[go: up one dir, main page]

Skip to main content
Log in

Predicting partition coefficients of drug-like molecules in the SAMPL6 challenge with Drude polarizable force fields

  • Published:
Journal of Computer-Aided Molecular Design Aims and scope Submit manuscript

Abstract

The water-octanol partition coefficient is an important physicochemical property for small molecule drug design. Here, we report our participation in the SAMPL6 logP prediction challenge with free energy perturbation (FEP) calculations in the water phase and in the 1-octanol phase using Drude polarizable force fields. Root mean square error (RMSE) and mean absolute error (MAE) of our prediction are equal to 1.85 and 1.25 logP units. The errors are not evenly distributed. Out of eleven SAMPL6 solutes, FEP/Drude performed very badly on three molecules (deviations all larger than 2 logP units) but good on the remaining eight (deviations all less than 1 logP unit). We find while FEP converges well within one nanosecond in water, simulations in 1-octanol need much longer simulation time and possibly more independent runs for sampling. We also find out that 1-octanol, albeit being a non-polar solvent, still polarizes solute molecules and forms stable hydrogen bonds with them. At the end, we attempt to reweight FEP trajectories with QM/Drude calculations and discuss possible caveats in our simulation setup.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Paul SM, Mytelka DS, Dunwiddie CT, Persinger CC, Munos BH, Lindborg SR, Schacht AL (2010) How to improve r&d productivity: the pharmaceutical industry’s grand challenge. Nat Rev Drug Discov 9(3):203

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Rustenburg AS, Dancer J, Lin B, Feng JA, Ortwine DF, Mobley DL, Chodera JD (2016) Measuring experimental cyclohexane-water distribution coefficients for the sampl5 challenge. J Comput Aided Mol Des 30(11):945–958

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. 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 pKa corrections. J Comput Aided Mol Des 30(11):1087–1100

    Article  CAS  PubMed  Google Scholar 

  5. König G, Pickard FC, Huang J, Simmonett AC, Tofoleanu F, Lee J, Dral PO, Prasad S, Jones M, Shao Y et al (2016) Calculating distribution coefficients based on multi-scale free energy simulations: an evaluation of mm and qm/mm explicit solvent simulations of water-cyclohexane transfer in the sampl5 challenge. J Comput Aided Mol Des 30(11):989–1006

    Article  PubMed  CAS  Google Scholar 

  6. Işık M, Levorse D, Mobley DL, Rhodes T, Chodera JD (2020) Octanol-water partition coefficient measurements for the sampl6 blind prediction challenge. J Comput Aided Mol Des. https://doi.org/10.1007/s10822-019-00271-3

    Article  PubMed  PubMed Central  Google Scholar 

  7. Kundi V, Ho J (2019) Predicting octanol-water partition coefficients: are quantum mechanical implicit solvent models better than empirical fragment-based methods? J Phys Chem B 123(31):6810–6822

    Article  CAS  PubMed  Google Scholar 

  8. Huang J, Lopes P, Roux B, MacKerell A (2014) Recent advances in polarizable force fields for macromolecules: microsecond simulations of proteins using the classical drude oscillator model. J Phys Chem Lett 5:3144–3150

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Lemkul J, Huang J, Roux B, MacKerell A (2016) An empirical polarizable force field based on the classical drude oscillator model: development history and recent applications. Chem Rev 116:4983–5013

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Huang J, MacKerell AD (2018) Force field development and simulations of intrinsically disordered proteins. Curr Opin Struct Biol 48:40–48

    Article  CAS  PubMed  Google Scholar 

  11. 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 Theor Comput 15(6):3854–3867

    Article  CAS  Google Scholar 

  12. Manzoni F, Söderhjelm P (2014) Prediction of hydration free energies for the sampl4 data set with the amoeba polarizable force field. J Comput Aided Mol Des 28(3):235–244

    Article  CAS  PubMed  Google Scholar 

  13. Bradshaw RT, Essex JW (2016) Evaluating parametrization protocols for hydration free energy calculations with the amoeba polarizable force field. J Chem Theor Comput 12(8):3871–3883

    Article  CAS  Google Scholar 

  14. Bell DR, Qi R, Jing Z, Xiang JY, Mejias C, Schnieders MJ, Ponder JW, Ren P (2016) Calculating binding free energies of host-guest systems using the amoeba polarizable force field. Phys Chem Chem Phys 18(44):30261–30269

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Laury ML, Wang Z, Gordon AS, Ponder JW (2018) Absolute binding free energies for the sampl6 cucurbit [8] uril host-guest challenge via the amoeba polarizable force field. J Comput Aided Mol Des 32(10):1087–1095

