Skip to main content

Advertisement

Log in

The influence of hydrogen bonding on partition coefficients

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

Abstract

This Perspective explores how consideration of hydrogen bonding can be used to both predict and better understand partition coefficients. It is shown how polarity of both compounds and substructures can be estimated from measured alkane/water partition coefficients. When polarity is defined in this manner, hydrogen bond donors are typically less polar than hydrogen bond acceptors. Analysis of alkane/water partition coefficients in conjunction with molecular electrostatic potential calculations suggests that aromatic chloro substituents may be less lipophilic than is generally believed and that some of the effect of chloro-substitution stems from making the aromatic π-cloud less available to hydrogen bond donors. Relationships between polarity and calculated hydrogen bond basicity are derived for aromatic nitrogen and carbonyl oxygen. Aligned hydrogen bond acceptors appear to present special challenges for prediction of alkane/water partition coefficients and this may reflect ‘frustration’ of solvation resulting from overlapping hydration spheres. It is also shown how calculated hydrogen bond basicity can be used to model the effect of aromatic aza-substitution on octanol/water partition coefficients.

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

Access this article

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
Fig. 9

Similar content being viewed by others

References

  1. van de Waterbeemd H, Smith DA, Jones BC (2001) Lipophilicity in PK design: methyl, ethyl, futile. J Comput Aided Mol Des 15:273–286

    Article  Google Scholar 

  2. Giaginis C, Tsantili-Kakoulidou A (2008) Alternative measures of lipophilicity: from octanol–water partitioning to IAM retention. J Pharm Sci 97:2984–3004

    Article  CAS  Google Scholar 

  3. Waring MJ (2010) Lipophilicity in drug discovery. Expert Opin Drug Discov 5:235–248

    Article  CAS  Google Scholar 

  4. Sarkar A, Kellogg GE (2010) Hydrophobicity—shake flasks, protein folding and drug discovery. Curr Top Med Chem 10:67–83

    Article  CAS  Google Scholar 

  5. Collander R (1937) Permeability. Ann Rev Biochem 6:1–18

    Article  Google Scholar 

  6. Lindemann B, Solomon AK (1962) Permeability of luminal surface of intestinal mucosal cells. J Gen Physiol 45:801–810

    Article  CAS  Google Scholar 

  7. Oldendorf WH (1974) Lipid solubility and drug penetration of the blood brain barrier. Exp Biol Med 147:813–816

    Article  CAS  Google Scholar 

  8. Banks WA, Kastin A (1985) Peptides and the blood–brain barrier: lipophilicity as a predictor of permeability. Brain Res Bull 15:287–292

    Article  CAS  Google Scholar 

  9. Yalkowsky SH, Valvan SC (1980) Solubility and partitioning I: solubility of nonelectrolytes in water. J Pharm Sci 69:912–922

    Article  CAS  Google Scholar 

  10. Hansch C, Björkroth JP, Leo A (1987) Hydrophobicity and central nervous system agents: on the principle of minimal hydrophobicity in drug design. J Pharm Sci 76:663–687

    Article  CAS  Google Scholar 

  11. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 23:3–25

    Article  CAS  Google Scholar 

  12. Kenny PW, Montanari CA (2013) Inflation of correlation in the pursuit of drug-likeness. J Comput Aided Mol Des 27:1–13

    Article  CAS  Google Scholar 

  13. Nernst W (1891) Verteilung eines Stoffes zwischen zwei Lösungsmitteln und zwischen Lösungsmittel und Dampfraum. Z Phys Chem 8:110–139

    Article  Google Scholar 

  14. Leo A, Hansch C, Elkins D (1971) Partition coefficients and their uses. Chem Rev 71:525–616

    Article  CAS  Google Scholar 

  15. Dearden JC, Bresnen GM (1988) The measurement of partition coefficients. Quant Struct Act Relatsh 7:133–144

    Article  CAS  Google Scholar 

  16. Mannhold R, Poda GI, Ostermann C, Tetko IV (2009) Calculation of molecular lipophilicity: state-of-the-art and comparison of log P methods on more than 96,000 compounds. J Pharm Sci 98:861–893

