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Quantum chemical predictions of water–octanol partition coefficients applied to the SAMPL6 logP blind challenge

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Abstract

Theoretical approaches for predicting physicochemical properties are valuable tools for accelerating the drug discovery process. In this work, quantum chemical methods are used to predict water–octanol partition coefficients as a part of the SAMPL6 blind challenge. The SMD continuum solvent model was employed with MP2 and eight DFT functionals in conjunction with correlation consistent basis sets to determine the water–octanol transfer free energy. Several tactics towards improving the predictions of the partition coefficient were examined, including increasing the quality of basis sets, considering tautomerization, and accounting for inhomogeneities in the water and n-octanol phases. Evaluation of these various schemes highlights the impact of modeling approaches across different methods. With the inclusion of tautomers and adjustments to the permittivity constants, the best predictions were obtained with smaller basis sets and the O3LYP functional, which yielded an RMSE of 0.79 logP units. The results presented correspond to the SAMPL6 logP submission IDs: DYXBT, O7DJK, and AHMTF.

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References

  1. Muhammad U, Uzairu A, Ebuka Arthur D (2018) Review on: quantitative structure activity relationship (QSAR) modeling. J Anal Pharm Res 7:240–242. https://doi.org/10.15406/japlr.2018.07.00232

    Article  Google Scholar 

  2. Wang T, Wu M-B, Lin J-P, Yang L-R (2015) Quantitative structure–activity relationship: promising advances in drug discovery platforms. Expert Opin Drug Discov 10:1283–1300. https://doi.org/10.1517/17460441.2015.1083006

    Article  CAS  PubMed  Google Scholar 

  3. Fourches D, Ash J (2019) 4D-quantitative structure–activity relationship modeling: making a comeback. Expert Opin Drug Discov 14:1227–1235. https://doi.org/10.1080/17460441.2019.1664467

    Article  CAS  PubMed  Google Scholar 

  4. Galvez J, Galvez-Llompart M, Zanni R, Garcia-Domenech R (2013) Advances in the molecular modeling and quantitative structure–activity relationship-based design for antihistamines. Expert Opin Drug Discov 8:305–317. https://doi.org/10.1517/17460441.2013.748745

    Article  CAS  PubMed  Google Scholar 

  5. Polanski J, Bak A, Gieleciak R, Magdziarz T (2006) Modeling robust QSAR. J Chem Inf Model 46:2310–2318. https://doi.org/10.1021/ci050314b

    Article  CAS  PubMed  Google Scholar 

  6. Neves BJ, Braga RC, Melo-Filho CC et al (2018) QSAR-based virtual screening: advances and applications in drug discovery. Front Pharmacol 9:1275. https://doi.org/10.3389/fphar.2018.01275

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Polishchuk P (2017) Interpretation of quantitative structure-activity relationship models: past, present, and future. J Chem Inf Model 57:2618–2639. https://doi.org/10.1021/acs.jcim.7b00274

    Article  CAS  PubMed  Google Scholar 

  8. Piir G, Kahn I, García-Sosa AT et al (2018) Best practices for QSAR model reporting: physical and chemical properties, ecotoxicity, environmental fate, human health, and toxicokinetics endpoints. Environ Health Perspect 126:126001. https://doi.org/10.1289/EHP3264

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Mobley DL, Wymer KL, Lim NM, Guthrie JP (2014) Blind prediction of solvation free energies from the SAMPL4 challenge. J Comput Aided Mol Des 28:135–150. https://doi.org/10.1007/s10822-014-9718-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Skillman AG (2012) SAMPL3: blinded prediction of host–guest binding affinities, hydration free energies, and trypsin inhibitors. J Comput Aided Mol Des 26:473–474. https://doi.org/10.1007/s10822-012-9580-z

    Article  CAS  PubMed  Google Scholar 

  11. Geballe MT, Skillman AG, Nicholls A et al (2010) The SAMPL2 blind prediction challenge: introduction and overview. J Comput Aided Mol Des 24:259–279. https://doi.org/10.1007/s10822-010-9350-8

    Article  CAS  PubMed  Google Scholar 

  12. Geballe MT, Guthrie JP (2012) The SAMPL3 blind prediction challenge: transfer energy overview. J Comput Aided Mol Des 26:489–496. https://doi.org/10.1007/s10822-012-9568-8

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  14. Muddana HS, Varnado CD, Bielawski CW et al (2012) Blind prediction of host–guest binding affinities: a new SAMPL3 challenge. J Comput Aided Mol Des 26:475–487. https://doi.org/10.1007/s10822-012-9554-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Bannan CC, Burley KH, Chiu M et al (2016) Blind prediction of cyclohexane–water distribution coefficients from the SAMPL5 challenge. J Comput Aided Mol Des 30:927–944. https://doi.org/10.1007/s10822-016-9954-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Pickard FC, Konig G, Tofoleanu F et al (2016) Blind prediction of distribution in the SAMPL5 challenge with QM based protomer and pK a corrections. J Comput Aided Mol Des 30:1087–1100. https://doi.org/10.1007/s10822-016-9955-7

