comput. Sci. 42 (2002), pp. 1320–1331. doi:10.1021/ci025530o.
  • T.N. Brown, Predicting hexadecane–air equilibrium partition coefficients (L) using a group contribution approach constructed from high quality data, SAR QSAR Environ. Res. 25 (2014), pp. 51–71. doi:10.1080/1062936X.2013.841286.
  • M.L. Card, V. Gomez-Alvarez, W.H. Lee, D.G. Lynch, N.S. Orentas, M.T. Lee, E.M. Wong, and R.S. Boethling, History of EPI suite and future perspectives on chemical property estimation in us toxic substances control act new chemical risk assessments, Environ. Sci. Proc. Imp. 19 (2017), pp. 203–212.
  • Y. Liang, R. Xiong, S.I. Sandler, and D.M. Di Toro, Quantum chemically estimated Abraham solute parameters using multiple solvent–water partition coefficients and molecular polarizability, Environ. Sci. Technol. 51 (2017), pp. 9887–9898. doi:10.1021/acs.est.7b01737.
  • Y. Wang, W.H. Tang, Z.J. Xiao, W. Yang, Y. Peng, J.W. Chen, and J. Li, Novel quantitative structure activity relationship models for predicting hexadecane/air partition coefficients of organic compounds, J. Environ. Sci.-China 124 (2023), pp. 98–104. doi:10.1016/j.jes.2021.10.033.
  • M.H. Abraham and J. Le, The correlation and prediction of the solubility of compounds in water using an amended solvation energy relationship, J. Pharm. Sci.-US 88 (1999), pp. 868–880. doi:10.1021/js9901007.
  • K.U. Goss, Predicting the equilibrium partitioning of organic compounds using just one linear solvation energy relationship (LSER), Fluid Phase Equilibr. 233 (2005), pp. 19–22. doi:10.1016/j.fluid.2005.04.006.
  • A.R. Katritzky, Y.L. Wang, S. Sild, T. Tamm, and M. Karelson, QSPR studies on vapor pressure, aqueous solubility, and the prediction of water-air partition coefficients, J Chem. Inf. Comp. Sci. 38 (1998), pp. 720–725. doi:10.1021/ci980022t.
  • D. Fourches, E. Muratov, and A. Tropsha, Trust, but verify: On the importance of chemical structure curation in cheminformatics and QSAR modeling research, J. Chem. Inf. Model. 50 (2010), pp. 1189–1204. doi:10.1021/ci100176x.
  • G.A. Petersson and M.A. Al Laham, A complete basis set model chemistry. Ii. Open‐shell systems and the total energies of the first‐row atoms, J Chem. Phys. 94 (1991), pp. 6081–6090. doi:10.1063/1.460447.
  • W.R. Wadt and P.J. Hay, Ab initio effective core potentials for molecular calculations. Potentials for main group elements Na to Bi, J. Chem. Phys. 82 (1985), pp. 284–298. doi:10.1063/1.448800.
  • M. Frisch, G. Trucks, H.B. Schlegel, G. Scuseria, M. Robb, J. Cheeseman, G. Scalmani, and V. Barone, Gaussian 09; software available at http://www.gaussian.com/g_prod/g09.htm.
  • A.L. Hickey and C.N. Rowley, Benchmarking quantum chemical methods for the calculation of molecular dipole moments and polarizabilities, J. Phys. Chem. A 118 (2014), pp. 3678–3687. doi:10.1021/jp502475e.
  • J.J.P. Stewart, Optimization of parameters for semiempirical methods v: Modification of Nddo approximations and application to 70 elements, J. Mol. Model. 13 (2007), pp. 1173–1213. doi:10.1007/s00894-007-0233-4.
  • T. Lu and F. Chen, Multiwfn: A multifunctional wavefunction analyzer, J. Comput. Chem. 33 (2012), pp. 580–592. doi:10.1002/jcc.22885.
  • T. Srl, Dragon (software for molecular descriptor calculation) ver. 6.0; software available at http://www.talete.mi.it/.
  • P.R. Duchowicz, J.F. Aranda, D.E. Bacelo, and S.E. Fioressi, QSPR study of the Henry’s law constant for heterogeneous compounds, Chem. Eng. Res. Des. 154 (2020), pp. 115–121. doi:10.1016/j.cherd.2019.12.009.
