Journal of Computer-Aided Molecular Design

, Volume 30, Issue 11, pp 1045–1058 | Cite as

Prediction of cyclohexane-water distribution coefficients for the SAMPL5 data set using molecular dynamics simulations with the OPLS-AA force field

  • Ian M. Kenney
  • Oliver BecksteinEmail author
  • Bogdan I. IorgaEmail author


All-atom molecular dynamics simulations were used to predict water-cyclohexane distribution coefficients \(D_{cw}\) of a range of small molecules as part of the SAMPL5 blind prediction challenge. Molecules were parameterized with the transferable all-atom OPLS-AA force field, which required the derivation of new parameters for sulfamides and heterocycles and validation of cyclohexane parameters as a solvent. The distribution coefficient was calculated from the solvation free energies of the compound in water and cyclohexane. Absolute solvation free energies were computed by an established protocol using windowed alchemical free energy perturbation with thermodynamic integration. This protocol resulted in an overall root mean square error in \(\log D_{cw}\) of almost 4 log units and an overall signed error of −3 compared to experimental data. There was no substantial overall difference in accuracy between simulating in NVT and NPT ensembles. The signed error suggests a systematic error but the experimental \(D_{cw}\) data on their own are insufficient to uncover the source of this error. Preliminary work suggests that the major source of error lies in the hydration free energy calculations.


Molecular dynamics Solvation free energy OPLS-AA force field Ligand parameterization Free energy perturbation Thermodynamic integration Cyclohexane-water distribution coefficients 



OB was supported in part by Grant ACI-1443054 from the National Science Foundation. BII was supported in part by Grants ANR-10-LABX-33 (LabEx LERMIT) and ANR-14-JAMR-0002-03 (JPIAMR) from the French National Research Agency (ANR).

