Biomedical Chemistry: Research and Methods, 2018, 1(3), e00060
The 40th Anniversary of the Institute of Physiologically Active Compounds of the Russian Academy of Sciences

Hydrogen Bond Contribution to Drug Bioavailability: cheminformatics approach

O.A. Raevsky

Institute of Physiologically Active Compounds of the Russian Academy of Sciences,
1 Severny proezd, Moscow region, Chernogolovka, 142432 Russia;*e-mail:

Key words: QSAR; HYBOT; hydrogen bond descriptors; bioavailability

DOI: 10.18097/BMCRM00060

The whole version of this paper is available in Russian.

A review, based mainly on own publications, is devoted to methods of investigation of “structure-bioavailability” relationships. The first part of this review contains information about classification of hydrogen bond descriptors, original 2D hydrogen bond thermodynamic descriptors, program HYBOT, original 3D hydrogen bonding potentials, original hydrogen bond surface area descriptors. The second part includes the results of applications of the above mentioned of hydrogen bond descriptors for prediction of bioavailability components such as lipophilicity, solubility in water and in physiological fluids, absorption and blood brain barrier permeability.

Figure 1. Hierarchy of the information level of various hydrogen bond descriptors [10].

Figure 2. TUnified scale of donor and acceptor factors of hydrogen bond (example for simple organic compounds).

Figure 3. Hydrogen bond potentials. The dependence of optimal energy from bond length.

Figure 4. The dependences of the fractions absorption (FA) from surface's HYBOT hydrogen bonds descriptors (OFEASA + OFEDSA).

Table 1. The simple and consensus AMP and LoReP models of solubility in water of 2,615 crystalline compounds.

Table 2. The coefficients of equation (19) and their statistical characteristics [60].

Table 3. Protocol of "CNS/not-CNS" classification of compounds by intuitive approaches and statistical methods. TP-correct recognition of "CNS", FN-incorrect recognition of "CNS", TN-correct recognition of "not-CNS", EP-misdiagnosis, "not CNS", SE-sensitivity (TP / TP + EN), SP- (TN / TN ++ EP), ACC- accuracy (TP + TN) / (TP + EN + TN + EP).


The work was performed within the framework of the state task for 2018 (the topic number 0090-2017-0020).