    Article  CAS  Google Scholar 

  17. Kenny PW, Montanari CA, Prokopczyk IM (2013) ClogPalk: a method for predicting alkane/water partition coefficient. J Comput Aided Mol Des 27:389–402

    Article  CAS  Google Scholar 

  18. Harris MJ, Higuchi T, Rytting JH (1973) Thermodynamic group contributions from ion pair extraction equilibriums for use in the prediction of partition coefficients. Correlation of surface area with group contributions. J Phys Chem 77:2694–2703

    Article  Google Scholar 

  19. Scherrer RA, Donovan SF (2009) Automated Potentiometric Titrations in KCl/Water-saturated octanol: method for quantifying factors influencing ion-pair partitioning. Anal Chem 81:2768–2778

    Article  CAS  Google Scholar 

  20. Kenny PW, Leitão A, Montanari CA (2014) Ligand efficiency metrics considered harmful. J Comput Aided Mol Des 28:699–710

    Article  CAS  Google Scholar 

  21. Andrews PR, Craik DJ, Martin JL (1984) Functional group contributions to drug-receptor interactions. J Med Chem 27:1648–1657

    Article  CAS  Google Scholar 

  22. Leach AR, Hann MM, Burrows JN, Griffen EJ (2006) Fragment screening: an introduction. Mol BioSyst 2:429–446

    Article  CAS  Google Scholar 

  23. Albert JS, Blomberg N, Breeze AL, Brown AJH, Burrows JN, Edwards PD, Folmer RHA, Geschwindner S, Griffen EJ, Kenny PW, Nowak T, Olsson L, Sanganee H, Shapiro AB (2007) An integrated approach to fragment-based lead generation: philosophy, strategy and case studies from AstraZeneca’s drug discovery programmes. Curr Top Med Chem 7:1600–1629

    Article  CAS  Google Scholar 

  24. Hopkins AL, Keserü GM, Leeson PD, Rees DC, Reynolds CH (2014) The role of ligand efficiency metrics in drug discovery. Nat Rev Drug Discov 13:105–121

    Article  CAS  Google Scholar 

  25. Collander R (1951) Partition of organic compounds between higher alcohols and water. Acta Chem Scand 5:774–780

    Article  CAS  Google Scholar 

  26. 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 1992:2155–2161

    Article  Google Scholar 

  27. Goldman S (1974) The determination and statistical mechanical interpretation of the solubility of water in benzene, carbon tetrachloride, and cyclohexane. Can J Chem 52:1668–1680

    Article  CAS  Google Scholar 

  28. Abraham MH, Whiting GS, Fuchs R, Chambers EJ (1990) Thermodynamics of solute transfer from water to hexadecane. J Chem Soc Perkin Trans 2 1990:291–300

    Article  Google Scholar 

  29. Finkelstein A (1976) Water and nonelectrolyte permeability of lipid bilayer membranes. J Gen Physiol 68:127–135

    Article  CAS  Google Scholar 

  30. Mayer PT, Anderson BD (2002) Transport across 1,9-decadiene precisely mimics the chemical selectivity of the barrier domain in egg lecithin bilayers. J Pharm Sci 91:640–646

    Article  CAS  Google Scholar 

  31. Radzicka A, Wolfenden R (1988) Comparing the polarities of the amino acids: side-chain distribution coefficients between the vapor phase, cyclohexane, 1-octanol, and neutral aqueous solution. Biochem 27:1664–1670

    Article  CAS  Google Scholar 

  32. Shih P, Pedersen LG, Gibbs PR, Wolfenden R (1998) Hydrophobicities of the nucleic acid bases: distribution coefficients from water to cyclohexane. J Mol Biol 280:421–430

    Article  CAS  Google Scholar 

  33. 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. doi:10.1007/s10822-016-9954-8