Supplementary material

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  1. 1.
    Leeson PD, Springthorpe B (2007) The influence of drug-like concepts on decision-making in medicinal chemistry. Nat Rev Drug Discov 6(11):881–890. doi: 10.1038/nrd2445 CrossRefGoogle Scholar
  2. 2.
    Bannan CC, Calabro G, Kyu DY, Mobley DL (2016) Calculating partition coefficients of small molecules in octanol/water and cyclohexane/water. J Chem Theory Comput. 12(8):4015–4024. doi: 10.1021/acs.jctc.6b00449 Google Scholar
  3. 3.
    Nicholls A, Mobley DL, Guthrie JP, Chodera JD, Bayly CI, Cooper MD, Pande VS (2008) Predicting small-molecule solvation free energies: an informal blind test for computational chemistry. J Med Chem 51(4):769–779. doi: 10.1021/jm070549+ CrossRefGoogle Scholar
  4. 4.
    Guthrie JP (2009) A blind challenge for computational solvation free energies: introduction and overview. J Phys Chem B 113(14):4501–4507. doi: 10.1021/jp806724u CrossRefGoogle Scholar
  5. 5.
    Geballe MT, Skillman AG, Nicholls A, Guthrie JP, Taylor PJ (2010) The SAMPL2 blind prediction challenge: introduction and overview. J Comput Aided Mol Des 24(4):259–279. doi: 10.1007/s10822-010-9350-8 CrossRefGoogle Scholar
  6. 6.
    Geballe MT, Guthrie JP (2012) The SAMPL3 blind prediction challenge: transfer energy overview. J Comput Aided Mol Des 26(5):489–496. doi: 10.1007/s10822-012-9568-8 CrossRefGoogle Scholar
  7. 7.
    Mobley DL, Wymer KL, Lim NM, Guthrie JP (2014) Blind prediction of solvation free energies from the SAMPL4 challenge. J Comput Aided Mol Des 28(3):135–150. doi: 10.1007/s10822-014-9718-2 CrossRefGoogle Scholar
  8. 8.
    Rustenburg AS, Dancer J, Lin B, Ortwine DF, Mobley DL, Chodera JD (2016) Measuring experimental cyclohexane/water distribution coefficients for the SAMPL5 challenge. J Comput Aided Mol Des (in press)Google Scholar
  9. 9.
    Lin B, Pease JH (2013) A novel method for high throughput lipophilicity determination by microscale shake flask and liquid chromatography tandem mass spectrometry. Comb Chem High Throughput Screen 16(10):817–825. doi: 10.1021/ct200866d CrossRefGoogle Scholar
  10. 10.
    Jorgensen WL, Tirado-Rives J (2005) Potential energy functions for atomic-level simulations of water and organic and biomolecular systems. Proc Natl Acad Sci USA 102(19):6665–6670. doi: 10.1073/pnas.0408037102 CrossRefGoogle Scholar
  11. 11.
    Beckstein O, Iorga BI (2012) Prediction of hydration free energies for aliphatic and aromatic chloro derivatives using molecular dynamics simulations with the OPLS-AA force field. J Comput Aided Mol Des 26(5):635–645. doi: 10.1007/s10822-011-9527-9 CrossRefGoogle Scholar
  12. 12.
    Beckstein O, Fourrier A, Iorga BI (2014) Prediction of hydration free energies for the SAMPL4 diverse set of compounds using molecular dynamics simulations with the OPLS-AA force field. J Comput Aided Mol Des 28(3):265–276. doi: 10.1007/s10822-014-9727-1 CrossRefGoogle Scholar
  13. 13.
    Kaminski G, Duffy E, Matsui T, Jorgensen W (1994) Free energies of hydration and pure liquid properties of hydrocarbons from the OPLS all-atom model. J Phys Chem 98(49):13077–13082. doi: 10.1021/j100100a043 CrossRefGoogle Scholar
  14. 14.
    Jorgensen WL, Maxwell DS, Tirado-Rives J (1996) Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J Am Chem Soc 118(45):11225–11236. doi: 10.1021/ja9621760 CrossRefGoogle Scholar
  15. 15.
    Damm W, Frontera A, Tirado-Rives J, Jorgensen W (1997) OPLS all-atom force field for carbohydrates. J Comput Chem 18(16):1955–1970. doi: 10.1002/(SICI)1096-987X(199712)18:16<1955:AID-JCC1>3.0.CO;2-L CrossRefGoogle Scholar
  16. 16.
    Jorgensen WL, McDonald NA (1998) Development of an all-atom force field for heterocycles. Properties of liquid pyridine and diazenes. J Mol Struct THEOCHEM 424(1–2):145–155. doi: 10.1016/S0166-1280(97)00237-6 CrossRefGoogle Scholar
  17. 17.
    McDonald NA, Jorgensen WL (1998) Development of an all-atom force field for heterocycles. Properties of liquid pyrrole, furan, diazoles, and oxazoles. J Phys Chem B 102(41):8049–8059. doi: 10.1021/jp981200o CrossRefGoogle Scholar
