Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Multi-phase Boltzmann weighting: accounting for local inhomogeneity in molecular simulations of water–octanol partition coefficients in the SAMPL6 challenge

  • 18 Accesses


Accurately computing partition coefficients is a pivotal part of drug discovery. Specifically, octanol–water partition coefficients can provide information into hydrophobicity of drug-like molecules, as well as a de facto representation of membrane permeability. However, one challenge facing the computation of partition coefficients is the need to encapsulate various microscopic environments. These include areas of largely bulk solvent (i.e., either water or octanol) or regions where octanol is saturated with water or areas of higher salt concentration. Also, tautomeric effects require consideration. Thus, we present a Boltzmann weighting approach that incorporates transfer free energies across varying microscopic media, as well as varying tautomeric state, to compute partition coefficients in the SAMPL6 challenge.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4


  1. 1.

    See openmmtools pull request #431, which implements this system as a testsystem and runs the comparison as a unit test. The CHARMM reference energies and all input files are provided on https://github.com/Olllom/charmm-vs-openmm-energies.


  1. 1.

    Sangster J (1997) Octanol–water partition coefficients: fundamentals and physical chemistry. Eur J Med Chem 32(11):842. https://doi.org/10.1016/s0223-5234(97)82764-x

  2. 2.

    Bannan CC, Burley KH, Chiu M, Shirts MR, Gilson MK, Mobley DL (2016) Blind prediction of cyclohexane–water distribution coefficients from the SAMPL5 challenge. J Comput Aided Mol Des 30(11):927–944

  3. 3.

    Guthrie JP (2009) A blind challenge for computational solvation free energies: introduction and overview. J Phys Chem B 113(14):4501–4507. https://doi.org/10.1021/jp806724u

  4. 4.

    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. https://doi.org/10.1007/s10822-010-9350-8

  5. 5.

    Muddana HS, Daniel Varnado C, Bielawski CW, Urbach AR, Isaacs L, Geballe MT, Gilson MK (2012) Blind prediction of host–guest binding affinities: a new SAMPL3 challenge. J Comput Aided Mol Des 26(5):475–487. https://doi.org/10.1007/s10822-012-9554-1

  6. 6.

    Muddana HS, Fenley AT, Mobley DL, Gilson MK (2014) The SAMPL4 host–guest blind prediction challenge: an overview. J Comput Aided Mol Des 28(4):305–317. https://doi.org/10.1007/s10822-014-9735-1

  7. 7.

    Yin J, Henriksen NM, Slochower DR, Shirts MR, Chiu MW, Mobley DL, Gilson MK (2016) Overview of the SAMPL5 host–guest challenge: are we doing better? J Comput Aided Mol Des 31(1):1–19. https://doi.org/10.1007/s10822-016-9974-4

  8. 8.

    Rizzi A, Murkli S, McNeill JN, Yao W, Sullivan M, Gilson MK, Chiu MW, Isaacs L, Gibb BC, Mobley DL, Chodera JD (2018) Overview of the SAMPL6 host–guest binding affinity prediction challenge. J Comput Aided Mol Des 32(10):937–963. https://doi.org/10.1007/s10822-018-0170-6

  9. 9.

    Işık M, Levorse D, Mobley DL, Rhodes T, Chodera JD (2019) Octanol–water partition coefficient measurements for the SAMPL6 blind prediction challenge. J Comput Aided Mol Des (in press). https://doi.org/10.1007/s10822-019-00271-3

  10. 10.

    Finkelstein A (1976) Water and nonelectrolyte permeability of lipid bilayer membranes. J Gen Physiol 68(2):127–135. https://doi.org/10.1085/jgp.68.2.127

  11. 11.

    Venable RM, Krämer A, Pastor RW (2019) Molecular dynamics simulations of membrane permeability. Chem Rev 119(9):5954–5997. https://doi.org/10.1021/acs.chemrev.8b00486

  12. 12.

    Li S, Hu PC, Malmstadt N (2011) Imaging molecular transport across lipid bilayers. Biophys J 101(3):700–708. https://doi.org/10.1016/j.bpj.2011.06.044

  13. 13.

    Walter A, Gutknecht J (1986) Permeability of small nonelectrolytes through lipid bilayer membranes. J Membr Biol 90(3):207–217. https://doi.org/10.1007/BF01870127

  14. 14.