    Google Scholar 

  34. Rustenburg AS, Dancer J, Lin B, Feng JA, Ortwine DF, Mobley DL, Chodera JD (2016) J Comput Aided Mol Des. doi:10.1007/s10822-016-9971-7

    Google Scholar 

  35. Cabani S, Gianni P, Mollica V, Lepori L (1981) Group contributions to the thermodynamic properties of nonionic organic solutes in dilute aqueous solution. J Solut Chem 10:563–595

    Article  CAS  Google Scholar 

  36. Dearden JC, Bresnen GM (2005) Thermodynamics of water–octanol and water–cyclohexane partitioning of some aromatic compounds. Int J Mol Sci 6:119–129

    Article  CAS  Google Scholar 

  37. Golumbic C, Orchin M, Weller S (1949) Partition studies on phenols. I. Relation between partition coefficient and ionization constant. J Am Chem Soc 71:2624–2627

    Article  CAS  Google Scholar 

  38. Delaney AD, Currie DJ, Holmes HL (1969) Partition coefficients of some N-alkyl and N, N-dialkyl derivatives of some cinnamamides and benzalcyanoacetamides in the system cyclohexane–water. Can J Chem 47:3273–3277

    Article  CAS  Google Scholar 

  39. Seiler P (1974) Interconversion of lipophilicities from hydrocarbon/water systems into the octanol/water system. Eur J Med Chem 9:473–479

    CAS  Google Scholar 

  40. Riebesehl W, Tomlinson E (1984) Enthalpies of solute transfer between alkanes and water determined directly by flow microcalorimetry. J Phys Chem 88:4770–4775

    Article  CAS  Google Scholar 

  41. Young RC, Mitchell RC, Brown TH, Ganellin CR, Griffiths R, Jones M, Rana KK, Saunders D, Smith IR, Sore NE, Wilks TJ (1988) Development of a new physicochemical model for brain penetration and its application to design of centrally acting H2 receptor histamine antagonists. J Med Chem 31:656–671

    Article  CAS  Google Scholar 

  42. Lambert WJ, Wright LA (1989) Development of a preformulation lipophilicity screen utilizing a C-18-derivatized polystyrene–divinylbenzene High-performance liquid chromatographic (HPLC) column. Pharm Res 7:577–586

    Article  Google Scholar 

  43. El Tayar N, Tsai R-S, Testa B, Carrupt P-A, Leo A (1991) Partitioning of solutes in different solvent systems: the contribution of hydrogen-bonding capacity and polarity. J Pharm Sci 80:590–598

    Article  CAS  Google Scholar 

  44. Leahy DE, Morris JJ, Taylor PJ, Wait AR (1992) Model solvent systems for QSAR. Part 2. Fragment values (f-values) for the critical quartet. J Chem Soc Perkin Trans 2 1992:723–731

    Article  Google Scholar 

  45. El Tayar N, Testa B, Carrupt P-A (1992) Polar intermolecular interactions encoded in partition coefficients: an indirect estimation of hydrogen-bond parameters of polyfunctional solutes. J Phys Chem 96:1455–1459

    Article  CAS  Google Scholar 

  46. Abraham MH, Chadha HS, Whiting GS, Mitchell RC (1994) Hydrogen bonding. 32. An analysis of water–octanol and water–alkane partitioning and the ∆logP parameter of Seiler. J Pharm Sci 83:1085–1100

    Article  CAS  Google Scholar 

  47. Habgood MD, Liu ZD, Dehkordi LS, Khodr HH, Abbott J, Hider RC (1999) Investigation into the correlation between structure of hydroxypyridones and blood–brain barrier permeability. Biochem Pharmacol 57:1305–1310

    Article  CAS  Google Scholar 

  48. Wohnsland F, Faller B (2001) High-throughput permeability pH profile and high-throughput alkane/water log P with artificial membranes. J Med Chem 44:923–930

    Article  CAS  Google Scholar 

  49. Zissimos AM, Abraham MH, Barker MC, Box KJ, Tam KY (2002) Calculation of Abraham descriptors from solvent–water partition coefficients in four different systems; evaluation of different methods of calculation. J Chem Soc Perkin Trans 2 2002:470–477