  1. Pimentel, G. C., & McClellan, A. L. (1960). The hydrogen bond. Freeman: San Francisco, CA.
  2. Moore, T. S., & Winmill, T. F. (1912). CLXXVII.—The state of amines in aqueous solution. Journal of the Chemical Society, Transactions, 101, 1635-1676. DOI
  3. Latimer, W. M., & Rodebush, W. H. (1920). Polarity and ionization from the standpoint of the Lewis theory of valence. Journal of the American Chemical Society, 42(7), 1419-1433. DOI
  4. Varfolomeev, S. D., & Pozhitkov, A. E. (2000). Active centers of hydrolases: the main types of structures and the mechanism of catalysis. Vestnik Moskovskogo universiteta [Bulletin of Moscow University]. Part 2. Chemistry, 41(3), 147-156.
  5. Pauling, L., & Pauling, P. (1978). Chemistry [Russian translation]. Mir, Moscow, 299.
  6. Kubinyi H. (2001) Hydrogen bonding, the last mystery in drug design? In: Pharmacokinetic. Optimization in Drug Research (B.Testa , H.Van de Waterbeemd, G.Folkers R.Guy, eds.) Wiley - VCH, Weinheim and VHCA, Zurich, pp.513 - 524.
  7. Hamelink, J., Landrum, P. F., Bergman, H., & Benson, W. H. (1994). Bioavailability: physical, chemical, and biological interactions. CRC Press.
  8. U.S. Code of Federal Regulations. 21 CFR 320.1.
  9. Devillers, J., & Balaban, A. T. (Eds.). (2000). Topological indices and related descriptors in QSAR and QSPAR. CRC Press.
  10. Raevskij, O.A. (2015). Modelirovanie sootnoshenij “struktura-svojstva”, Dobrosvet, M. (in Russian)
  11. Raevskii, O. A., Avidon, V. V., & Novikov, V. P. (1982). Use of a unified scale of donor-acceptor interactions for the analysis of the similarity of the structures of biologically active compounds. Pharmaceutical Chemistry Journal, 16(8), 633-636. DOI
  12. Raevskii, O. A., & Novikov, V. P. (1982). Unification of the characteristics of donor-acceptor interactions in the framework of the problem of the structure-activity relationship. Khim.-Farm. Zh., 16(5), 583-586.
  13. Raevsky, O. A., Grigor'ev, V. Y., & Solov'ev, V. P. (1989). The Estimation of Donor-Acceptor Parameters in Biologically Active Compounds. Khim. Pharm. Zhurn.(Rus.), 23, 1294-1300.
  14. Martynov, I. V., & Raevsky, O. A. (1983). Physical-chemical approach to purposeful search of biological active substances. Vestnik akademii nauk SSSR, (7), 93-101.
  15. Martynov, I. V., & Raevskii, O. A. (1983). Estimation of electron-donor and acceptor ability of some active-centers in molecules of physiologically active compounds. Zhurnal vsesoyuznogo khimicheskogo obshchestva imeni di mendeleeva, 28(6), 716-717.
  16. Raevskii, O. A., Grigor'ev, V. Y., & Solov'ev, V. P. (1984). Evaluation of the electron-donor and electron-acceptor functions of ionized atoms and groups in biologically active substances on the basis of thermodynamic data. Pharmaceutical Chemistry Journal, 18(5), 327-331.
  17. Sapegin, A. M., Raevsky, O., Chistyakov, V. V., & Martynov, I. V. (1987). Donor-acceptor factor-analysis algorithm to depict molecules of biologically-active compounds. Khimiko-farmatsevticheskii zhurnal, 21(9), 1098-1102.
  18. Raevsky, O., & Sapegin, A. M. (1987). Developed physicochemical approach to recognition of physiologically active compound structures. Khimiko-farmatsevticheskii zhurnal, 21(11), 1338-1341.
  19. Sapegin, A. M., Razdolsky, A., Chistyakov, V. V., & Raevsky, O. (1987). structure recognition-realization of a physicochemical approach to the study of structure-activity-relationships. Khimiko-farmatsevticheskii zhurnal, 21(11), 1341-1344.
  20. Raevsky, O. A., Solotnov, A. F., & Solovyev, V. P. (1987). Electron-donating and electrophilic functions of физиологически physiological acting and model compounds. Journal of common chemistry, 57(6), 1241-1248.
  21. Raevskii, O. A., Grigoriev, V., Soloviev, V., & Martynov, I. V. (1988). Electron-acceptor enthalpy factors of phenols. Doklady Akademii Nauk SSSR, 298(5), 1166-1169.
  22. Raevsky, O. A., Grigor'ev, V. Y., Kireev, D. B., & Zefirov, N. S. (1992). Complete thermodynamic description of H‐bonding in the framework of multiplicative approach. Quantitative Structure‐Activity Relationships, 11(1), 49-63. DOI
  23. Raevsky, O. A. (1997). Quantification of non‐covalent interactions on the basis of the thermodynamic hydrogen bond parameters. Journal of physical organic chemistry, 10(5), 405-413. DOI
  24. Raevsky, O. A. (1997). Hydrogen bond strength estimation by means of the HYBOT program package. Computer‐Assisted Lead Finding and Optimization: Current Tools for Medicinal Chemistry, 367-378.