    MacCallum JL, Tieleman DP (2002) Structures of neat and hydrated 1-octanol from computer simulations. J Am Chem Soc 124(50):15085–15093. https://doi.org/10.1021/ja027422o

  15. 15.

    Chen B, Siepmann JI (2006) Microscopic structure and solvation in dry and wet octanol. J Phys Chem B 110(8):3555–3563. https://doi.org/10.1021/jp0548164 pMID: 16494411

  16. 16.

    Procacci P (2019) Solvation free energies via alchemical simulations: let’s get honest about sampling, once more. Phys Chem Chem Phys 21(25):13826–13834. https://doi.org/10.1039/c9cp02808k

  17. 17.

    Zhao YH, Abraham MH (2005) Octanol/water partition of ionic species, including 544 cations. J Organic Chem 70(7):2633–2640. https://doi.org/10.1021/jo048078b pMID: 15787554

  18. 18.

    Yue Z, Li C, Voth GA, Swanson JMJ (2019) Dynamic protonation dramatically affects the membrane permeability of drug-like molecules. J Am Chem Soc 141(34):13421–13433. https://doi.org/10.1021/jacs.9b04387 pMID: 31382734

  19. 19.

    Martin YC (2009) Let’s not forget tautomers. J Comput Aided Mol Des 23(10):693–704. https://doi.org/10.1007/s10822-009-9303-2

  20. 20.

    Pickard FC, König G, Tofoleanu F, Lee J, Simmonett AC, Shao Y, Ponder JW, Brooks BR (2016) Blind prediction of distribution in the SAMPL5 challenge with QM based protomer and pK a corrections. J Comput Aided Mol Des 30(11):1087–1100. https://doi.org/10.1007/s10822-016-9955-7

  21. 21.

    Prasad S, Huang J, Zeng Q, Brooks BR (2018) An explicit-solvent hybrid QM and MM approach for predicting pKa of small molecules in SAMPL6 challenge. J Comput Aided Mol Des 32(10):1191–1201. https://doi.org/10.1007/s10822-018-0167-1

  22. 22.

    Vanommeslaeghe K, Hatcher E, Acharya C, Kundu S, Zhong S, Shim J, Darian E, Guvench O, Lopes P, Vorobyov I, Mackerell AD (2009) CHARMM general force field: a force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J Comput Chem 31(4):671–690. https://doi.org/10.1002/jcc.21367

  23. 23.

    Landrum G (2006) RDKit: open-source cheminformatics. https://doi.org/10.2307/3592822. http://www.rdkit.org

  24. 24.

    O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR (2011) Open babel: an open chemical toolbox. J Cheminform 3(1):33. https://doi.org/10.1186/1758-2946-3-33

  25. 25.

    Gilbert AT (2019) iQmol. http://iqmol.org

  26. 26.

    Stewart JJP (2007) Optimization of parameters for semiempirical methods V: modification of NDDO approximations and application to 70 elements. J Mol Model 13(12):1173–1213. https://doi.org/10.1007/s00894-007-0233-4

  27. 27.

    Møller C, Plesset MS (1934) Note on an approximation treatment for many-electron systems. Phys Rev 46(7):618–622. https://doi.org/10.1103/physrev.46.618

  28. 28.

    Francl MM, Pietro WJ, Hehre WJ, Binkley JS, Gordon MS, DeFrees DJ, Pople JA (1982) Self-consistent molecular orbital methods. XXIII. A polarization-type basis set for second-row elements. J Chem Phys 77:3654–3665. https://doi.org/10.1063/1.444267

  29. 29.

    Gordon MS, Binkley JS, Pople JA, Pietro WJ, Hehre WJ (1982) Self-consistent molecular–orbital methods. 22. small split-valence basis sets for second-row elements. J Am Chem Soc 104:2797–2803. https://doi.org/10.1021/ja00374a017

  30. 30.

    Hariharan PC, Pople JA (1973) The influence of polarization functions on molecular orbital hydrogenation energies. Theor Chim Acta 28:213–222. https://doi.org/10.1007/bf00533485

  31. 31.

