Advertisement

Using Machine Learning to Predict Enthalpy of Solvation

  • Brandon J. Jaquis
  • Ailin Li
  • Nolan D. Monnier
  • Robert G. Sisk
  • William E. AcreeJr.
  • Andrew S. I. D. LangEmail author
Article

Abstract

Enthalpy of solvation is an important thermodynamic property for studying molecular interactions. However, measuring enthalpies of solvation is non-trivial and time-consuming. Therefore, models that can predict enthalpy of solvation values are of significant worth to the general community. Here we present such models, based upon the Acree enthalpy of solvation open data dataset, which can be used to predict enthalpy of solvation values directly from structure. We created machine learning models for enthalpies of solvation in ethanol using open Chemistry Development Kit descriptors that have overall test-set R2 values of 0.89–0.90 and test-set root mean squared error values of 6.60–7.10 kJ·mol−1. The accuracy of the models was improved by limiting them to a single dominant cluster. Since our models were developed under Open Notebook Science conditions, they are fully reproducible and our techniques transparent and easily adaptable to other solvents.

Keywords

QSPR Enthalpy of solvation Ethanol Machine learning Deep learning Random forest 

Notes

Acknowledgements

The authors acknowledge the influence of Jean-Claude Bradley and his advocacy for Open Science on this work.