  18. 18.
    Rizzo RC, Jorgensen WL (1999) OPLS all-atom model for amines: resolution of the amine hydration problem. J Am Chem Soc 121(20):4827–4836. doi: 10.1021/ja984106u CrossRefGoogle Scholar
  19. 19.
    Kaminski GA, Friesner RA, Tirado-Rives J, Jorgensen WL (2001) Evaluation and reparametrization of the OPLS-AA force field for proteins via comparison with accurate quantum chemical calculations on peptides. J Phys Chem B 105(28):6474–6487. doi: 10.1021/jp003919d CrossRefGoogle Scholar
  20. 20.
    Hess B, Kutzner C, van der Spoel D, Lindahl E (2008) GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J Chem Theory Comput 4(3):435–447. doi: 10.1021/ct700301q CrossRefGoogle Scholar
  21. 21.
    Pronk S, Páll S, Schulz R, Larsson P, Bjelkmar P, Apostolov R, Shirts MR, Smith JC, Kasson PM, van der Spoel D, Hess B, Lindahl E (2013) GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics 29(7):845–854. doi: 10.1093/bioinformatics/btt055 CrossRefGoogle Scholar
  22. 22.
    Watkins EK, Jorgensen WL (2001) Perfluoroalkanes: conformational analysis and liquid-state properties from ab initio and Monte Carlo calculations. J Phys Chem A 105(16):4118–4125. doi: 10.1021/jp004071w CrossRefGoogle Scholar
  23. 23.
    Price M, Ostrovsky D, Jorgensen W (2001) Gas-phase and liquid-state properties of esters, nitriles, and nitro compounds with the OPLS-AA force field. J Comput Chem 22(13):1340–1352. doi: 10.1002/jcc.1092 CrossRefGoogle Scholar
  24. 24.
    Kony D, Damm W, Stoll S, Van Gunsteren W (2002) An improved OPLS-AA force field for carbohydrates. J Comput Chem 23(15):1416–1429. doi: 10.1002/jcc.10139 CrossRefGoogle Scholar
  25. 25.
    Kahn K, Bruice T (2002) Parameterization of OPLS-AA force field for the conformational analysis of macrocyclic polyketides. J Comput Chem 23(10):977–996. doi: 10.1002/jcc.10051 CrossRefGoogle Scholar
  26. 26.
    Thomas L, Christakis T, Jorgensen W (2006) Conformation of alkanes in the gas phase and pure liquids. J Phys Chem B 110(42):21198–21204. doi: 10.1021/jp064811m CrossRefGoogle Scholar
  27. 27.
    Jorgensen W, Jensen K, Alexandrova A (2007) Polarization effects for hydrogen-bonded complexes of substituted phenols with water and chloride ion. J Chem Theory Comput 3(6):1987–1992. doi: 10.1021/ct7001754 CrossRefGoogle Scholar
  28. 28.
    Xu Z, Luo HH, Tieleman DP (2007) Modifying the OPLS-AA force field to improve hydration free energies for several amino acid side chains using new atomic charges and an off-plane charge model for aromatic residues. J Comput Chem 28(3):689–697. doi: 10.1002/jcc.20560 CrossRefGoogle Scholar
  29. 29.
    Domański J, Beckstein O, Iorga BI (2012) Ligandbook—an online repository for small and drug-like molecule force field parameters. In: 244th Meeting of the American Chemical Society—abstracts of papers. COMP–227.
  30. 30.
    Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79(2):926–935. doi: 10.1063/1.445869 CrossRefGoogle Scholar
  31. 31.
    Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Scalmani G, Barone V, Mennucci B, Petersson GA, Nakatsuji H, Caricato M, Li X, Hratchian HP, Izmaylov AF, Bloino J, Zheng G, Sonnenberg JL, Hada M, Ehara M, Toyota K, Fukuda R, Hasegawa J, Ishida M, Nakajima T, Honda Y, Kitao O, Nakai H, Vreven T, Montgomery JA Jr, Peralta JE, Ogliaro F, Bearpark M, Heyd JJ, Brothers E, Kudin KN, Staroverov VN, Kobayashi R, Normand J, Raghavachari K, Rendell A, Burant JC, Iyengar SS, Tomasi J, Cossi M, Rega N, Millam JM, Klene M, Knox JE, Cross JB, Bakken V, Adamo C, Jaramillo J, Gomperts R, Stratmann RE, Yazyev O, Austin AJ, Cammi R, Pomelli C, Ochterski JW, Martin RL, Morokuma K, Zakrzewski VG, Voth GA, Salvador P, Dannenberg JJ, Dapprich S, Daniels AD, Farkas O, Foresman JB, Ortiz JV, Cioslowski J, Fox DJ (2009) Gaussian 09 Revision D.01. Gaussian Inc, WallingfordGoogle Scholar
  32. 32.
    Vilseck JZ, Tirado-Rives J, Jorgensen WL (2014) Evaluation of CM5 charges for condensed-phase modeling. J Chem Theory Comput 10(7):2802–2812. doi: 10.1021/ct500016d CrossRefGoogle Scholar
  33. 33.