    Hehre WJ, Ditchfield R, Pople JA (1972) Self-consistent molecular orbital methods. XII. Further extensions of Gaussian-type basis sets for use in molecular orbital studies of organic molecules. J Chem Phys 56:2257–2261. https://doi.org/10.1063/1.1677527

  32. 32.

    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 (2010) Gaussian09 revision D.01. Gaussian Inc., Wallingford, CT

  33. 33.

    Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Scalmani G, Barone V, Petersson GA, Nakatsuji H, Li X, Caricato M, Marenich AV, Bloino J, Janesko BG, Gomperts R, Mennucci B, Hratchian HP, Ortiz JV, Izmaylov AF, Sonnenberg JL, Williams-Young D, Ding F, Lipparini F, Egidi F, Goings J, Peng B, Petrone A, Henderson T, Ranasinghe D, Zakrzewski VG, Gao J, Rega N, Zheng G, Liang W, Hada M, Ehara M, Toyota K, Fukuda R, Hasegawa J, Ishida M, Nakajima T, Honda Y, Kitao O, Nakai H, Vreven T, Throssell K, Montgomery JA Jr, Peralta JE, Ogliaro F, Bearpark MJ, Heyd JJ, Brothers EN, Kudin KN, Staroverov VN, Keith TA, Kobayashi R, Normand J, Raghavachari K, Rendell AP, Burant JC, Iyengar SS, Tomasi J, Cossi M, Millam JM, Klene M, Adamo C, Cammi R, Ochterski JW, Martin RL, Morokuma K, Farkas O, Foresman JB, Fox DJ (2016) Gaussian16 revision B.01. Gaussian Inc., Wallingford, CT

  34. 34.

    Vanommeslaeghe K, Raman EP, MacKerell AD (2012) Automation of the CHARMM general force field (CGenFF) II: assignment of bonded parameters and partial atomic charges. J Chem Inf Model 52(12):3155–3168. https://doi.org/10.1021/ci3003649

  35. 35.

    Mayne CG, Saam J, Schulten K, Tajkhorshid E, Gumbart JC (2013) Rapid parameterization of small molecules using the force field toolkit. J Comput Chem 34(32):2757–2770. https://doi.org/10.1002/jcc.23422

  36. 36.

    Prall M (2001) VMD: a graphical tool for the modern chemists. J Comput Chem 22(1):132–134

  37. 37.

    Soteras Gutiérrez I, Lin FY, Vanommeslaeghe K, Lemkul JA, Armacost KA, Brooks CL, MacKerell AD (2016) Parametrization of halogen bonds in the CHARMM general force field: improved treatment of ligand–protein interactions. Bioorg Med Chem 24(20):4812–4825. https://doi.org/10.1016/j.bmc.2016.06.034

  38. 38.

    Phillips JC, Braun R, Wang W, Gumbart J, Tajkhorshid E, Villa E, Chipot C, Skeel RD, Kalé L, Schulten K (2005) Scalable molecular dynamics with NAMD. J Comput Chem 26(16):1781–1802. https://doi.org/10.1002/jcc.20289

  39. 39.

    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. https://doi.org/10.1063/1.445869

  40. 40.

    Brooks BR, Brooks CL, Mackerell AD, Nilsson L, Petrella RJ, Roux B, Won Y, Archontis G, Bartels C, Boresch S, Caflisch A, Caves L, Cui Q, Dinner AR, Feig M, Fischer S, Gao J, Hodoscek M, Im W, Kuczera K, Lazaridis T, Ma J, Ovchinnikov V, Paci E, Pastor RW, Post CB, Pu JZ, Schaefer M, Tidor B, Venable RM, Woodcock HL, Wu X, Yang W, York DM, Karplus M (2009) CHARMM: the biomolecular simulation program. J Comput Chem 30(10):1545–1614. https://doi.org/10.1002/jcc.21287

  41. 41.

    Nosé S (1984) A molecular dynamics method for simulations in the canonical ensemble. Mol Phys 52(2):255–268. https://doi.org/10.1080/00268978400101201

  42. 42.

    Hoover WG (1985) Canonical dynamics: equilibrium phase-space distributions. Phys Rev A 31(3):1695–1697. https://doi.org/10.1103/PhysRevA.31.1695

  43. 43.