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Kamath G, Kurnikov I, Fain B, Leontyev I, Illarionov A, Butin O, Olevanov M, Pereyaslavets L (2016) Prediction of cyclohexane-water distribution coefficient for SAMPL5 drug-like compounds with the QMPFF3 and arrow polarizable force fields. J Comput Aided Mol Des 30(11):977–988

    Article  CAS  PubMed  Google Scholar 

  17. Lamoureux G, Roux B (2003) Modeling induced polarization with classical drude oscillators: theory and molecular dynamics simulation algorithm. J Chem Phys 119:3025–3039

    Article  CAS  Google Scholar 

  18. Lamoureux G, MacKerell AD, Roux B (2003) A simple polarizable model of water based on classical Drude oscillators. J Chem Phys 119:5185–5197

    Article  CAS  Google Scholar 

  19. Lopes P, Huang J, Shim J, Luo Y, Li H, Roux B, MacKerell A (2013) Polarizable force field for peptides and proteins based on the classical drude oscillator. J Chem Theor Comput 9:5430–5449

    Article  CAS  Google Scholar 

  20. Lemkul JA, MacKerell AD Jr (2017) Polarizable force field for dna based on the classical drude oscillator: I. Refinement using quantum mechanical base stacking and conformational energetics. J Chem Theor Comput 13(5):2053–2071

    Article  CAS  Google Scholar 

  21. Lemkul JA, MacKerell AD Jr (2017) Polarizable force field for dna based on the classical drude oscillator: II. Microsecond molecular dynamics simulations of duplex DNA. J Chem Theor Comput 13(5):2072–2085

    Article  CAS  Google Scholar 

  22. Lemkul JA, MacKerell AD Jr (2018) Polarizable force field for RNA based on the classical Drude oscillator. J Comput Chem 39(32):2624–2646

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Chowdhary J, Harder E, Lopes PE, Huang L, MacKerell AD Jr, Roux B (2013) A polarizable force field of dipalmitoylphosphatidylcholine based on the classical Drude model for molecular dynamics simulations of lipids. J Phys Chem B 117(31):9142–9160

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Li H, Chowdhary J, Huang L, He X, MacKerell AD Jr, Roux B (2017) Drude polarizable force field for molecular dynamics simulations of saturated and unsaturated Zwitterionic lipids. J Chem Theor Comput 13(9):4535–4552

    Article  CAS  Google Scholar 

  25. Patel DS, He X, MacKerell AD Jr (2014) Polarizable empirical force field for hexopyranose monosaccharides based on the classical drude oscillator. J Phys Chem B 119(3):637–652

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Small MC, Aytenfisu AH, Lin FY, He X, MacKerell AD (2017) Drude polarizable force field for aliphatic ketones and aldehydes, and their associated acyclic carbohydrates. J Comput Aided Mol Des 31(4):349–363

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Yu H, Whitfield TW, Harder E, Lamoureux G, Vorobyov I, Anisimov VM, MacKerell AD Jr, Roux B (2010) Simulating monovalent and divalent ions in aqueous solution using a drude polarizable force field. J Chem Theor Comput 6(3):774–786

    Article  CAS  Google Scholar 

  28. Lemkul JA, MacKerell AD Jr (2016) Balancing the interactions of mg2+ in aqueous solution and with nucleic acid moieties for a polarizable force field based on the classical Drude oscillator model. J Phys Chem B 120(44):11436–11448

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Lin FY, Lopes PE, Harder E, Roux B, MacKerell AD Jr (2018) Polarizable force field for molecular ions based on the classical Drude oscillator. J Chem Inf Model 58(5):993–1004

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Jiang W, Hardy DJ, Phillips JC, MacKerell AD Jr, Schulten K, Roux B (2010) High-performance scalable molecular dynamics simulations of a polarizable force field based on classical drude oscillators in NAMD. J Phys Chem Lett 2(2):87–92

    Article  CAS  Google Scholar 

  31. Dequidt A, Devemy J, Padua AA (2015) Thermalized drude oscillators with the LAMMPS molecular dynamics simulator. J Chem Inf Model 56(1):260–268

    Article  PubMed  CAS  Google Scholar 

  32. Lemkul JA, Roux B, van der Spoel D, MacKerell AD Jr (2015) Implementation of extended Lagrangian dynamics in gromacs for polarizable simulations using the classical Drude oscillator model. J Comp Chem 36(19):1473–1479