    Dodda LS, Vilseck JZ, Cutrona KJ, Jorgensen WL (2015) Evaluation of CM5 charges for nonaqueous condensed-phase modeling. J Chem Theory Comput 11(9):4273–4282. doi: 10.1021/acs.jctc.5b00414 CrossRefGoogle Scholar
  34. 34.
    Páll S, Abraham MJ, Kutzner C, Hess B, Lindahl E (2015) Tackling exascale software challenges in molecular dynamics simulations with GROMACS. In: Markidis S, Laure E (eds) Solving software challenges for exascale: international conference on exascale applications and software, EASC 2014, Stockholm, Sweden, April 2-3, 2014, Revised selected papers, lecture notes in computer science, vol 8759. Springer International Publishing, Switzerland, pp 3–27. doi: 10.1007/978-3-319-15976-8_1
  35. 35.
    Mobley DL, Dumont E, Chodera JD, Dill KA (2007) Comparison of charge models for fixed-charge force fields: small-molecule hydration free energies in explicit solvent. J Phys Chem B 111(9):2242–2254. doi: 10.1021/jp0667442 CrossRefGoogle Scholar
  36. 36.
    Parrinello M, Rahman A (1981) Polymorphic transitions in single crystals: a new molecular dynamics method. J Appl Phys 52(12):7182–7190. doi: 10.1063/1.328693,
  37. 37.
    Shirts MR, Pitera JW, Swope WC, Pande VS (2003) Extremely precise free energy calculations of amino acid side chain analogs: comparison of common molecular mechanics force fields for proteins. J Chem Phys 119(11):5740–5761. doi: 10.1063/1.1587119 CrossRefGoogle Scholar
  38. 38.
    Essman U, Perela L, Berkowitz ML, Darden T, Lee H, Pedersen LG (1995) A smooth particle mesh Ewald method. J Chem Phys 103:8577–8592. doi: 10.1063/1.470117 CrossRefGoogle Scholar
  39. 39.
    Hess B (2008) P-LINCS: a parallel linear constraint solver for molecular simulation. J Chem Theory Comput 4(1):116–122. doi: 10.1021/ct700200b CrossRefGoogle Scholar
  40. 40.
    Jorge M, Garrido N, Queimada A, Economou I, Macedo E (2010) Effect of the integration method on the accuracy and computational efficiency of free energy calculations using thermodynamic integration. J Chem Theory Comput 6(4):1018–1027. doi: 10.1021/ct900661c CrossRefGoogle Scholar
  41. 41.
    Jones E, Oliphant T, Peterson P, et al (2001) SciPy: open source scientific tools for Python. Accessed 11 June 2016
  42. 42.
    Faber NKM (1999) Estimating the uncertainty in estimates of root mean square error of prediction: application to determining the size of an adequate test set in multivariate calibration. Chemometr Intell Lab Syst 49(1):79–89. doi: 10.1016/S0169-7439(99)00027-1 CrossRefGoogle Scholar
  43. 43.
    O’Neil MJ (ed) (2013) The Merck index—an encyclopedia of chemicals, drugs, and biologicals. Royal Society of Chemistry, CambridgeGoogle Scholar
  44. 44.
    Marenich AV, Kelly CP, Thompson JD, Hawkins GD, Chambers CC, Giesen DJ, Winget P, Cramer CJ, Truhlar DG (2009) Minnesota solvation database—version 2009. University of Minnesota, Minneapolis.
  45. 45.
    Marenich AV, Jerome SV, Cramer CJ, Truhlar DG (2012) Charge Model 5: an extension of Hirshfeld population analysis for the accurate description of molecular interactions in gaseous and condensed phases. J Chem Theory Comput 8(2):527–541. doi: 10.1021/ct200866d CrossRefGoogle Scholar
  46. 46.
    Mobley DL, Bayly CI, Cooper MD, Shirts MR, Dill KA (2009) Small molecule hydration free energies in explicit solvent: an extensive test of fixed-charge atomistic simulations. J Chem Theory Comput 5(2):350–358. doi: 10.1021/ct800409d CrossRefGoogle Scholar
  47. 47.
    Bannan CC, Burley KH, Mobley DL (2016) Blind prediction of cyclohexane-water distribution coefficients from the SAMPL5 challenge. J Comput Aided Mol Des. doi: 10.1007/s10822-016-9954-8
  48. 48.
    Shirts MR, Pande VS (2005) Solvation free energies of amino acid side chain analogs for common molecular mechanics water models. J Chem Phys 122(13):134,508. doi: 10.1063/1.1877132 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of PhysicsArizona State UniversityTempeUSA
  2. 2.Center for Biological PhysicsArizona State UniversityTempeUSA
  3. 3.Institut de Chimie des Substances Naturelles, CNRS UPR 2301Université Paris-Saclay, Labex LERMITGif-sur-YvetteFrance

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