    Lang BE (2012) Solubility of water in octan-1-ol from (275 to 369) K. J Chem Eng Data 57(8):2221–2226. https://doi.org/10.1021/je3001427

  44. 44.

    Eastman P, Swails J, Chodera JD, McGibbon RT, Zhao Y, Beauchamp KA, Wang LP, Simmonett AC, Harrigan MP, Stern CD, Wiewiora RP, Brooks BR, Pande VS (2017) OpenMM 7: rapid development of high performance algorithms for molecular dynamics. PLoS Comput Biol 13(7):e1005659. https://doi.org/10.1371/journal.pcbi.1005659

  45. 45.

    Chow KH, Ferguson DM (1995) Isothermal–isobaric molecular dynamics simulations with Monte Carlo volume sampling. Comput Phys Commun 91(1–3):283–289. https://doi.org/10.1016/0010-4655(95)00059-O

  46. 46.

    Simonson T (1993) Free energy of particle insertion an exact analysis of the origin singularity for simple liquids. Mol Phys 80(2):441–447. https://doi.org/10.1080/00268979300102371

  47. 47.

    Rizzi A, Chodera J, Naden L, Beauchamp K, Grinaway P, Fass J, Rustenburg B, Ross GA, Swenson DW, Simmonett AC, Krämer A (2019) https://doi.org/10.5281/zenodo.2592819

  48. 48.

    Shirts MR, Chodera JD (2008) Statistically optimal analysis of samples from multiple equilibrium states. J Chem Phys 129(12):124105. https://doi.org/10.1063/1.2978177

  49. 49.

    Huang J, Lemkul JA, Eastman PK, Mackerell AD (2018) Molecular dynamics simulations using the Drude polarizable force field on GPUs with OpenMM: implementation, validation, and benchmarks. J Comput Chem 39(21):1682–1689. https://doi.org/10.1002/jcc.25339

  50. 50.

    Steinbach PJ, Brooks BR (1994) New spherical-cutoff methods for long-range forces in macromolecular simulation. J Comput Chem 15(7):667–683. https://doi.org/10.1002/jcc.540150702

  51. 51.

    Zwanzig RW (1954) High-temperature equation of state by a perturbation method. i. nonpolar gases. J Chem Phys 22(8):1420–1426. https://doi.org/10.1063/1.1740409

  52. 52.

    Xie WH, Shiu WY, Mackay D (1997) A review of the effect of salts on the solubility of organic compounds in seawater. Marine Environ Res 44(4):429–444. https://doi.org/10.1016/S0141-1136(97)00017-2

  53. 53.

    Setschenow J (1889) Über die Konstitution der Salzlösungen auf Grund ihres Verhaltens zu Kohlensäure. Zeitschrift für Physikalische Chemie 4(1):117–125

  54. 54.

    Best SA, Merz KM, Reynolds CH (1999) Free energy perturbation study of octanol/water partition coefficients: comparison with continuum GB/SA calculations. J Phys Chem 103:714–726. https://doi.org/10.1021/jp984215v

  55. 55.

    Mobley DL (2012) Let’s get honest about sampling. J Comput Aided Mol Des 26:93–95. https://doi.org/10.1007/s10822-011-9497-y

  56. 56.

    Whitfield TW, Varma S, Harder E, Lamoureux G, Rempe SB, Roux B (2007) Theoretical study of aqueous solvation of K+ comparing ab initio, polarizable, and fixed-charge models. J Chem Theory Comput 3(6):2068–2082. https://doi.org/10.1021/ct700172b

  57. 57.

    Pohorille A, Jarzynski C, Chipot C (2010) Good practices in free-energy calculations. J Phys Chem B 114(32):10235–10253. https://doi.org/10.1021/jp102971x

  58. 58.

    Williams-Noonan BJ, Yuriev E, Chalmers DK (2018) Free energy methods in drug design: prospects of alchemical perturbation. J Med Chem 61:638–649. https://doi.org/10.1021/acs.jmedchem.7b00681

  59. 59.

    Procacci P (2019) Accuracy, precision, and efficiency of nonequilibrium alchemical methods for computing free energies of solvation. I. Bidirectional approaches. J Chem Phys 151(14):144113. https://doi.org/10.1063/1.5120615

  60. 60.