  25. Abraham, M. H., Ibrahim, A., Zissimos, A. M., Zhao, Y. H., Comer, J., & Reynolds, D. P. (2002). Application of hydrogen bonding calculations in property based drug design. Drug Discovery Today, 7(20), 1056-1063. DOI
  26. Ruelle, P. (1999). Towards a comprehensive non-ergodic treatment of H-bonds and hydrophobicity in real solutions: The mobile order and disorder theory. Perspectives in Drug Discovery and Design, 17(1), 61-96.
  27. Laurence, C., Brameld, K. A., Graton, J., Le Questel, J. Y., & Renault, E. (2009). The p K BHX database: toward a better understanding of hydrogen-bond basicity for medicinal chemists. Journal of medicinal chemistry, 52(14), 4073-4086. DOI
  28. Raevsky, O. A., Grigor’ev, V. J., & Trepalin, S. V. (1999). HYBOT program package. Registration by Russian State Patent Agency, (990090).
  29. Trepalin, S.V., Yarkov, A.V. (2001). CheD: Chemical Database Compilation Tool, Internet Server, and client for SQL Servers. J. Chem. Inf. Comput. Sci., 41, 100-107. DOI
  30. Verma, J., Khedkar, V. M., & Coutinho, E. C. (2010). 3D-QSAR in drug design-a review. Current topics in medicinal chemistry, 10(1), 95-115. DOI
  31. SYBYL-Х 2.1. Certara, Princeton, NJ, USA
  32. Pearlman, D. A., Case, D. A., Caldwell, J. W., Seibel, G. L., Singh, U. C., Weiner, P., & Kollman, P. A. (1991). AMBER 4.0, University of California, San Francisco.
  33. Boobbyer, D. N., Goodford, P. J., McWhinnie, P. M., & Wade, R. C. (1989). New hydrogen-bond potentials for use in determining energetically favorable binding sites on molecules of known structure. Journal of medicinal chemistry, 32(5), 1083-1094. DOI
  34. Groom, C. R., Bruno, I. J., Lightfoot, M. P., & Ward, S. C. (2016). The Cambridge structural database. Acta Crystallographica Section B: Structural Science, Crystal Engineering and Materials, 72(2), 171-179. DOI
  35. Raevsky, O. A., & Skvortsov, V. S. 3D HYBOT programm. Russian State Patent Department, (004612207).
  36. Trepalin, S. V., Yarkov, A. V., & Raevsky, O. A. (2018). MOLTRA-II. New three dimensional descriptors of the hydrogen bond. Biomedical Chemistry: Research and Methods, 1(3), e00069. DOI
  37. Van de Waterbeemd, H., & Testa, B. (1987). The parametrization of lipophilicity and other structural properties in drug design. Advances in drug research, 16, 85-225.
  38. Raevsky, O. A., Schaper, K. J., & Seydel, J. K. (1995). H‐Bond Contribution to Octanol‐Water Partition Coefficients of Polar Compounds. Quantitative Structure‐Activity Relationships, 14(5), 433-436. DOI
  39. Raevsky O.A., Trepalina E.P., Trepalin S.V.(2000) in Molecular Modelling and Prediction of Bioactivity (Gundertofe K. and Jorgensen F.eds.), Kluwer Academic/Plenum Publ.; p. 489-490.
  40. Raevsky, O. A. (2001). Molecular lipophilicity calculations of chemically heterogeneous chemicals and drugs on the basis of structural similarity and physicochemical parameters. SAR and QSAR in Environmental Research, 12(4), 367-381. DOI
  41. Raevsky, O. A., Trepalin, S. V., Trepalina, H. P., Gerasimenko, V. A., & Raevskaja, O. E. (2002). 41 SLIPPER-2001− Software for predicting molecular properties on the basis of physicochemical descriptors and structural similarity. Journal of chemical information and computer sciences, 42(3), 540-549. DOI
  42. Lipinski, C. A. (2000). Drug-like properties and the causes of poor solubility and poor permeability. Journal of pharmacological and toxicological methods, 44(1), 235-249. DOI
  43. Alelyunas, Y. W., Empfield, J. R., McCarthy, D., Spreen, R. C., Bui, K., Pelosi-Kilby, L., & Shen, C. (2010). Experimental solubility profiling of marketed CNS drugs, exploring solubility limit of CNS discovery candidate. Bioorganic & medicinal chemistry letters, 20(24), 7312-7316. DOI
  44. Hansch, C., Quinlan, J. E., & Lawrence, G. L. (1968). Linear free-energy relationship between partition coefficients and the aqueous solubility of organic liquids. The Journal of Organic Chemistry, 33(1), 347-350. DOI
  45. Schaper, K. J., Kunz, B., & Raevsky, O. A. (2003). Analysis of water solubility data on the basis of HYBOT descriptors: Part 2. Solubility of liquid chemicals and drugs. QSAR & Combinatorial Science, 22(9‐10), 943-958. DOI
  46. Dearden, J. C. (2006). In silico prediction of aqueous solubility. Expert opinion on drug discovery, 1(1), 31-52. DOI
  47. Faller, B., & Ertl, P. (2007). Computational approaches to determine drug solubility. Advanced drug delivery reviews, 59(7), 533-545. DOI