  50. Caron G, Ermondi G (2005) Calculating virtual log P in the alkane/water System (log PN alk) and its derived parameters ∆log PN oct–alk and log DpH alk. J Med Chem 48:3269–3279

    Article  CAS  Google Scholar 

  51. Toulmin A, Wood JM, Kenny PW (2008) Toward prediction of alkane/water partition coefficients. J Med Chem 51:3720–3730

    Article  CAS  Google Scholar 

  52. Wittekindt C, Klamt A (2009) COSMO-RS as a predictive tool for lipophilicity. QSAR Comb Sci 28:874–877

    Article  CAS  Google Scholar 

  53. Shalaeva M, Giulia Caron G, Abramov YA, O’Connell TN, Plummer MS, Yalamanchi G, Farley KA, Goetz GH, Philippe L, Shapiro MJ (2013) Integrating intramolecular hydrogen bonding (IMHB) considerations in drug discovery using ∆logP as a tool. J Med Chem 56:4870–4879

    Article  CAS  Google Scholar 

  54. Ermondi G, Visconti A, Esposito R, Caron G (2014) The Block Relevance (BR) analysis supports the dominating effect of solutes hydrogen bond acidity on ∆log Poct–tol. Eur J Pharm Sci 53:50–54

    Article  CAS  Google Scholar 

  55. Chen D, Oezguen N, Urvil P, Ferguson C, Dann SM, Savidge TC (2016) Regulation of protein–ligand binding affinity by hydrogen bond pairing. Sci Adv 2:e1501240

    Article  Google Scholar 

  56. Tsai R-S, Fan W, El Tayar N, Carrupt P-A, Testa B, Kier LB (1993) Solute-water interactions in the organic phase of a biphasic system. 1. Structural influence of organic solutes on the “water-dragging” effect. J Am Chem Soc 115:9632–9639

    Article  CAS  Google Scholar 

  57. Bard B, Carrupt P-A, Martel S (2012) Determination of alkane/water partition coefficients of polar compounds using hydrophilic interaction chromatography. J Chromatogr A 1260:164–168

    Article  CAS  Google Scholar 

  58. 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: 817–825.

    Article  CAS  Google Scholar 

  59. Jensen DA, Gary RK (2015) Estimation of alkane–water log P for neutral, acidic, and basic compounds using an alkylated polystyrene–divinylbenzene high-performance liquid chromatography column. J Chromatogr A 1417:21–29

    Article  CAS  Google Scholar 

  60. Chung K, Park H (2016) Extended solvent-contact model approach to blind SAMPL5 prediction challenge for the distribution coefficients of drug-like molecules. J Comput Aided Mol Des. doi:10.1007/s10822-016-9928-x

    Google Scholar 

  61. 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. doi:10.1007/s10822-016-9927-y

    Google Scholar 

  62. Bannan CC, Calabro G, Kyu DY, Mobley DL (2016) Calculating partition coefficients of small molecules in octanol/water and cyclohexane/water. J Chem Theor Comput 12:4015–4024

    Article  CAS  Google Scholar 

  63. Kenny PW, Montanari CA, Prokopczyk IM, Ribeiro JFR, Sartori GR (2016) Hydrogen bond basicity prediction for medicinal chemistry design. J Med Chem 59:4278–4288

    Article  CAS  Google Scholar 

  64. Abraham MH (1993) Scales of solute hydrogen-bonding: their construction and application to physicochemical and biochemical processes. Chem Soc Rev 22:73–83

    Article  CAS  Google Scholar 

  65. Taft RW, Gurka D, Joris L, Schleyer PVR, Rakshys JW (1969) Studies of hydrogen-bonded complex formation with p-fluorophenol. V. Linear free energy relationships with OH reference acids. J Am Chem Soc 91:4801–4808