    Kolář MH, Hobza P (2016) Computer Modeling of Halogen Bonds and Other \(\sigma\)-Hole Interactions. Chem Rev 116(9):5155–5187. https://doi.org/10.1021/acs.chemrev.5b00560

  61. 61.

    Ahmed A, Sandler SI (2016) Predictions of the physicochemical properties of amino acid side chain analogs using molecular simulation. Phys Chem Chem Phys 18(9):6559–6568. https://doi.org/10.1039/c5cp05393e

  62. 62.

    Krämer A, Pickard FC, Huang J, Venable RM, Simmonett AC, Reith D, Kirschner KN, Pastor RW, Brooks BR (2019) Interactions of water and alkanes: modifying additive force fields to account for polarization effects. J Chem Theory Comput 15(6):3854–3867. https://doi.org/10.1021/acs.jctc.9b00016

  63. 63.

    Yesselman JD, Price DJ, Knight JL, Brooks CL III (2012) Match: An atom-typing toolset for molecular mechanics force fields. J Comput Chem 33(2):189–202. https://doi.org/10.1002/jcc.21963

  64. 64.

    Gao J, Xia X (1992) A priori evaluation of aqueous polarization effects through Monte Carlo QM–MM simulations. Science 258(5082):631–635. https://doi.org/10.1126/science.1411573

  65. 65.

    Gao J, Luque FJ, Orozco M (1993) Induced dipole moment and atomic charges based on average electrostatic potentials in aqueous solution. J Chem Phys 98(4):2975–2982. https://doi.org/10.1063/1.464126

  66. 66.

    Luzhkov V, Warshel A (1992) Microscopic models for quantum mechanical calculations of chemical processes in solutions: LD/AMPAC and SCAAS/AMPAC calculations of solvation energies. J Comput Chem 13(2):199–213. https://doi.org/10.1002/jcc.540130212

  67. 67.

    Wesolowski T, Warshel A (1994) Ab initio free energy perturbation calculations of solvation free energy using the frozen density functional approach. J Phys Chem 98(20):5183–5187. https://doi.org/10.1021/j100071a003

  68. 68.

    Gao J, Freindorf M (1997) Hybrid ab initio QM/MM simulation of N-methylacetamide in aqueous solution. J Phys Chem A 101(17):3182–3188. https://doi.org/10.1021/jp970041q

  69. 69.

    Zheng YJ, Merz KM (1992) Mechanism of the human carbonic anhydrase II-catalyzed hydration of carbon dioxide. J Am Chem Soc 114(26):10498–10507. https://doi.org/10.1021/ja00052a054

  70. 70.

    Hudson PS, White JK, Kearns FL, Hodoscek M, Boresch S, Lee WH (2015a) Efficiently computing pathway free energies: new approaches based on chain-of-replica and non-Boltzmann Bennett reweighting schemes. Biochim Biophys Acta 1850(5):944–953. https://doi.org/10.1016/j.bbagen.2014.09.016

  71. 71.

    Hudson PS, Woodcock HL, Boresch S (2015b) Use of nonequilibrium work methods to compute free energy differences between molecular mechanical and quantum mechanical representations of molecular systems. J Phys Chem Lett 6(23):4850–4856. https://doi.org/10.1021/acs.jpclett.5b02164

  72. 72.

    Hudson PS, Boresch S, Rogers DM, Woodcock HL (2018a) Accelerating QM/MM free energy computations via intramolecular force matching. J Chem Theory Comput 14(12):6327–6335. https://doi.org/10.1021/acs.jctc.8b00517

  73. 73.

    Hudson PS, Han K, Woodcock HL, Brooks BR (2018b) Force matching as a stepping stone to QM/MM CB[8] host/guest binding free energies: a SAMPL6 cautionary tale. J Comput Aided Mol Des 32(10):983–999. https://doi.org/10.1007/s10822-018-0165-3

  74. 74.

    Vanommeslaeghe K, Yang M, Mackerell AD (2015) Robustness in the fitting of molecular mechanics parameters. J Comput Chem 36(14):1083–1101. https://doi.org/10.1002/jcc.23897

  75. 75.

    Huang J, Simmonett AC, Pickard FC, MacKerell AD, Brooks BR (2017) Mapping the Drude polarizable force field onto a multipole and induced dipole model. J Chem Phys 147(16):161702. https://doi.org/10.1063/1.4984113

  76. 76.