    Article  CAS  PubMed  Google Scholar 

  18. Jones MR, Brooks BR, Wilson AK (2016) Partition coefficients for the SAMPL5 challenge using transfer free energies. J Comput Aided Mol Des 30:1129–1138. https://doi.org/10.1007/s10822-016-9964-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. König G, Pickard FC, Huang J 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 30:989–1006. https://doi.org/10.1007/s10822-016-9936-x

    Article  CAS  PubMed  Google Scholar 

  20. Klamt A, Eckert F, Reinisch J, Wichmann K (2016) Prediction of cyclohexane-water distribution coefficients with COSMO-RS on the SAMPL5 data set. J Comput Aided Mol Des 30:959–967. https://doi.org/10.1007/s10822-016-9927-y

    Article  CAS  PubMed  Google Scholar 

  21. Işık M, Levorse D, Rustenburg AS et al (2018) pKa measurements for the SAMPL6 prediction challenge for a set of kinase inhibitor-like fragments. J Comput Aided Mol Des 32:1117–1138. https://doi.org/10.1007/s10822-018-0168-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Işık M, Levorse D, Mobley DL et al (2019) 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 

  23. O’Boyle NM, Banck M, James CA et al (2011) Open Babel: an open chemical toolbox. J Cheminform 3:33. https://doi.org/10.1186/1758-2946-3-33

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Frisch MJ, Trucks GW, Schlegel HB, et al Gaussian 16 Revision A.03

  25. Head-Gordon M, Pople JA, Frisch MJ (1988) MP2 energy evaluation by direct methods. Chem Phys Lett 153:503–506. https://doi.org/10.1016/0009-2614(88)85250-3

    Article  CAS  Google Scholar 

  26. Frisch MJ, Head-Gordon M, Pople JA (1990) A direct MP2 gradient method. Chem Phys Lett 166:275–280. https://doi.org/10.1016/0009-2614(90)80029-D

    Article  CAS  Google Scholar 

  27. Sæbø S, Almlöf J (1989) Avoiding the integral storage bottleneck in LCAO calculations of electron correlation. Chem Phys Lett 154:83–89. https://doi.org/10.1016/0009-2614(89)87442-1

    Article  Google Scholar 

  28. Head-Gordon M, Head-Gordon T (1994) Analytic MP2 frequencies without fifth-order storage. Theory and application to bifurcated hydrogen bonds in the water hexamer. Chem Phys Lett 220:122–128. https://doi.org/10.1016/0009-2614(94)00116-2

    Article  CAS  Google Scholar 

  29. Woon DE, Dunning TH (1993) Gaussian basis sets for use in correlated molecular calculations. III. The atoms aluminum through argon. J Chem Phys 98:1358. https://doi.org/10.1063/1.464303

    Article  CAS  Google Scholar 

  30. Dunning TH Jr et al (1989) Gaussian basis sets for use in correlated molecular calculations. I. The atoms boron through neon and hydrogen. J Chem Phys 90:1007. https://doi.org/10.1063/1.456153

    Article  CAS  Google Scholar 

  31. Dunning TH, Peterson KA, Wilson AK (2001) Gaussian basis sets for use in correlated molecular calculations. X. The atoms aluminum through argon revisited. J Chem Phys 114:9244–9253. https://doi.org/10.1063/1.1367373

    Article  CAS  Google Scholar 

  32. 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 113:6378–6396. https://doi.org/10.1021/jp810292n

    Article  CAS  PubMed  Google Scholar 

  33. Becke AD (1988) Density-functional exchange-energy approximation with correct asymptotic behavior. Phys Rev A 38:3098–3100. https://doi.org/10.1103/PhysRevA.38.3098

    Article  CAS  Google Scholar 

  34. 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 37:785–789. https://doi.org/10.1103/PhysRevB.37.785

    Article  CAS  Google Scholar 

  35. Stephens PJ, Devlin FJ, Chabalowski CF, Frisch MJ (1994) Ab initio calculation of vibrational absorption and circular dichroism spectra using density functional force fields. J Phys Chem 98:11623–11627. https://doi.org/10.1021/j100096a001

    Article  CAS  Google Scholar 

  36. Becke AD (1993) Density-functional thermochemistry. III. The role of exact exchange. J Chem Phys 98:5648–5652. https://doi.org/10.1063/1.464913

    Article  CAS  Google Scholar 

  37. Handy NC, Cohen AJ (2001) Left-right correlation energy. Mol Phys 99:403–412. https://doi.org/10.1080/00268970010018431

    Article  CAS  Google Scholar 

  38. Adamo C, Barone V (1999) Toward reliable density functional methods without adjustable parameters: the PBE0 model. J Chem Phys 110:6158. https://doi.org/10.1063/1.478522

    Article  CAS  Google Scholar 

  39. Peverati R, Truhlar DG (2011) Improving the accuracy of hybrid meta-GGA density functionals by range separation. J Phys Chem Lett 2:2810–2817. https://doi.org/10.1021/jz201170d

    Article  CAS  Google Scholar 

  40. Grimme S (2006) Semiempirical hybrid density functional with perturbative second-order correlation. J Chem Phys 124:34108. https://doi.org/10.1063/1.2148954

