Journal of Computer-Aided Molecular Design

, Volume 31, Issue 2, pp 163–181 | Cite as

The influence of hydrogen bonding on partition coefficients

  • Nádia Melo Borges
  • Peter W. KennyEmail author
  • Carlos A. Montanari
  • Igor M. Prokopczyk
  • Jean F. R. Ribeiro
  • Josmar R. Rocha
  • Geraldo Rodrigues Sartori


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.


Alkane/water Hydrogen bonding Lipophilicity LogP Octanol/water Molecular design Partition coefficient Polarity Property-based drug design 



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.

Supplementary material

10822_2016_2_MOESM1_ESM.pdf (1.9 mb)
Supplementary material 1 (PDF 1940 KB)
10822_2016_2_MOESM2_ESM.docx (93 kb)
Supplementary material 2 (DOCX 93 KB)
10822_2016_2_MOESM3_ESM.xml (1 kb)
Supplementary material 3 (XML 0 KB)
10822_2016_2_MOESM4_ESM.docx (941 kb)
Supplementary material 4 (DOCX 940 KB)
10822_2016_2_MOESM5_ESM.docx (20 kb)
Supplementary material 5 (DOCX 19 KB) (465 kb)
Supplementary material 6 (ZIP 464 KB)


  1. 1.
    van de Waterbeemd H, Smith DA, Jones BC (2001) Lipophilicity in PK design: methyl, ethyl, futile. J Comput Aided Mol Des 15:273–286CrossRefGoogle Scholar
  2. 2.
    Giaginis C, Tsantili-Kakoulidou A (2008) Alternative measures of lipophilicity: from octanol–water partitioning to IAM retention. J Pharm Sci 97:2984–3004CrossRefGoogle Scholar
  3. 3.
    Waring MJ (2010) Lipophilicity in drug discovery. Expert Opin Drug Discov 5:235–248CrossRefGoogle Scholar
  4. 4.
    Sarkar A, Kellogg GE (2010) Hydrophobicity—shake flasks, protein folding and drug discovery. Curr Top Med Chem 10:67–83CrossRefGoogle Scholar
  5. 5.
    Collander R (1937) Permeability. Ann Rev Biochem 6:1–18CrossRefGoogle Scholar
  6. 6.
    Lindemann B, Solomon AK (1962) Permeability of luminal surface of intestinal mucosal cells. J Gen Physiol 45:801–810CrossRefGoogle Scholar
  7. 7.
    Oldendorf WH (1974) Lipid solubility and drug penetration of the blood brain barrier. Exp Biol Med 147:813–816CrossRefGoogle Scholar
  8. 8.
    Banks WA, Kastin A (1985) Peptides and the blood–brain barrier: lipophilicity as a predictor of permeability. Brain Res Bull 15:287–292CrossRefGoogle Scholar
  9. 9.
    Yalkowsky SH, Valvan SC (1980) Solubility and partitioning I: solubility of nonelectrolytes in water. J Pharm Sci 69:912–922CrossRefGoogle Scholar
  10. 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 76:663–687CrossRefGoogle Scholar
  11. 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 23:3–25CrossRefGoogle Scholar
  12. 12.
    Kenny PW, Montanari CA (2013) Inflation of correlation in the pursuit of drug-likeness. J Comput Aided Mol Des 27:1–13CrossRefGoogle Scholar
  13. 13.
    Nernst W (1891) Verteilung eines Stoffes zwischen zwei Lösungsmitteln und zwischen Lösungsmittel und Dampfraum. Z Phys Chem 8:110–139CrossRefGoogle Scholar
  14. 14.
    Leo A, Hansch C, Elkins D (1971) Partition coefficients and their uses. Chem Rev 71:525–616CrossRefGoogle Scholar
  15. 15.
    Dearden JC, Bresnen GM (1988) The measurement of partition coefficients. Quant Struct Act Relatsh 7:133–144CrossRefGoogle Scholar
  16. 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 98:861–893CrossRefGoogle Scholar
  17. 17.
    Kenny PW, Montanari CA, Prokopczyk IM (2013) ClogPalk: a method for predicting alkane/water partition coefficient. J Comput Aided Mol Des 27:389–402CrossRefGoogle Scholar
  18. 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 77:2694–2703CrossRefGoogle Scholar
  19. 19.
    Scherrer RA, Donovan SF (2009) Automated Potentiometric Titrations in KCl/Water-saturated octanol: method for quantifying factors influencing ion-pair partitioning. Anal Chem 81:2768–2778CrossRefGoogle Scholar
  20. 20.
    Kenny PW, Leitão A, Montanari CA (2014) Ligand efficiency metrics considered harmful. J Comput Aided Mol Des 28:699–710CrossRefGoogle Scholar
  21. 21.
    Andrews PR, Craik DJ, Martin JL (1984) Functional group contributions to drug-receptor interactions. J Med Chem 27:1648–1657CrossRefGoogle Scholar
