LogP prediction performance with the SMD solvation model and the M06 density functional family for SAMPL6 blind prediction challenge molecules

  • Davy Guan
  • Raymond Lui
  • Slade MatthewsEmail author


This work presents a quantum mechanical model for predicting octanol-water partition coefficients of small protein-kinase inhibitor fragments as part of the SAMPL6 LogP Prediction Challenge. The model calculates solvation free energy differences using the M06-2X functional with SMD implicit solvation and the def2-SVP basis set. This model was identified as dqxk4 in the SAMPL6 Challenge and was the third highest performing model in the physical methods category with 0.49 log Root Mean Squared Error (RMSE) for predicting the 11 compounds in SAMPL6 blind prediction set. We also collaboratively investigated the use of empirical models to address model deficiencies for halogenated compounds at minimal additional computational cost. A mixed model consisting of the dqxk4 physical and hdpuj empirical models found improved performance at 0.34 log RMSE on the SAMPL6 dataset. This collaborative mixed model approach shows how empirical models can be leveraged to expediently improve performance in chemical spaces that are difficult for ab initio methods to simulate.


SAMPL6 LogP Computational chemistry Implicit solvation DFT 



We acknowledge the National Institutes of Health for supporting the experimental work carried out in the SAMPL6 logP Prediction Challenge.


  1. 1.
    Vlahovic F et al (2017) Density functional theory calculation of lipophilicity for organophosphate type pesticides. J Serb Chem Soc 82:104–104CrossRefGoogle Scholar
  2. 2.
    Michalík M, Lukeš V (2016) The validation of quantum chemical lipophilicity prediction of alcohols. Acta Chim Slov 9(2):89CrossRefGoogle Scholar
  3. 3.
    Zhang J et al (2017) Comparison of implicit and explicit solvent models for the calculation of solvation free energy in organic solvents. J Chem Theory Comput 13(3):1034–1043CrossRefGoogle Scholar
  4. 4.
    Bayat Z, Movaffagh J (2010) The 1-octanol/water partition coefficient of nucleoside analogs via free energy estimated in quantum chemical calculations. Russ J Phys Chem A 84(13):2293–2299CrossRefGoogle Scholar
  5. 5.
    Marenich AV, Cramer CJ, Truhlar DG (2009) 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(18):6378–6396CrossRefGoogle Scholar
  6. 6.
    Neese F (2012) The ORCA program system. Wiley Interdiscip Rev: Computat Mol Sci 2(1):73–78Google Scholar
  7. 7.
    Neese F (2018) Software update: the ORCA program system, version 4.0. Wiley Interdiscip Rev: Comput Mol Sci 8(1):e1327Google Scholar
  8. 8.
    Zhao Y, Truhlar DG (2008) The M06 suite of density functionals for main group thermochemistry, thermochemical kinetics, noncovalent interactions, excited states, and transition elements: two new functionals and systematic testing of four M06-class functionals and 12 other functionals. Theor Chem Acc 120(1):215–241CrossRefGoogle Scholar
  9. 9.
    Nedyalkova MA et al (2019) Calculating the partition coefficients of organic solvents in octanol/water and octanol/air. J Chem Inf Model 59(5):2257–2263CrossRefGoogle Scholar
  10. 10.
    Jones MR, Brooks BR, Wilson AK (2016) Partition coefficients for the SAMPL5 challenge using transfer free energies. J Comput Aided Mol Des 30(11):1129–1138CrossRefGoogle Scholar
  11. 11.
    Zhao Y, Truhlar DG (2008) The M06 suite of density functionals for main group thermochemistry, thermochemical kinetics, noncovalent interactions, excited states, and transition elements: two new functionals and systematic testing of four M06-class functionals and 12 other functionals. Theor Chem Acc 120(1–3):215–241CrossRefGoogle Scholar
  12. 12.
    Zhao Y, Truhlar DG (2006) A new local density functional for main-group thermochemistry, transition metal bonding, thermochemical kinetics, and noncovalent interactions. J Chem Phys 125(19):194101CrossRefGoogle Scholar
  13. 13.
    Işık M et al (2019) Octanol-water partition coefficient measurements for the SAMPL6 blind prediction challenge. bioRxiv. CrossRefGoogle Scholar
  14. 14.
    OECD (2004) Test No. 117: Partition coefficient (n-octanol/water), HPLC method, OECD guidelines for the testing of chemicals, Section 1, OECD Publishing, Paris.
  15. 15.
    Berthold MR et al (2008) KNIME: the Konstanz information miner. Springer, BerlinGoogle Scholar
  16. 16.
    O'Boyle NM et al (2011) Open babel: an open chemical toolbox. J Cheminform 3(1):33CrossRefGoogle Scholar
  17. 17.
    RDKit: Open-source cheminformatics.
  18. 18.
    Halgren TA (1996) Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. J Comput Chem 17(5-6):490–519CrossRefGoogle Scholar
  19. 19.
    Weigend F, Ahlrichs R (2005) Balanced basis sets of split valence, triple zeta valence and quadruple zeta valence quality for H to Rn: design and assessment of accuracy. Phys Chem Chem Phys 7(18):3297–3305CrossRefGoogle Scholar
  20. 20.
    Grimme S et al (2010) A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu. J Chem Phys 132(15):154104CrossRefGoogle Scholar
  21. 21.
    Weigend F, Kattannek M, Ahlrichs R (2009) Approximated electron repulsion integrals: Cholesky decomposition versus resolution of the identity methods. J Chem Phys 130(16):164106CrossRefGoogle Scholar
  22. 22.
    Hohenstein EG, Chill ST, Sherrill CD (2008) Assessment of the performance of the M05-2X and M06-2X exchange-correlation functionals for noncovalent interactions in biomolecules. J Chem Theory Comput 4(12):1996–2000CrossRefGoogle Scholar
  23. 23.
    Goerigk L, Grimme S (2011) A thorough benchmark of density functional methods for general main group thermochemistry, kinetics, and noncovalent interactions. Phys Chem Chem Phys 13(14):6670–6688CrossRefGoogle Scholar
  24. 24.
  25. 25.
    Kossmann S, Neese F (2009) Comparison of two efficient approximate Hartee-Fock approaches. Chem Phys Lett 481:240–243CrossRefGoogle Scholar
  26. 26.
    Kozuch S, Martin JML (2013) Halogen bonds: benchmarks and theoretical analysis. J Chem Theory Comput 9(4):1918CrossRefGoogle Scholar
  27. 27.
    Basdogan Y, Keith JA (2018) A paramedic treatment for modeling explicitly solvated chemical reaction mechanisms. Chem Sci 9(24):5341–5346CrossRefGoogle Scholar
  28. 28.
    Viswanadhan VN et al (1989) Atomic physicochemical parameters for three dimensional structure directed quantitative structure-activity relationships. 4. Additional parameters for hydrophobic and dispersive interactions and their application for an automated superposition of certain naturally occurring nucleoside antibiotics. J Chem Inf Comput Sci 29(3):163–172CrossRefGoogle Scholar
  29. 29.
    Li W et al (2019) Efficient corrections for DFT noncovalent interactions based on ensemble learning models. J Chem Inf Model 59(5):1849–1857CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Pharmacoinformatics Laboratory, Discipline of Pharmacology, School of Medical Sciences, Faculty of Medicine and HealthThe University of SydneySydneyAustralia

Personalised recommendations