  48. Johnson, S. R., & Zheng, W. (2006). Recent progress in the computational prediction of aqueous solubility and absorption. The AAPS journal, 8(1), E27-E40. DOI
  49. Sugano, K., Okazaki, A., Sugimoto, S., Tavornvipas, S., & Omura, A. (2007). Solubility and dissolution profile assessment in drug discovery. Drug metabolism and pharmacokinetics, 22(4), 225-254. DOI
  50. Wang, J., & Hou, T. (2011). Recent advances on aqueous solubility prediction. Combinatorial chemistry & high throughput screening, 14(5), 328-338.
  51. Skyner, R. E., McDonagh, J. L., Groom, C. R., Van Mourik, T., & Mitchell, J. B. O. (2015). A review of methods for the calculation of solution free energies and the modelling of systems in solution. Physical Chemistry Chemical Physics, 17(9), 6174-6191. DOI
  52. Chevillard, F., Lagorce, D., Reynès, C., Villoutreix, B. O., Vayer, P., & Miteva, M. A. (2012). In silico prediction of aqueous solubility: a multimodel protocol based on chemical similarity. Molecular pharmaceutics, 9(11), 3127-3135. DOI
  53. Yalkowsky, S. H., & Valvani, S. C. (1980). Solubility and partitioning I: solubility of nonelectrolytes in water. Journal of pharmaceutical sciences, 69(8), 912-922. DOI
  54. Raevsky, O. A., Polianczyk, D. E., Grigorev, V. Y., Raevskaja, O. E., & Dearden, J. C. (2015). In silico prediction of aqueous solubility: A comparative study of local and global predictive models. Molecular informatics, 34(6‐7), 417-430. DOI
  55. Raevsky, O. A., Grigor’ev, V. Y., Polianczyk, D. E., Raevskaja, O. E., & Dearden, J. C. (2014). Calculation of aqueous solubility of crystalline un-ionized organic chemicals and drugs based on structural similarity and physicochemical descriptors. Journal of chemical information and modeling, 54(2), 683-691. DOI
  56. DRAGON, version 5.5;Talete srl, Milano, Italy, 2011.
  57. Obrezanova, O., Csányi, G., Gola, J. M., & Segall, M. D. (2007). Gaussian processes: a method for automatic QSAR modeling of ADME properties. Journal of chemical information and modeling, 47(5), 1847-1857. DOI
  58. Obrezanova, O., Gola, J. M., Champness, E. J., & Segall, M. D. (2008). Automatic QSAR modeling of ADME properties: blood–brain barrier penetration and aqueous solubility. Journal of computer-aided molecular design, 22(6-7), 431-440. DOI
  59. Raevsky, O. A., Grigorev, V. Y., Polianczyk, D. E., Raevskaja, O. E., & Dearden, J. C. (2017). Six global and local QSPR models of aqueous solubility at pH= 7.4 based on structural similarity and physicochemical descriptors. SAR and QSAR in Environmental Research, 28(8), 661-676. DOI
  60. Raevsky, O. A., Fetisov, V. I., Trepalina, E. P., McFarland, J. W., & Schaper, K. J. (2000). Quantitative estimation of drug absorption in humans for passively transported compounds on the basis of their physico‐chemical parameters. Quantitative Structure‐Activity Relationships, 19(4), 366-374. DOI
  61. Raevsky, O. A., & Skvortsov, V. S. (2005). Quantifying hydrogen bonding in QSAR and molecular modeling. SAR and QSAR in Environmental Research, 16(3), 287-300. DOI
  62. Raevsky, O. A., Schaper, K. J., Artursson, P., & McFarland, J. W. (2001). A novel approach for prediction of intestinal absorption of drugs in humans based on hydrogen bond descriptors and structural similarity. Quantitative Structure‐Activity Relationships, 20(5‐6), 402-413. DOI
  63. Bradbury, M. W. B. (1979). The concept of a blood-brain barrier. John Wiley & Sons.
  64. Wolf, S., Seehaus, B., Minol, K., & Gassen, H. G. (1996). The blood-brain barrier: a specialty of cerebral microcirculation systems. Die Naturwissenschaften, 83(7), 302-311.
  65. Pauletti, G. M., Okumu, F. W., & Borchardt, R. T. (1997). Effect of size and charge on the passive diffusion of peptides across Caco-2 cell monolayers via the paracellular pathway. Pharmaceutical research, 14(2), 164-168. DOI
  66. Hansch, C., Steward, A. R., Anderson, S. M., & Bentley, D. L. (1968). Parabolic dependence of drug action upon lipophilic character as revealed by a study of hypnotics. Journal of medicinal chemistry, 11(1), 1-11. DOI
  67. Young, R. C., Mitchell, R. C., Brown, T. H., Ganellin, C. R., Griffiths, R., Jones, M., ... & Smith, I. R. (1988). Development of a new physicochemical model for brain penetration and its application to the design of centrally acting H2 receptor histamine antagonists. Journal of medicinal chemistry, 31(3), 656-671. DOI