  22. 22.
    Leach AR, Hann MM, Burrows JN, Griffen EJ (2006) Fragment screening: an introduction. Mol BioSyst 2:429–446CrossRefGoogle Scholar
  23. 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 7:1600–1629CrossRefGoogle Scholar
  24. 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 13:105–121CrossRefGoogle Scholar
  25. 25.
    Collander R (1951) Partition of organic compounds between higher alcohols and water. Acta Chem Scand 5:774–780CrossRefGoogle Scholar
  26. 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 1992:2155–2161CrossRefGoogle Scholar
  27. 27.
    Goldman S (1974) The determination and statistical mechanical interpretation of the solubility of water in benzene, carbon tetrachloride, and cyclohexane. Can J Chem 52:1668–1680CrossRefGoogle Scholar
  28. 28.
    Abraham MH, Whiting GS, Fuchs R, Chambers EJ (1990) Thermodynamics of solute transfer from water to hexadecane. J Chem Soc Perkin Trans 2 1990:291–300CrossRefGoogle Scholar
  29. 29.
    Finkelstein A (1976) Water and nonelectrolyte permeability of lipid bilayer membranes. J Gen Physiol 68:127–135CrossRefGoogle Scholar
  30. 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 91:640–646CrossRefGoogle Scholar
  31. 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 27:1664–1670CrossRefGoogle Scholar
  32. 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 280:421–430CrossRefGoogle Scholar
  33. 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. 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. 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 10:563–595CrossRefGoogle Scholar
  36. 36.
    Dearden JC, Bresnen GM (2005) Thermodynamics of water–octanol and water–cyclohexane partitioning of some aromatic compounds. Int J Mol Sci 6:119–129CrossRefGoogle Scholar
  37. 37.
    Golumbic C, Orchin M, Weller S (1949) Partition studies on phenols. I. Relation between partition coefficient and ionization constant. J Am Chem Soc 71:2624–2627CrossRefGoogle Scholar
  38. 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 47:3273–3277CrossRefGoogle Scholar
  39. 39.
    Seiler P (1974) Interconversion of lipophilicities from hydrocarbon/water systems into the octanol/water system. Eur J Med Chem 9:473–479Google Scholar
  40. 40.
    Riebesehl W, Tomlinson E (1984) Enthalpies of solute transfer between alkanes and water determined directly by flow microcalorimetry. J Phys Chem 88:4770–4775CrossRefGoogle Scholar
  41. 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 31:656–671CrossRefGoogle Scholar
  42. 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 7:577–586CrossRefGoogle Scholar
  43. 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 80:590–598CrossRefGoogle Scholar
  44. 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 1992:723–731CrossRefGoogle Scholar
  45. 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 96:1455–1459CrossRefGoogle Scholar
  46. 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 83:1085–1100CrossRefGoogle Scholar
  47. 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 57:1305–1310CrossRefGoogle Scholar
  48. 48.
    Wohnsland F, Faller B (2001) High-throughput permeability pH profile and high-throughput alkane/water log P with artificial membranes. J Med Chem 44:923–930CrossRefGoogle Scholar
  49. 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 2002:470–477Google Scholar
  50. 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 48:3269–3279CrossRefGoogle Scholar
  51. 51.
    Toulmin A, Wood JM, Kenny PW (2008) Toward prediction of alkane/water partition coefficients. J Med Chem 51:3720–3730CrossRefGoogle Scholar
  52. 52.
    Wittekindt C, Klamt A (2009) COSMO-RS as a predictive tool for lipophilicity. QSAR Comb Sci 28:874–877CrossRefGoogle Scholar
  53. 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 56:4870–4879CrossRefGoogle Scholar
  54. 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 53:50–54CrossRefGoogle Scholar