    Article  CAS  Google Scholar 

  66. Abraham MH, Duce PP, Prior DV, Barratt DG, Morris JJ, Taylor PJ (1989) Hydrogen bonding. Part 9. Solute proton-donor and proton-acceptor scales for use in drug design. J Chem Soc Perkin Trans 2 1989:1355–1375

    Article  Google Scholar 

  67. Laurence C, Berthelot M (2000) Observations on the strength of hydrogen bonding. Perspect Drug Discov Des 18:39–60

    Article  CAS  Google Scholar 

  68. Laurence C, Brameld KA, Graton J, Le Questel J-Y, Renault E (2009) The pKBHX database: toward a better understanding of hydrogen-bond basicity for medicinal chemists. J Med Chem 52:4073–4086

    Article  CAS  Google Scholar 

  69. Kenny PW (2009) Hydrogen bonding, electrostatic potential and molecular design. J Chem Inf Model 49:1234–1244

    Article  CAS  Google Scholar 

  70. Murray JS, Ranganathan S, Politzer P (1991) Correlations between the solvent hydrogen bond acceptor parameter β and the calculated molecular electrostatic potential. J Org Chem 56:3734–3739

    Article  CAS  Google Scholar 

  71. Kenny PW (1994) Prediction of hydrogen bond basicity from computed molecular electrostatic properties: implications for comparative molecular field analysis. J Chem Soc Perkin Trans 2 1994:199–202

    Article  Google Scholar 

  72. Graton J, Le Questel J-Y, Maxwell P, Popelier PLA (2016) Hydrogen-bond accepting properties of new heteroaromatic rings chemical motifs: a theoretical study. J Chem Inf Model 56:322–334

    Article  CAS  Google Scholar 

  73. Graton J, Berthelot M, Gal J-F, Laurence C, Lebreton J, Le Questel J-Y, Maria P-C, Robins R (2003) The nicotinic pharmacophore: thermodynamics of the hydrogen-bonding complexation of nicotine, nornicotine, and models. J Org Chem 68:8208–8221

    Article  CAS  Google Scholar 

  74. Bissantz C, Kuhn B, Stahl M (2010) A medicinal chemist’s guide to molecular interactions. J Med Chem 53:5061–5084

    Article  CAS  Google Scholar 

  75. Persch E, Dumele O, Diederich F (2015) Molecular recognition in chemical and biological systems. Angew Chem Int Ed 54:3290–3327

    Article  CAS  Google Scholar 

  76. OEChem Toolkit. OpenEye Scientific Software. http://www.eyesopen.com/oechem-tk. Accessed 19 Aug 2016

  77. Spicoli Toolkit. OpenEye Scientific Software. http://www.eyesopen.com/spicoli-tk. Accessed 19 Aug 2016

  78. OpenEye Scientific Software, 9 Bisbee Court, Suite D, Santa Fe, NM 87508. http://www.eyesopen.com. Accessed 28 Feb 2013

  79. SMARTS Theory Manual. Daylight Chemical Information Systems. http://www.daylight.com/dayhtml/doc/theory/theory.smarts.html

  80. SMARTS at Wikipedia http://en.wikipedia.org/wiki/Smiles_arbitrary_target_specification

  81. Weininger D (1988) SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Comp Sci 28:31–36

    Article  CAS  Google Scholar 

  82. Weininger D, Weininger A, Weininger JL (1989) SMILES. 2. Algorithm for generation of unique SMILES notation. J Chem Inf Comp Sci 29:97–101

    Article  CAS  Google Scholar 

  83. OMEGA. OpenEye Scientific Software http://www.eyesopen.com/omega

  84. Hawkins PCD, Skillman AG, Warren GL, Ellingson BA, Stahl MT (2010) Conformer generation with OMEGA: algorithm and validation using high quality structures from the protein databank and Cambridge structural database. J Chem Inf Model 50:572–584