    Article  CAS  Google Scholar 

  33. Huang J, Lemkul JA, Eastman PK, MacKerell AD Jr (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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Nishikawa N, Han K, Wu X, Tofoleanu F, Brooks BR (2018) Comparison of the umbrella sampling and the double decoupling method in binding free energy predictions for sampl6 octa-acid host-guest challenges. J Comput Aided Mol Des 32(10):1075–1086

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Jones MR, Brooks BR, Wilson AK (2016) Partition coefficients for the SAMPL5 challenge using transfer free energies. J Comput Aided Mol Des 30(11):1129–1138

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. 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) Gaussian\(^{}\sim\)16 Revision C.01. Gaussian Inc., Wallingford

    Google Scholar 

  37. Dunning TH Jr (1989) Gaussian basis sets for use in correlated molecular calculations. I. The atoms boron through neon and hydrogen. J Chem Phys 90(2):1007–1023

    Article  CAS  Google Scholar 

  38. Woon DE, Dunning TH Jr (1993) Gaussian basis sets for use in correlated molecular calculations. III. The atoms aluminum through argon. J Chem Phys 98(2):1358–1371

    Article  CAS  Google Scholar 

  39. Woon DE, Dunning TH Jr (1995) Gaussian basis sets for use in correlated molecular calculations. V. Core-valence basis sets for boron through neon. J Chem Phys 103(11):4572–4585

    Article  CAS  Google Scholar 

  40. Woon DE, Dunning TH Jr (1994) Gaussian basis sets for use in correlated molecular calculations. IV. Calculation of static electrical response properties. J Chem Phys 100(4):2975–2988

    Article  CAS  Google Scholar 

  41. Brooks BR, Bruccoleri RE, Olafson BD, States DJ, Swaminathan S, Karplus M (1983) CHARMM: a program for macromolecular energy, minimization, and dynamics calculations. J Comp Chem 4:187–217

    Article  CAS  Google Scholar 

  42. Brooks BR, Brooks CL III, Mackerell AD Jr, Nilsson L, Petrella RJ, Roux B, Won Y, Archontis G, Bartels C, Boresch S et al (2009) CHARMM: the biomolecular simulation program. J Comp Chem 30(10):1545–1614

    Article  CAS  Google Scholar 

  43. Girard M, Ehlen A, Shakya A, Bereau T, de la Cruz MO (2019) Hoobas: a highly object-oriented builder for molecular dynamics. Comput Mater Sci 167:25–33

    Article  CAS  Google Scholar 

  44. Lamoureux G, Harder E, Vorobyov IV, Roux B, MacKerell AD (2006) A polarizable model of water for molecular dynamics simulations of biomolecules. Chem Phys Lett 418(1):245–249

    Article  CAS  Google Scholar 

  45. Anisimov VM, Vorobyov IV, Roux B, MacKerell AD (2007) Polarizable empirical force field for the primary and secondary alcohol series based on the classical Drude model. J Chem Theor Comput 3(6):1927–1946

    Article  CAS  Google Scholar 

  46. Vorobyov IV, Anisimov VM, MacKerell AD (2005) Polarizable empirical force field for alkanes based on the classical Drude oscillator model. J Phys Chem B 109(40):18988–18999

    Article  CAS  PubMed  Google Scholar 

  47. Darden T, York D, Pedersen L (1993) Particle mesh Ewald: an Nlog (N) method for Ewald sums in large systems. J Chem Phys 98(12):10089–10092

    Article  CAS  Google Scholar 

  48. Van Gunsteren W, Berendsen H (1977) Algorithms for macromolecular dynamics and constraint dynamics. Mol Phys 34(5):1311–1327

    Article  Google Scholar 

  49. Lee J, Cheng X, Swails JM, Yeom MS, Eastman PK, Lemkul JA, Wei S, Buckner J, Jeong JC, Qi Y et al (2015) CHARMM-GUI input generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM simulations using the CHARMM36 additive force field. J Chem Theor Comput 12(1):405–413

    Article  CAS  Google Scholar 

  50. Eastman P, Swails J, Chodera JD, McGibbon RT, Zhao Y, Beauchamp KA, Wang LP, Simmonett AC, Harrigan MP, Stern CD et al (2017) OpenMM 7: rapid development of high performance algorithms for molecular dynamics. PLoS Comput Biol 13(7):e1005659

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Martyna GJ, Tuckerman ME, Tobias DJ, Klein ML (1996) Explicit reversible integrators for extended systems dynamics. Mol Phys 87(5):1117–1157

    Article  CAS  Google Scholar 

  52. Nosé S (1984) A unified formulation of the constant temperature molecular dynamics methods. J Chem Phys 81(1):511–519