  55. 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:e1501240CrossRefGoogle Scholar
  56. 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 115:9632–9639CrossRefGoogle Scholar
  57. 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 1260:164–168CrossRefGoogle Scholar
  58. 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.CrossRefGoogle Scholar
  59. 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 1417:21–29CrossRefGoogle Scholar
  60. 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. 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. 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 12:4015–4024CrossRefGoogle Scholar
  63. 63.
    Kenny PW, Montanari CA, Prokopczyk IM, Ribeiro JFR, Sartori GR (2016) Hydrogen bond basicity prediction for medicinal chemistry design. J Med Chem 59:4278–4288CrossRefGoogle Scholar
  64. 64.
    Abraham MH (1993) Scales of solute hydrogen-bonding: their construction and application to physicochemical and biochemical processes. Chem Soc Rev 22:73–83CrossRefGoogle Scholar
  65. 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 91:4801–4808CrossRefGoogle Scholar
  66. 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 1989:1355–1375CrossRefGoogle Scholar
  67. 67.
    Laurence C, Berthelot M (2000) Observations on the strength of hydrogen bonding. Perspect Drug Discov Des 18:39–60CrossRefGoogle Scholar
  68. 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 52:4073–4086CrossRefGoogle Scholar
  69. 69.
    Kenny PW (2009) Hydrogen bonding, electrostatic potential and molecular design. J Chem Inf Model 49:1234–1244CrossRefGoogle Scholar
  70. 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 56:3734–3739CrossRefGoogle Scholar
  71. 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 1994:199–202CrossRefGoogle Scholar
  72. 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 56:322–334CrossRefGoogle Scholar
  73. 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 68:8208–8221CrossRefGoogle Scholar
  74. 74.
    Bissantz C, Kuhn B, Stahl M (2010) A medicinal chemist’s guide to molecular interactions. J Med Chem 53:5061–5084CrossRefGoogle Scholar
  75. 75.
    Persch E, Dumele O, Diederich F (2015) Molecular recognition in chemical and biological systems. Angew Chem Int Ed 54:3290–3327CrossRefGoogle Scholar
  76. 76.
    OEChem Toolkit. OpenEye Scientific Software. Accessed 19 Aug 2016
  77. 77.
    Spicoli Toolkit. OpenEye Scientific Software. Accessed 19 Aug 2016
  78. 78.
    OpenEye Scientific Software, 9 Bisbee Court, Suite D, Santa Fe, NM 87508. Accessed 28 Feb 2013
  79. 79.
    SMARTS Theory Manual. Daylight Chemical Information Systems.
  80. 80.
  81. 81.
    Weininger D (1988) SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Comp Sci 28:31–36CrossRefGoogle Scholar
  82. 82.
    Weininger D, Weininger A, Weininger JL (1989) SMILES. 2. Algorithm for generation of unique SMILES notation. J Chem Inf Comp Sci 29:97–101CrossRefGoogle Scholar
  83. 83.
    OMEGA. OpenEye Scientific Software
  84. 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 50:572–584CrossRefGoogle Scholar
  85. 85.
    Halgren TA (1999) MMFF VI. MMFF94S option for energy minimization studies. J Comp Chem 20:720–729CrossRefGoogle Scholar
  86. 86.
    SZYBKI. OpenEye Scientific Software
  87. 87.
    Bondi A (1964) van der Waals volumes and radii. J Phys Chem 68:441–451CrossRefGoogle Scholar
  88. 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., WallingfordGoogle Scholar
  89. 89.
    Szabo A, Ostlund NS (1996) Modern quantum chemistry. Introduction to advanced electronic structure theory. Dover, MineolaGoogle Scholar
  90. 90.
    Becke AD (1993) Density-functional thermochemistry. III. The role of exact exchange. J Chem Phys 98:5648–5652CrossRefGoogle Scholar
  91. 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 3:785–789CrossRefGoogle Scholar
  92. 92.
    Møller C, Plesset MS (1934) Note on the approximation treatment for many-electron systems. Phys Rev 46:618–622.CrossRefGoogle Scholar