    Article  CAS  Google Scholar 

  41. Chai J-D, Head-Gordon M (2008) Systematic optimization of long-range corrected hybrid density functionals. J Chem Phys 128:84106. https://doi.org/10.1063/1.2834918

    Article  CAS  Google Scholar 

  42. Chai J-D, Head-Gordon M (2008) Long-range corrected hybrid density functionals with damped atom–atom dispersion corrections. Phys Chem Chem Phys 10:6615. https://doi.org/10.1039/b810189b

    Article  CAS  PubMed  Google Scholar 

  43. Hasted JB, Ritson DM, Collie CH (1948) Dielectric properties of aqueous ionic solutions. Parts I and II. J Chem Phys 16:1–21. https://doi.org/10.1063/1.1746645

    Article  CAS  Google Scholar 

  44. Gavish N, Promislow K (2016) Dependence of the dielectric constant of electrolyte solutions on ionic concentration: a microfield approach. Phys Rev E 94:12611. https://doi.org/10.1103/PhysRevE.94.012611

    Article  CAS  Google Scholar 

  45. Grunwald E, Pan KC, Effio A (1976) Hydrogen bonding in polar liquid solutions. 4. Effect of hydrogen-bonding solutes on dielectric constant and solvent structure in 1-octanol. J Phys Chem 80:2937–2940. https://doi.org/10.1021/j100908a004

    Article  CAS  Google Scholar 

  46. Westall JC, Johnson CA, Zhang W (1990) Distribution of lithium chloride, sodium chloride, potassium chloride, hydrochloric acid, magnesium chloride, and calcium chloride between octanol and water. Environ Sci Technol 24:1803–1810. https://doi.org/10.1021/es00082a003

    Article  CAS  Google Scholar 

  47. Riojas AG, Wilson AK (2014) Solv-ccCA: implicit solvation and the correlation consistent composite approach for the determination of p K a. J Chem Theory Comput 10:1500–1510. https://doi.org/10.1021/ct400908z

    Article  CAS  PubMed  Google Scholar 

  48. Zeng Q, Jones MR, Brooks BR (2018) Absolute and relative pKa predictions via a DFT approach applied to the SAMPL6 blind challenge. J Comput Aided Mol Des 32:1179–1189. https://doi.org/10.1007/s10822-018-0150-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Sassi P, Paolantoni M, Cataliotti RS et al (2004) Water/alcohol mixtures: a spectroscopic study of the water-saturated 1-octanol solution. J Phys Chem B 108:19557–19565. https://doi.org/10.1021/jp046647d

    Article  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  Google Scholar 

  53. Riebesehl W, Tomlinson E (1986) Thermodynamics of non-electrolyte transfer between octanol and water. J Solution Chem 15:141–150. https://doi.org/10.1007/BF00646285

    Article  CAS  Google Scholar 

  54. Berti P, Cabani S, Conti G, Mollica V (1986) Thermodynamic study of organic compounds in octan-1-ol. Processes of transfer from gas and from dilute aqueous solution. J Chem Soc Faraday Trans 1(82):2547. https://doi.org/10.1039/f19868202547

    Article  Google Scholar 

  55. Dallas AJ, Carr PW (1992) A thermodynamic and solvatochromic investigation of the effect of water on the phase-transfer properties of octan-1-ol. J Chem Soc Perkin Trans 2:2155. https://doi.org/10.1039/p29920002155

    Article  Google Scholar 

  56. Cabani S, Conti G, Mollica V, Bernazzani L (1991) Free energy and enthalpy changes for the process of transfer from gas and from dilute aqueous solutions of some alkanes and monofunctional saturated organic compounds. J Chem Soc Faraday Trans 87:2433. https://doi.org/10.1039/ft9918702433

    Article  CAS  Google Scholar 

  57. Bernazzani L, Cabani S, Conti G, Mollica V (1995) Thermodynamic study of the partitioning of organic compounds between water and octan-1-ol. Effects of water as cosolvent in the organic phase. J Chem Soc Faraday Trans 91:649. https://doi.org/10.1039/ft9959100649

    Article  CAS  Google Scholar 

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Acknowledgements

The authors would like to thank Phillip S. Hudson, Andreas Krämer, and Andy Simmonett for their excellent dialogue and insight. We extend appreciation to Richard Venable, John Legato, Daniel Roe, and Rubén Meana Pañeda for technical assistance. This research was supported by the Intermural Research Program of the National Heart, Lung, and Blood Institute of the National Institutes of Health and utilized the high-performance computational capabilities of the LoBoS and Biowulf Linux clusters at the National Institutes of Health (https://www.lobos.nih.gov and https://biowulf.nih.gov).

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  1. Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, 20892-5690, USA

    Michael R. Jones & Bernard R. Brooks

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Jones, M.R., Brooks, B.R. Quantum chemical predictions of water–octanol partition coefficients applied to the SAMPL6 logP blind challenge. J Comput Aided Mol Des 34, 485–493 (2020). https://doi.org/10.1007/s10822-020-00286-1

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