  • A. Rácz, D. Bajusz, and K. Héberger, Intercorrelation limits in molecular descriptor preselection for QSAR/QSPR, Mol. Inform. 38 (2019), pp. 1800154. doi:10.1002/minf.201800154.
  • K. Roy, P. Ambure, S. Kar, and P.K. Ojha, Is it possible to improve the quality of predictions from an “intelligent” use of multiple QSAR/QSPR/QSTR models? J. Chemom. 32 (2018), pp. e2992. doi:10.1002/cem.2992.
  • A. Tropsha, Best practices for QSAR model development, validation, and exploitation, Mol. Inform. 29 (2010), pp. 476–488. doi:10.1002/minf.201000061.
  • OECD, Guidance Document on the Validation of (Quantitative) Structure-Activity Relationship [(Q)SAR] Models, OECD Series on Testing and Assessment, No. 69, OECD Publishing, Paris, 2014.
  • C.Y. Zhao, R.S. Zhang, H.X. Zhang, C.X. Xue, H.X. Liu, M.C. Liu, Z.D. Hu, and B.T. Fan, QSAR study of natural, synthetic and environmental endocrine disrupting compounds for binding to the androgen receptor, SAR QSAR Environ. Res. 16 (2005), pp. 349–367. doi:10.1080/10659360500204368.
  • I. Mitra, A. Saha, and K. Roy, Exploring quantitative structure-activity relationship studies of antioxidant phenolic compounds obtained from traditional Chinese medicinal plants, Mol. Simulat. 36 (2010), pp. 1067–1079. doi:10.1080/08927022.2010.503326.
  • P. Gramatica, E. Giani, and E. Papa, Statistical external validation and consensus modeling: A QSAR case study for KOCprediction, J. Mol. Graph. Model. 25 (2007), pp. 755–766. doi:10.1016/j.jmgm.2006.06.005.
  • R: A language and environment for statistical computing. R Core Team, Vienna, Austria, 2015; software available at https://www.R-project.org/.
  • P.C.M. van Noort, J.J.H. Haftka, and J.R. Parsons, Updated Abraham solvation parameters for polychlorinated biphenyls, Environ. Sci. Technol. 44 (2010), pp. 7037–7042. doi:10.1021/es102210g.
  • G. Caron, V. Digiesi, S. Solaro, and G. Ermondi, Flexibility in early drug discovery: Focus on the beyond-rule-of-5 chemical space, Drug Discov. Today 25 (2020), pp. 621–627. doi:10.1016/j.drudis.2020.01.012.
  • R.P. Feynman, R.B. Leighton, and M. Sands, The Feynman Lectures on Physics, Volume III: Quantum Mechanics, Addison-Wesley, New York, 1965.
  • X. Cao, B.C. Hancock, N. Leyva, J. Becker, W. Yu, and V.M. Masterson, Estimating the refractive index of pharmaceutical solids using predictive methods, Int. J. Pharmaceut. 368 (2009), pp. 16–23. doi:10.1016/j.ijpharm.2008.09.044.
  • M.H. Abraham and W.E. Acree, Descriptors for fluorotelomere alcohols. Calculation of physicochemical properties, Phys. Chem. Liq. 59 (2021), pp. 932–937. doi:10.1080/00319104.2021.1888094.
  • A. Opperhuizen, P. Serne, and J.M. Van der Steen, Thermodynamics of fish/water and octan-1-ol/water partitioning of some chlorinated benzenes, Environ. Sci. Technol. 22 (1988), pp. 286–292. doi:10.1021/es00168a008.
  • J. Kuleshova, P.R. Birkin, and J.M. Elliott, Contribution of the double layer to transient faradaic processes: Implications for hydrodynamic modulated voltammetry of nanostructures, J. Phys. Chem. C 114 (2010), pp. 13442–13450. doi:10.1021/jp102308p.
  • A.V. Marenich, C.J. Cramer, and D.G. Truhlar, Universal solvation model based on solute electron density and on a continuum model of the solvent defined by the bulk dielectric constant and atomic surface tensions, J. Phys. Chem. B 113 (2009), pp. 6378–6396. doi:10.1021/jp810292n.
