Binary Classification of Blood-Brain Barrier Penetration by the Logistic Regression Method

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O.A. Raevsky
D.E. Polianczyk
O.E. Raevskaja


Stable classification predictive models of 83 drugs with different blood-brain barrier penetration capacity have been constructed by the logistic regression method using physicochemical descriptors characterizing steric, electrostatic interactions and hydrogen bond energy. The models are balanced, with the prediction level of 75-80%.

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Raevsky, O., Polianczyk, D., & Raevskaja, O. (2018). Binary Classification of Blood-Brain Barrier Penetration by the Logistic Regression Method. Biomedical Chemistry: Research and Methods, 1(3), e00065.


  1. 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
  2. Bradbury, M. W. B. (1979). The concept of a blood-brain barrier. John Wiley & Sons.
  3. Young, R.C.; Mitchell, R.C.; Brown, T.H.; Ganellin, C.R.; Griffiths, R.; Jones, M.; Rana, K.K.; Saunders, D.; Smith, I.R.; Sore, N.E.; Wilks, T.J. (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
  4. van de Waterbeemd, H.D.; Kansy, M. (1992). Hydrogen-bonding capacity and brain penetration. Chimia, 46(7-8), 299-303.
  5. Kelder, J.; Grootenhuis, P.D.J.; Bayada, D.M.; Delbressine, L.P.; Ploemen, J.P. (1999). Polar molecular surface as a dominating determinant for oral absorption and brain penetration of drugs. Pharmaceutical Research, 16(10), 1514-1519. DOI
  6. Gleeson, M.P. (2008). Generation of a set of simple, interpretable ADMET rules of thumb.Journal of Medicinal Chemistry, 51(4), 817-834. DOI
  7. Waring, M.J. (2009). Defining optimum lipophilicity and molecular weight ranges for drug candidates-Molecular weight dependent lower logD limits based on permeability.Bioorganic & Medicinal Chemistry Letters, 19(10), 2844-2851. DOI
  8. 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 Review, 23(1-3), 3-26. DOI
  9. Wager, T.T.;Hou, X.; Verhoest, P.R.; Villalobos, A. (2010).Moving beyond rules: the development of a central nervous system multiparameter optimization (CNS MPO) approach to enable alignment of druglike properties. ACS Chemical Neuroscience, 1(6), 435-439. DOI
  10. Rankovic, Z. (2015). CNS drug design: balancing physicochemical properties for optimal brain exposure.Journal of Medicinal Chemistry, 58(6), 2584-2608. DOI
  11. Rankovic, Z. (2017). CNS physicochemical property space shaped by a diverse set of molecules with experimentally determined exposure in the mouse brain. Journal of Medicinal Chemistry, 60(14), 5943-5954. DOI
  12. van de Waterbeemd, H.D.; Camenisch, G.; Folkers, G.; Raevsky, O.A. (1996). Estimation of CACO-2 cell permeability using calculated molecular descriptors. Quantitative Structure-Activity Relationships, 15(6), 480-490. DOI
  13. van de Waterbeemd, H.D.; 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
  14. 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
  15. 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
  16. 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
  17. 17. Raevsky, O.A. (2018). Hydrogen Bond Contribution to Drug Bioavailability: cheminformatics approach. Biomedical Chemistry: Research and Methods, 1(3), e00060. DOI
  18. Raevsky, O.A.; Solodova, S.L.; Lagunin, A.A.; Poroikov, V.V. (2014). Computer Modeling of Blood-Brain Barrier Permeability for Physiologically Active Compounds. Biochemistry (Moscow) Supplement Series B: Biomedical Chemistry, 60(2), 161-181. DOI
  19. Ooms, F.; Weber, P.; Carrupt, P.-A.; Testa, B. (2002). A simple model to predict blood–brain barrier permeation from 3D molecular fields. Biochimica et Biophysica Acta, 1587(2-3), 118-125. DOI
  20. Raevskij, O.A. (2015). Modelirovanie sootnoshenij “struktura-svojstva”, Dobrosvet, M.
  21. Singh, N.; Chaudhury, S.; Liu, R.; AbdulHameed, M.D.M.; Tawa, G.; Wallqvist, A. (2012). QSAR Classification Model for Antibacterial Compounds and Its Use in Virtual Screening.Journal of Chemical Information and Modeling, 52(10), 2559-2569. DOI
  22. Riniker, S.; Wang, Y.; Jenkins, J.L.; Landrum, G.A. (2014). Using Information from Historical High-Throughput Screens to Predict Active Compounds. Journal of Chemical Information and Modeling, 54(7), 1880-1891. DOI
  23. Iwata, H.; Sawada,R.; Mizutani, S.; Yamanishi, Y. (2015). Systematic Drug Repositioning for a Wide Range of Diseases with Integrative Analyses of Phenotypic and Molecular Data. Journal of Chemical Information and Modeling, 55(2), 446-459. DOI
  24. Yee, L.C.; Wei, Y.C. (2012). Statistical Modelling of Molecular Descriptors in QSAR/QSPR, Vol. 2 (Eds: Dehmer, M.; Varmuza, K.; Bonchev, D.), Wiley-VCH, Verlag GmbH & Co. KGaA., 1-31.
  25. SPSS Inc. Released 2008. SPSS Statistics for Windows, Version 17.0. Chicago: SPSS Inc.
  26. DRAGON, version 5.5; Talete srl: Milano, Italy (2011).
  27. Raevsky, O.A.; Grigor’ev, V.Y.; Trepalin, S.V. HYBOT program, registration by Russian State Patent Agency No. 990090 of 26.02.99