    Ponder JW, Wu C, Ren P, Pande VS, Chodera JD, Schnieders MJ, Haque I, Mobley DL, Lambrecht DS, DiStasio RA, Head-Gordon M, Clark GNI, Johnson ME, Head-Gordon T (2010) Current status of the AMOEBA polarizable force field. J Phys Chem B 114(8):2549–2564. https://doi.org/10.1021/jp910674d pMID: 20136072

  77. 77.

    Lamoureux G, Roux B (2003) Modeling induced polarization with classical drude oscillators: theory and molecular dynamics simulation algorithm. J Chem Phys 119(6):3025–3039. https://doi.org/10.1063/1.1589749

  78. 78.

    Wang LPP, Martinez TJ, Pande VS (2014) Building force fields: an automatic, systematic, and reproducible approach. J Phys Chem Lett 5(11):1885–1891. https://doi.org/10.1021/jz500737mPM-26273869M4-Citavi

  79. 79.

    Mobley DL, Bannan CC, Rizzi A, Bayly CI, Chodera JD, Lim VT, Lim NM, Beauchamp KA, Slochower DR, Shirts MR, Gilson MK, Eastman PK (2018) Escaping atom types in force fields using direct chemical perception. J Chem Theory Comput 14(11):6076–6092. https://doi.org/10.1021/acs.jctc.8b00640 pMID: 30351006

  80. 80.

    Krämer A, Hülsmann M, Köddermann T, Reith D (2014) Automated parameterization of intermolecular pair potentials using global optimization techniques. Comput Phys Commun 185(12):3228–3239. https://doi.org/10.1016/j.cpc.2014.08.022

  81. 81.

    Hülsmann M, Kirschner KN, Krämer A, Heinrich DD, Krämer-Fuhrmann O, Reith D (2016) Optimizing molecular models through force-field parameterization via the efficient combiation of new modular program packages. In: Snurr RQ, Adjiman CS, Kofke DA (eds) Found Mol Model Simulation Select Pap from FOMMS 2015, Molecular Modeling and Simulation. Springer, Singapore, pp 53–77

  82. 82.

    Huang L, Roux B (2013) Automated force field parameterization for nonpolarizable and polarizable atomic models based on ab initio target data. J Chem Theory Comput 9(8):3543–3556. https://doi.org/10.1021/ct4003477

Download references


The authors would like to thank Richard Venable and John Legato, for technical assistance. We extend our gratitude to Mehtap Işık, David Mobley, Andy Simmonett, Samarjeet Prasad, and Richard Pastor for helpful comments on the manuscript and general insights. This work was partially supported by the intramural research program of the National Heart, Lung and Blood Institute (NHLBI) of the National Institutes of Health and employed the high-performance computational capabilities of the LoBoS and Biowulf Linux clusters at the National Institutes of Health. (http://www.lobos.nih.gov and http://biowulf.nih.gov). P.S.H. acknowledges funding support from the Intramural Research Program of the NIH, NHLBI. A.K. and P.S.H. contributed equally to this work.

Author information

Correspondence to Andreas Krämer.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Andreas Krämer and Phillip S. Hudson equally contributed equally to this work as co-first authors.

Electronic supplementary material

Below is the link to the electronic supplementary material. (a) Details on submissions and slab setup. (b) Table of Penalty statistics from CGenFF. (c) Results of the submitted predictions. (d) Illustration of tautomers. (e) Tautomer solvation free energies for all solvent phases and associated probabilities. (f) Plot of water saturation in the slab simulation. (g) PDBs of all tautomers.

Electronic supplementary material 1 (PDF 779 kb)

Electronic supplementary material 2 (TGZ 23 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Krämer, A., Hudson, P.S., Jones, M.R. et al. Multi-phase Boltzmann weighting: accounting for local inhomogeneity in molecular simulations of water–octanol partition coefficients in the SAMPL6 challenge. J Comput Aided Mol Des (2020). https://doi.org/10.1007/s10822-020-00285-2

Download citation


  • Alchemical simulation
  • Solvation free energy
  • Multi-phase
  • Boltzmann weighting
  • SAMPL6