    Article  CAS  Google Scholar 

  85. Halgren TA (1999) MMFF VI. MMFF94S option for energy minimization studies. J Comp Chem 20:720–729

    Article  CAS  Google Scholar 

  86. SZYBKI. OpenEye Scientific Software http://www.eyesopen.com/szybki

  87. Bondi A (1964) van der Waals volumes and radii. J Phys Chem 68:441–451

    Article  CAS  Google Scholar 

  88. 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, 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 Ö, Foresman JB, Ortiz JV, Cioslowski J, Fox DJ (2009) Gaussian 09, Revision A.1, Gaussian, Inc., Wallingford

    Google Scholar 

  89. Szabo A, Ostlund NS (1996) Modern quantum chemistry. Introduction to advanced electronic structure theory. Dover, Mineola

    Google Scholar 

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

    Article  CAS  Google Scholar 

  91. 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 3:785–789

    Article  Google Scholar 

  92. Møller C, Plesset MS (1934) Note on the approximation treatment for many-electron systems. Phys Rev 46:618–622.

    Article  Google Scholar 

  93. Frisch MJ, Head-Gordon M, Pople JA (1990) A direct MP2 gradient method. Chem Phys Lett 166:275–280

    Article  CAS  Google Scholar 

  94. Hehre WJ, Pople JA (1971) Self-consistent molecular-orbital methods. IX. Extended Gaussian-type basis for molecular-orbital studies of organic molecules. J Chem Phys 54:724–728

    Article  Google Scholar 

  95. Frisch MJ, Pople JA, Binkley JS (1984) Self-consistent molecular orbital methods. 25. Supplementary functions for Gaussian basis sets. J Chem Phys 80:3265–3269

    Article  CAS  Google Scholar 

  96. Spitznagel GW, Clark T, Chandrasekhar J, Schleyer PVR (1982) Stabilization of methyl anions by first-row substituents. The superiority of diffuse function-augmented basis sets for anion calculations. J Comput Chem 3:363–371

    Article  CAS  Google Scholar 

  97. Hansch C, Leo A, Hoekman D (1995) Exploring QSAR. American Chemical Society, Washington DC

    Google Scholar 

  98. Kenny PW, Montanari CA, Prokopczyk IM, Sala FA, Sartori GR (2013) Automated molecule editing in molecular design. J Comput Aided Mol Des 27:655–664

    Article  CAS  Google Scholar 

  99. Kenny PW, Sadowski J (2005) Structure modification in chemical databases. Methods and principles in medicinal chemistry. In: Oprea T (ed) Chemoinformatics in drug discovery 23:271–285

  100. Leach AG, Jones HD, Cosgrove DA, Kenny PW, Ruston L, MacFaul P, Wood JM, Colclough N, Law B (2006) Matched molecular pairs as a guide in the optimization of pharmaceutical properties; a study of aqueous solubility, plasma protein binding and oral exposure. J Med Chem 49:6672–6682

    Article  CAS  Google Scholar 

  101. Hussain J, Rea C (2010) Computationally efficient algorithm to identify matched molecular pairs (MMPs) in large data sets. J Chem Inf Model 50:339–348

    Article  CAS  Google Scholar 

  102. Hu X, Hu Y, Vogt M, Stumpfe D, Bajorath J (2012) MMP-Cliffs: systematic identification of activity cliffs on the basis of matched molecular pairs. J Chem Inf Model 52:1138–1145

    Article  CAS  Google Scholar 

  103. Dossetter AG, Griffen EJ, Leach AG (2013) Matched molecular pair analysis in drug discovery. Drug Discov Today 18:724–731

    Article  CAS  Google Scholar 

  104. Kramer C, Fuchs JE, Whitebread S, Gedeck P, Liedl KR (2014) Matched molecular pair analysis: significance and the impact of experimental uncertainty. J Med Chem 57:3786–3802

    Article  CAS  Google Scholar 

  105. JMP version 12.0, SAS Institute, Cary, NC 27513. http://www.jmp.com

  106. Ritchie TJ, Macdonald SJF, Pickett SD (2015) Insights into the impact of N- and O-methylation on aqueous solubility and lipophilicity using matched molecular pair analysis. MedChemComm 6:1787–1797