  93. 93.
    Frisch MJ, Head-Gordon M, Pople JA (1990) A direct MP2 gradient method. Chem Phys Lett 166:275–280CrossRefGoogle Scholar
  94. 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 54:724–728CrossRefGoogle Scholar
  95. 95.
    Frisch MJ, Pople JA, Binkley JS (1984) Self-consistent molecular orbital methods. 25. Supplementary functions for Gaussian basis sets. J Chem Phys 80:3265–3269CrossRefGoogle Scholar
  96. 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 3:363–371CrossRefGoogle Scholar
  97. 97.
    Hansch C, Leo A, Hoekman D (1995) Exploring QSAR. American Chemical Society, Washington DCGoogle Scholar
  98. 98.
    Kenny PW, Montanari CA, Prokopczyk IM, Sala FA, Sartori GR (2013) Automated molecule editing in molecular design. J Comput Aided Mol Des 27:655–664CrossRefGoogle Scholar
  99. 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 23:271–285Google Scholar
  100. 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 49:6672–6682CrossRefGoogle Scholar
  101. 101.
    Hussain J, Rea C (2010) Computationally efficient algorithm to identify matched molecular pairs (MMPs) in large data sets. J Chem Inf Model 50:339–348CrossRefGoogle Scholar
  102. 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 52:1138–1145CrossRefGoogle Scholar
  103. 103.
    Dossetter AG, Griffen EJ, Leach AG (2013) Matched molecular pair analysis in drug discovery. Drug Discov Today 18:724–731CrossRefGoogle Scholar
  104. 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 57:3786–3802CrossRefGoogle Scholar
  105. 105.
    JMP version 12.0, SAS Institute, Cary, NC 27513.
  106. 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 6:1787–1797CrossRefGoogle Scholar
  107. 107.
    Mobley DL, Baker JR, Barber AE, Fennell CJ, Dill KA (2008) Charge asymmetries in hydration of polar solutes. J Phys Chem B 112:2405–2414CrossRefGoogle Scholar
  108. 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 116:9776–9783CrossRefGoogle Scholar
  109. 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 120:8485–8517CrossRefGoogle Scholar
  110. 110.
    Ritchie TJ, Macdonald SJF (2014) Physicochemical descriptors of aromatic character and their use in drug discovery. J Med Chem 57:5206–5215CrossRefGoogle Scholar
  111. 111.
    Adler TK, Albert A (1960) Diazaindenes (“azaindoles”). Part I. Ionization constants and spectra. J Chem Soc 1960:1794–1797CrossRefGoogle Scholar
  112. 112.
    Topliss JG (1972) Utilization of operational schemes for analog synthesis in drug design. J Med Chem 15:1006–1011CrossRefGoogle Scholar
  113. 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 58:2390–2405CrossRefGoogle Scholar
  114. 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 7:743–750CrossRefGoogle Scholar
  115. 115.
    Edwards JO, Pearson RG (1962) The factors determining nucleophilic reactivities. J Am Chem Soc 84:16–24CrossRefGoogle Scholar
  116. 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 112:2008–2010CrossRefGoogle Scholar
  117. 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 41:856–864CrossRefGoogle Scholar
  118. 118.
    Johnson ME, Malardier-Jugroot C, Murarka RK, Head-Gordon T (2009) Hydration water dynamics near biological interfaces. J Phys Chem B 113:4082–4092CrossRefGoogle Scholar
  119. 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 19:4622–4625CrossRefGoogle Scholar
  120. 120.
    Murray CW, Rees DC (2016) Opportunity knocks: organic chemistry for fragment-based drug discovery (FBDD). Angew Chem Int Ed 55:488–492CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Nádia Melo Borges
    • 1
  • Peter W. Kenny
    • 1
    Email author
  • Carlos A. Montanari
    • 1
  • Igor M. Prokopczyk
    • 1
  • Jean F. R. Ribeiro
    • 1
  • Josmar R. Rocha
    • 1
  • Geraldo Rodrigues Sartori
    • 1
  1. 1.Grupo de Estudos em Química Medicinal – NEQUIMEDInstituto de Química de São Carlos – Universidade de São PauloSão CarlosBrazil

Personalised recommendations