  68. van de Waterbeemd, H., & Kansy, M. (1992). Hydrogen-bonding capacity and brain penetration. CHIMIA International Journal for Chemistry, 46(7-8), 299-303.
  69. Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (1997). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced drug delivery reviews, 23(1-3), 3-25. DOI
  70. Norinder, U., & Haeberlein, M. (2002). Computational approaches to the prediction of the blood–brain distribution. Advanced drug delivery reviews, 54(3), 291-313. DOI
  71. Didziapetris, R., Japertas, P., Avdeef, A., & Petrauskas, A. (2003). Classification analysis of P-glycoprotein substrate specificity. Journal of drug targeting, 11(7), 391-406. DOI
  72. Pajouhesh, H., & Lenz, G. R. (2005). Medicinal chemical properties of successful central nervous system drugs. NeuroRx, 2(4), 541-553. DOI
  73. Borchardt, R., Kerns, E., Hageman, M., Thakker, D., & Stevens, J. (Eds.). (2007). Optimizing the" drug-like" Properties of Leads in Drug Discovery. Springer Science & Business Media.
  74. Wager, T. T., Chandrasekaran, R. Y., Hou, X., Troutman, M. D., Verhoest, P. R., Villalobos, A., & Will, Y. (2010). Defining desirable central nervous system drug space through the alignment of molecular properties, in vitro ADME, and safety attributes. ACS chemical neuroscience, 1(6), 420-434. DOI
  75. Ghose, A. K., Herbertz, T., Hudkins, R. L., Dorsey, B. D., & Mallamo, J. P. (2011). Knowledge-based, central nervous system (CNS) lead selection and lead optimization for CNS drug discovery. ACS chemical neuroscience, 3(1), 50-68. DOI
  76. Desai, P. V., Sawada, G. A., Watson, I. A., & Raub, T. J. (2013). Integration of in silico and in vitro tools for scaffold optimization during drug discovery: predicting P-glycoprotein efflux. Molecular pharmaceutics, 10(4), 1249-1261. DOI
  77. Panarin, V. A., Kondratyev, V. A., & Rayevsky, O. A. (1990). Some characteristics of the functioning of membrane receptor‐channel complexes of Limnaea stagnalis neurones. The Journal of physiology, 423(1), 363-380. DOI
  78. Hitchcock, S. A., & Pennington, L. D. (2006). Structure− brain exposure relationships. Journal of medicinal chemistry, 49(26), 7559-7583. DOI
  79. Raevsky, O. A. (1990). The structure and properties of complexes simulating molecular recognition. Uspekhi Khimii, 59(3), 375-400.
  80. Raevsky, O. A. (2004). Physicochemical descriptors in property-based drug design. Mini reviews in medicinal chemistry, 4(10), 1041-1052. DOI
  81. van de Waterbeemd, H., Camenisch, G., Folkers, G., Chretien, J. R., & Raevsky, O. A. (1998). Estimation of blood-brain barrier crossing of drugs using molecular size and shape, and H-bonding descriptors. Journal of drug targeting, 6(2), 151-165. DOI
  82. Raevsky, O. A., & Schaper, K. J. (1998). Quantitative estimation of hydrogen bond contribution to permeability and absorption processes of some chemicals and drugs. European journal of medicinal chemistry, 33(10), 799-807. DOI
  83. Raevsky, O. A., Solodova, S. L., Raevskaya, O. E., Liplavskiy, Y. V., & Mannhold, R. (2012). The computer classification models on the relationship between chemical structures of compounds and drugs with their blood brain barrier penetration ability. Biochemistry (Moscow) Supplement Series B: Biomedical Chemistry, 6(1), 31-38. DOI
  84. Raevsky, O. A., Solodova, S. L., Raevskaya, O. E., & Mannhold, R. (2012). Quantitative interaction between the structures of organic compounds and their abilities to penetrate the blood-brain barrier. Pharmaceutical Chemistry Journal, 46(3), 133-138. DOI
  85. Rankovic, Z. (2015). CNS drug design: balancing physicochemical properties for optimal brain exposure. Journal of medicinal chemistry, 58(6), 2584-2608. DOI
  86. Raevsky, O. A. (2016). CNS Multiparameter Optimization Approach: Is it in Accordance with Occam’s Razor Principle?. Molecular informatics, 35(3‐4), 94-98. DOI
  87. Raevsky, O. A., Polianczyk, D. E., Mukhametov, A., & Grigorev, V. Y. (2016). Assessment of the classification abilities of the CNS multi-parametric optimization approach by the method of logistic regression. SAR and QSAR in Environmental Research, 27(8), 629-635. DOI
  88. Raevsky, O. A., Grigorev, V. Y., Polianczyk, D. E., Sandakov, G. I., Solodova, S. L., Yarkov, A. V., Bachurin, S.O. & Dearden, J. C. (2016). Physicochemical property profile for brain permeability: comparative study by different approaches. Journal of drug targeting, 24(7), 655-662. DOI