    Article  CAS  Google Scholar 

  107. Mobley DL, Baker JR, Barber AE, Fennell CJ, Dill KA (2008) Charge asymmetries in hydration of polar solutes. J Phys Chem B 112:2405–2414

    Article  CAS  Google Scholar 

  108. Mukhopadhyay A, Fenley AT, Tolokh IS, Onufriev AV (2012) Charge hydration asymmetry: the basic principle and how to use it to test and improve water models. J Phys Chem B 116:9776–9783

    Article  CAS  Google Scholar 

  109. Reif MM, Hünenberger PH (2016) Origin of asymmetric solvation effects for ions in water and organic solvents investigated using molecular dynamics simulations: the Swain acity-basity scale revisited. J Phys Chem B 120:8485–8517

    Article  CAS  Google Scholar 

  110. Ritchie TJ, Macdonald SJF (2014) Physicochemical descriptors of aromatic character and their use in drug discovery. J Med Chem 57:5206–5215

    Article  CAS  Google Scholar 

  111. Adler TK, Albert A (1960) Diazaindenes (“azaindoles”). Part I. Ionization constants and spectra. J Chem Soc 1960:1794–1797

    Article  Google Scholar 

  112. Topliss JG (1972) Utilization of operational schemes for analog synthesis in drug design. J Med Chem 15:1006–1011

    Article  CAS  Google Scholar 

  113. Brown DG, Gagnon MM, Boström J (2015) Understanding our love affair with p-chlorophenyl: present day implications from historical biases of reagent selection. J Med Chem 58:2390–2405

    Article  CAS  Google Scholar 

  114. Leahy DE, Morris JJ, Taylor PJ, Wait AR (1994) Model solvent systems for QSAR. Part IV. The hydrogen bond acceptor behaviour of heterocycles. J Phys Org Chem 7:743–750

    Article  CAS  Google Scholar 

  115. Edwards JO, Pearson RG (1962) The factors determining nucleophilic reactivities. J Am Chem Soc 84:16–24

    Article  CAS  Google Scholar 

  116. Jorgensen WL, Pranata J (1990) Importance of secondary interactions in triply hydrogen bonded complexes: guanine-cytosine vs uracil-2,6-diaminopyridine. J Am Chem Soc 112:2008–2010

    Article  CAS  Google Scholar 

  117. Hann MM, Leach AR, Harper G (2001) Molecular complexity and its impact on the probability of finding leads for drug discovery. J Chem Inf Comp Sci 41:856–864

    Article  CAS  Google Scholar 

  118. Johnson ME, Malardier-Jugroot C, Murarka RK, Head-Gordon T (2009) Hydration water dynamics near biological interfaces. J Phys Chem B 113:4082–4092

    Article  CAS  Google Scholar 

  119. Bethel PA, Gerhardt S, Jones EV, Kenny PW, Karoutchi GI, Morley AD, Oldham K, Rankine N, Augustin M, Krapp S, Simader H, Steinbacher S (2009) Design of selective cathepsin inhibitors. Bioorg Med Chem Lett 19:4622–4625

    Article  CAS  Google Scholar 

  120. Murray CW, Rees DC (2016) Opportunity knocks: organic chemistry for fragment-based drug discovery (FBDD). Angew Chem Int Ed 55:488–492

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank FAPESP (Fundação de Amparo à Pesquisa do Estado de São Paulo; Grant No. 2013/18009-4) and CNPq (Conselho Nacional de Pesquisa; Grant No. 303991/2014-3) for financial support. NMB and IMP thank Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and JFRR and GRS thank CNPq for scholarships. We are grateful to OpenEye Scientific Software for an academic software license. We also thank the two anonymous reviewers of the manuscript for their constructive and insightful comments.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Peter W. Kenny.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Borges, N.M., Kenny, P.W., Montanari, C.A. et al. The influence of hydrogen bonding on partition coefficients. J Comput Aided Mol Des 31, 163–181 (2017). https://doi.org/10.1007/s10822-016-0002-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-016-0002-5

Keywords

Navigation