  • E. Wild, A. Cabrerizo, J. Dachs, and K.C. Jones, Clustering of nonpolar organic compounds in lipid media: Evidence and implications, J. Phys. Chem. A 112 (2008), pp. 11699–11703. doi:10.1021/jp804751f.
  • X. Guo, X. Jin, X. Lv, Y. Pu, and F. Bai, Real-time visualization of perylene nanoclusters in water and their partitioning to graphene surface and macrophage cells, Environ. Sci. Technol. 49 (2015), pp. 7926–7933. doi:10.1021/acs.est.5b01880.
  • J.M. Parnis and D. Mackay, Multimedia Environmental Models: The Fugacity Approach, 3rd ed., CRC press, Boca Raton, 2020.
  • W.R. Li, G.H. Ding, H. Gao, Y. Zhuang, X. Gu, and W.J.G.M. Peijnenburg, Prediction of octanol-air partition coefficients for PCBs at different ambient temperatures based on the solvation free energy and the dimer ratio, Chemosphere 242 (2020), pp. 125246. doi:10.1016/j.chemosphere.2019.125246.
  • J. Chen, T. Harner, G. Ding, Q. Xie, K. Schramm, and A. Kettrup, Universal predictive models on octanol-air partition coefficients at different temperatures for persistent organic pollutants, Environ. Toxicol. Chem. 23 (2010), pp. 2309–2317. doi:10.1897/03-341.
  • M.H. Abraham, P.L. Grellier, D.V. Prior, P.P. Duce, J.J. Morris, and P.J. Taylor, Hydrogen bonding. Part 7. A scale of solute hydrogen-bond acidity based on log k values for complexation in tetrachloromethane, J. Chem. Soc. Perkin Tran. 6 (1989), pp. 699–711. doi:10.1039/p29890000699.
  • H.C. Tuelp, K. Goss, R.P. Schwarzenbach, and K. Fenner, Experimental determination of LSER parameters for a set of 76 diverse pesticides and pharmaceuticals, Environ. Sci. Technol. 42 (2008), pp. 2034–2040. doi:10.1021/es702473f.
  • M.H. Abraham, C.M. Du, and J.A. Platts, Lipophilicity of the nitrophenols, J. Org. Chem. 65 (2000), pp. 7114–7118. doi:10.1021/jo000840w.
  • J. Jover, R. Bosque, and J. Sales, Neural network based QSAR study for predicting pKa of phenols in different solvents, QSAR Comb. Sci. 26 (2007), pp. 385–397. doi:10.1002/qsar.200610088.
  • M.H. Abraham, Hydrogen-bonding. 27. Solvation parameters for functionally-substituted aromatic-compounds and heterocyclic-compounds, from gas-liquid-chromatographicdata, J. Chromatogr. 644 (1993), pp. 95–139. doi:10.1016/0021-9673(93)80123-P.
  • M.H. Abraham, L. Honcharova, S.A. Rocco, J.W.E. Acree, and K.M. De Fina, The lipophilicity and hydrogen bond strength of pyridine-n-oxides and protonated pyridine-n-oxides, New J. Chem. 35 (2011), pp. 930–936. doi:10.1039/c0nj00893a.
  • M.H. Abraham and W.E. Acree, Descriptors for the prediction of partition coefficients and solubilities of organophosphorus compounds, Sep. Sci. Technol. 48 (2013), pp. 884–897. doi:10.1080/01496395.2012.721043.
  • A. Alin, Multicollinearity, Wiley Interdiscip. Rev. Comput. Stat. 2 (2010), pp. 370–374. doi:10.4330/wjc.v2.i11.370.
  • J.W. Johnson, A heuristic method for estimating the relative weight of predictor variables in multiple regression, Multivar. Behav. Res. 35 (2000), pp. 1–19. doi:10.1207/S15327906MBR3501_1.
  • K. Zhang and H.C. Zhang, Predicting solute descriptors for organic chemicals by a deep neural network (DNN) using basic chemical structures and a surrogate metric, Environ. Sci. Technol. 36 (2022), pp. 171–185.
  • A.H. Vo, T.R. Van Vleet, R.R. Gupta, M.J. Liguori, and M.S. Rao, An overview of machine learning and big data for drug toxicity evaluation, Chem. Res. Toxicol. 33 (2020), pp. 20–37. doi:10.1021/acs.chemrestox.9b00227.

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