References

  1. 1.
    Varfolomeev, M.A., Rakipov, I.T., Acree Jr., W.E., Brumfield, M., Abraham, M.H.: Examination of hydrogen-bonding interactions between dissolved solutes and alkylbenzene solvents based on Abraham model correlations derived from measured enthalpies of solvation. Thermochim. Acta 594, 68–79 (2014)CrossRefGoogle Scholar
  2. 2.
    Wilson, A., Tian, A., Dabadge, N., Acree Jr., W.E., Varfolomeev, M.A., Rakipov, I.T., Arkhipova, S.M., Abraham, M.H.: Enthalpy of solvation correlations for organic solutes and gases dissolved in dichloromethane and 1,4-dioxane. Struct. Chem. 24(6), 1841–1853 (2013)CrossRefGoogle Scholar
  3. 3.
    Solomonov, B.N., Varfolomeev, M.A., Novikov, V.B., Klimovitskii, A.E., Faizullin, D.A.: The influence of H-bonding on the enthalpies of solvation of proton acceptors in methanol. Russ. J. Phys. Chem. 79(7), 1029–1032 (2005)Google Scholar
  4. 4.
    Mintz, C., Clark, M., Acree Jr., W.E., Abraham, M.H.: Enthalpy of solvation correlations for gaseous solutes dissolved in water and in 1-octanol based on the Abraham model. J. Chem. Inf. Model. 47(1), 115–121 (2007)CrossRefGoogle Scholar
  5. 5.
    Mintz, C., Clark, M., Burton, K., Acree Jr., W.E., Abraham, M.H.: Enthalpy of solvation correlations for gaseous solutes dissolved in toluene and carbon tetrachloride based on the Abraham model. J. Solution Chem. 36(8), 947–966 (2007)CrossRefGoogle Scholar
  6. 6.
    Mintz, C., Burton, K., Acree Jr., W.E., Abraham, M.H.: Enthalpy of solvation correlations for gaseous solutes dissolved in chloroform and 1,2-dichloroethane based on the Abraham model. Fluid Phase Equilib. 258(2), 191–198 (2007)CrossRefGoogle Scholar
  7. 7.
    Mintz, C., Clark, M., Burton, K., Acree Jr., W.E., Abraham, M.H.: Enthalpy of solvation correlations for gaseous solutes dissolved in benzene and in alkane solvents based on the Abraham model. QSAR Comb. Sci. 26(8), 881–888 (2007)CrossRefGoogle Scholar
  8. 8.
    Mintz, C., Burton, K., Ladlie, T., Clark, M., Acree Jr., W.E., Abraham, M.H.: Enthalpy of solvation correlations for gaseous solutes dissolved in dibutyl ether and ethyl acetate. Thermochim. Acta 470(1–2), 67–76 (2008)CrossRefGoogle Scholar
  9. 9.
    Mintz, C., Ladlie, T., Burton, K., Clark, M., Acree Jr., W.E., Abraham, M.H.: Enthalpy of solvation correlations for gaseous solutes dissolved in alcohol solvents based on the Abraham model. QSAR Comb. Sci. 27(5), 627–635 (2008)CrossRefGoogle Scholar
  10. 10.
    Stolov, M.A., Zaitseva, K.V., Varfolomeev, M.A., Acree Jr., W.E., Abraham, M.H.: Enthalpies of solution and enthalpies of solvation of organic solutes in ethylene glycol at 298.15 K: prediction and analysis of intermolecular interaction contributions. Thermochim. Acta 648, 91–99 (2017)CrossRefGoogle Scholar
  11. 11.
    Varfolomeev, M.A., Rakipov, I.T., Khachatrian, A.A., Acree Jr., W.E., Brumfield, M., Abraham, M.H.: Effect of halogen substitution on the enthalpies of solvation and hydrogen bonding of organic solutes in chlorobenzene and 1,2-dichlorobenzene derived using multi-parameter correlations. Thermochim. Acta 617, 8–20 (2015)CrossRefGoogle Scholar
  12. 12.
    Hart, E., Grover, D., Zettl, H., Acree Jr., W.E., Abraham, M.H.: Abraham model enthalpy of solvation correlations for solutes dissolved in dimethyl carbonate and diethyl carbonate. Phys. Chem. Liq. 53(6), 732–747 (2015)CrossRefGoogle Scholar
  13. 13.
    Hart, E., Zettl, H., Grover, D., Acree Jr., W.E., Abraham, M.H.: Abraham model enthalpy of solvation correlations for solutes dissolved in 1-alkanol solvents (C4–C6). Phys. Chem. Liq. 53(5), 638–659 (2015)CrossRefGoogle Scholar
  14. 14.
    Stephens, T.W., De La Rosa, N.E., Saifullah, M., Ye, S., Chou, V., Quay, A.N., Acree Jr., W.E., Abraham, M.H.: Enthalpy of solvation correlations for organic solutes and gases dissolved in 2-propanol, 2-butanol, 2-methyl-1-propanol and ethanol. Thermochim. Acta 523(1–2), 214–220 (2011)CrossRefGoogle Scholar
  15. 15.
    Naef, R., Acree, W.E.: Calculation of five thermodynamic molecular descriptors by means of a general computer algorithm based on the group-additivity method: standard enthalpies of vaporization, sublimation and solvation, and entropy of fusion of ordinary organic molecules and total phase-change entropy of liquid crystals. Molecules 22(7), 1059 (2017)CrossRefGoogle Scholar
  16. 16.
    Dashtbozorgi, Z., Golmohammadi, H., Acree Jr., W.E.: Prediction of gas to water solvation enthalpy of organic compounds using support vector machines. Thermochim. Acta 539, 7–15 (2012)CrossRefGoogle Scholar
  17. 17.
    Golmohammadi, H., Dashtbozorgi, Z., Samani, M.G., Acree Jr., W.E.: QSPR prediction of gas-to-methanol solvation enthalpy of organic compounds using replacement method and support vector machines. Phys. Chem. Liq. 53(1), 46–66 (2015)CrossRefGoogle Scholar
  18. 18.
    Golmohammadi, H., Dashtbozorgi, Z., Acree Jr., W.E.: QSPR models for prediction of gas-to-heptane and gas-to-hexadecane solvation enthalpies of organic compounds from theoretical molecular descriptors. Struct. Chem. 24(6), 1799–1810 (2013)CrossRefGoogle Scholar
  19. 19.
    Acree, W.E. Jr., Lang, A.S.I.D.: Acree enthalpy of solvation dataset. figshare. (2015)  https://doi.org/10.6084/m9.figshare.1572326.v1
  20. 20.
    Bradley, J.-C., Lang, A.S.I.D., Koch, S., Neylon, C.: Collaboration using Open Notebook Science in academia. In: Ekin, S., Hupcey, M.A.Z., Williams, A.J., Bingham, A. (eds.) Collaborative Computational Technologies for Biomedical Research, pp. 423–452. Wiley, New York (2011)CrossRefGoogle Scholar
  21. 21.
    Jaquis, B.J., Lang, A.S.I.D., Li, A., Monnier, N.D.: Enthalpy of solvation in ethanol. wikidot.com (2018) http://oruons.wikidot.com/enthalpy-of-solvation. Accessed 31 May 2018
  22. 22.
    Guha, R.: CDK Descriptor GUI (v 1.4.8). GitHub (2017). https://github.com/rajarshi/cdkdescui. Accessed 31 May 2018
  23. 23.
    Steinbeck, C., Han, Y., Kuhn, S., Horlacher, O., Luttmann, E., Willighagen, E.: The chemistry development kit (CDK): an open-source java library for chemo- and bioinformatics. J. Chem. Inf. Comput. Sci. 43(2), 493–500 (2003)CrossRefGoogle Scholar
  24. 24.
    Willighagen, E., Mayfield, J.W., Alvarsson, J., Berg, A., Carlsson, L., Jeliazkova, N., Kuhn, S., Pluskal, T., Rojas-Chertó, M., Spjuth, O., Torrance, G., Evelo, C.T., Guha, R., Steinbeck, C.: The Chemistry Development Kit (CDK): v2.0 atom typing, depiction, molecular formulas, and substructure searching. J. Cheminform. 9, 33 (2017)CrossRefGoogle Scholar
  25. 25.
    R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2013) http://www.R-project.org/. Accessed 31 May 2018
  26. 26.
    Kuhn, M.: Building predictive models in R using the Caret Package. J. Stat.Soft. 28 (2008)  https://doi.org/10.18637/jss.v028.i05
  27. 27.
    Liaw, A., Wiener, M.: Classification and Regression by randomForest. R news 2/3 (2002)Google Scholar
  28. 28.
    Candel, A., LeDell, E.: Deep Learning with H2O. Bartz, A., editor. H2O.ai Inc. (2018) http://h2o.ai/resources/. Accessed 31 May 2018

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Computing & Mathematics DepartmentOral Roberts UniversityTulsaUSA
  2. 2.Chemistry DepartmentUniversity of North TexasDentonUSA

Personalised recommendations