    Article  Google Scholar 

  53. Sugita Y, Kitao A, Okamoto Y (2000) Multidimensional replica-exchange method for free-energy calculations. J Chem Phys 113(15):6042–6051

    Article  CAS  Google Scholar 

  54. Zacharias M, Straatsma T, McCammon J (1994) Separation-shifted scaling, a new scaling method for Lennard-Jones interactions in thermodynamic integration. J Chem Phys 100(12):9025–9031

    Article  CAS  Google Scholar 

  55. König G, Pickard FC, Huang J, Thiel W, MacKerell AD, Brooks BR, York DM (2018) A comparison of qm/mm simulations with and without the drude oscillator model based on hydration free energies of simple solutes. Molecules 23(10):2695

    Article  PubMed Central  CAS  Google Scholar 

  56. Shirts MR, Chodera JD (2008) Statistically optimal analysis of samples from multiple equilibrium states. J Chem Phys 129(12):124105

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Klimovich PV, Shirts MR, Mobley DL (2015) Guidelines for the analysis of free energy calculations. J Comput Aided Mol Des 29(5):397–411

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Shirts MR, Mobley DL, Chodera JD, Pande VS (2007) Accurate and efficient corrections for missing dispersion interactions in molecular simulations. J Phys Chem B 111(45):13052–13063

    Article  CAS  PubMed  Google Scholar 

  59. Wennberg CL, Murtola T, Páll S, Abraham MJ, Hess B, Lindahl E (2015) Direct-space corrections enable fast and accurate Lorentz-Berthelot combination rule Lennard-Jones lattice summation. J Chem Theor Comput 11(12):5737–5746

    Article  CAS  Google Scholar 

  60. Leonard AN, Simmonett AC, Pickard FC, Huang J, Venable RM, Klauda JB, Brooks BR, Pastor RW (2018) Comparison of additive and polarizable models with explicit treatment of long-range Lennard-Jones interactions using alkane simulations. J Chem Theor Comput 14(2):948–958

    Article  CAS  Google Scholar 

  61. Zwanzig RW (1954) High-temperature equation of state by a perturbation method. I. Nonpolar gases. J Chem Phys 22(8):1420–1426

    Article  CAS  Google Scholar 

  62. Becke AD (1988) Density-functional exchange-energy approximation with correct asymptotic behavior. Phys Rev A 38(6):3098

    Article  CAS  Google Scholar 

  63. Lee C, Yang W, Parr RG (1988) Development of the colle-salvetti correlation-energy formula into a functional of the electron density. Phys Rev B 37(2):785

    Article  CAS  Google Scholar 

  64. 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):6378–6396

    Article  CAS  PubMed  Google Scholar 

  65. Becke AD (1993) Density-functional thermochemistry. III. The role of exact exchange. J Chem Phys 98(7):5648–5652

    Article  CAS  Google Scholar 

  66. Dunning TH Jr, Peterson KA, Wilson AK (2001) Gaussian basis sets for use in correlated molecular calculations. X. The atoms aluminum through argon revisited. J Chem Phys 114(21):9244–9253

    Article  CAS  Google Scholar 

  67. Shao Y, Gan Z, Epifanovsky E, Gilbert AT, Wormit M, Kussmann J, Lange AW, Behn A, Deng J, Feng X et al (2015) Advances in molecular quantum chemistry contained in the Q-Chem 4 program package. Mol Phys 113(2):184–215

    Article  CAS  Google Scholar 

  68. Woodcock HL III, Hodošček M, Gilbert AT, Gill PM, Schaefer HF III, Brooks BR (2007) Interfacing q-chem and charmm to perform qm/mm reaction path calculations. J Comp Chem 28(9):1485–1502

    Article  CAS  Google Scholar 

  69. Fox SJ, Pittock C, Fox T, Tautermann CS, Skylaris CK (2011) Electrostatic embedding in large-scale first principles quantum mechanical calculations on biomolecules. J Chem Phys 135(22):224107

    Article  PubMed  CAS  Google Scholar 

  70. Petersson GA, Bennett A, Tensfeldt TG, Al-Laham MA, Shirley WA, Mantzaris J (1988) A complete basis set model chemistry. I. The total energies of closed-shell atoms and hydrides of the first-row elements. J Chem Phys 89(4):2193–2218

    Article  CAS  Google Scholar 

  71. Genheden S, Martinez AIC, Criddle MP, Essex JW (2014) Extensive all-atom monte carlo sampling and qm/mm corrections in the sampl4 hydration free energy challenge. J Comput Aided Mol Des 28(3):187–200

    Article  CAS  PubMed  Google Scholar 

  72. Weast RC, Astle MJ, Beyer WH et al (1988) CRC handbook of chemistry and physics, vol 69. CRC Press, Boca Raton, p E-52

    Google Scholar 

  73. Palombo F, Sassi P, Paolantoni M, Morresi A, Cataliotti RS (2006) Comparison of hydrogen bonding in 1-octanol and 2-octanol as probed by spectroscopic techniques. J Phys Chem B 110(36):18017–18025

    Article  CAS  PubMed  Google Scholar 

  74. Lang BE (2012) Solubility of water in octan-1-ol from (275 to 369) k. J Chem Eng Data 57(8):2221–2226

    Article  CAS  Google Scholar 

  75. Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) Development and testing of a general amber force field. J Comp Chem 25(9):1157–1174

    Article  CAS  Google Scholar 

  76. Vanommeslaeghe K, Hatcher E, Acharya C, Kundu S, Zhong S, Shim J, Darian E, Guvench O, Lopes P, Vorobyov I et al (2010) Charmm general force field: a force field for drug-like molecules compatible with the charmm all-atom additive biological force fields. J Comp Chem 31(4):671–690

    CAS  Google Scholar 

  77. Huang L, Roux B (2013) Automated force field parameterization for nonpolarizable and polarizable atomic models based on ab initio target data. J Chem Theor Comput 9(8):3543–3556

    Article  CAS  Google Scholar 

  78. Huang J, Mei Y, König G, Simmonett AC, Pickard FC, Wu Q, Wang LP, MacKerell AD, Brooks BR, Shao Y (2017) An estimation of hybrid quantum mechanical molecular mechanical polarization energies for small molecules using polarizable force-field approaches. J Chem Theor Comput 13(2):679–695

    Article  CAS  Google Scholar 

  79. 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(161):702

    Google Scholar 

  80. Wang H, Yang W (2016) Determining polarizable force fields with electrostatic potentials from quantum mechanical linear response theory. J Chem Phys 144(22):224,107

    Article  CAS  Google Scholar 

  81. König G, Hudson PS, Boresch S, Woodcock HL (2014) Multiscale free energy simulations: an efficient method for connecting classical md simulations to qm or qm/mm free energies using non-boltzmann bennett reweighting schemes. J Chem Theor Comput 10(4):1406–1419

    Article  CAS  Google Scholar 

  82. Li P, Jia X, Pan X, Shao Y, Mei Y (2018) Accelerated computation of free energy profile at ab initio quantum mechanical/molecular mechanics accuracy via a semi-empirical reference potential. I. Weighted thermodynamics perturbation. J Chem Theor Comput 14(11):5583–5596

    Article  CAS  Google Scholar 

  83. Pan X, Li P, Ho J, Pu J, Mei Y, Shao Y (2019) Accelerated computation of free energy profile at ab initio quantum mechanical/molecular mechanical accuracy via a semi-empirical reference potential. II. Recalibrating semi-empirical parameters with force matching. Phys Chem Chem Phys 21:20595–20605

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Ganguly A, Boulanger E, Thiel W (2017) Importance of mm polarization in qm/mm studies of enzymatic reactions: assessment of the qm/mm drude oscillator model. J Chem Theor Comput 13(6):2954–2961

    Article  CAS  Google Scholar 

  85. Kratz EG, Walker AR, Lagardère L, Lipparini F, Piquemal JP, Andrés Cisneros G (2016) Lichem: a qm/mm program for simulations with multipolar and polarizable force fields. J Comp Chem 37(11):1019–1029

    Article  CAS  Google Scholar 

  86. Dziedzic J, Mao Y, Shao Y, Ponder J, Head-Gordon T, Head-Gordon M, Skylaris CK (2016) Tinktep: a fully self-consistent, mutually polarizable qm/mm approach based on the amoeba force field. J Chem Phys 145(12):124106

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The work is supported by National Natural Science Foundation of China (Grant No. 21803057) and by Zhejiang Provincial Natural Science Foundation of China (Grant No. LR19B030001). We acknowledge the usage of the HPC cluster of Westlake University.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jing Huang.

Additional information

Publisher's Note

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

Electronic supplementary material

Below is the link to the electronic supplementary material.

Electronic supplementary material 1 (PDF 1004 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ding, Y., Xu, Y., Qian, C. et al. Predicting partition coefficients of drug-like molecules in the SAMPL6 challenge with Drude polarizable force fields. J Comput Aided Mol Des 34, 421–435 (2020). https://doi.org/10.1007/s10822-020-00282-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-020-00282-5

Navigation