Six global and local QSPR models of aqueous solubility at pH = 7.4 based on structural similarity and physicochemical descriptors

References

• G.L. Amidon, H. Lennernas, V.P. Shah, and J.R. Crison, A theoretical basis for a biopharmaceutic drug classification: The correlation of in vitro drug product dissolution and in vivo bioavailability, Pharm. Res. 12 (1995), pp. 413–420.
   PubMed Web of Science ®Google Scholar
• M.-L. Chen, G.L. Amidon, L.Z. Benet, H. Lennernas, and L.X. Yu, The BCS, BDDCS, and regulatory guidances, Pharm. Res. 28 (2011), pp. 1774–1778.
   PubMed Web of Science ®Google Scholar
• S.H. Yalkowsky and S. Banerjee, Aqueous Solubility: Methods of Estimation for Organic Compounds, Marcel Dekker, New York, 1992.
   Google Scholar
• K.V. Balakin, N.P. Savchuk, and I.V. Tetko, In silico approaches to prediction of aqueous and DMSO solubility of drug-like compounds: Trends, problems and solutions, Current Med. Chem. 13 (2006), pp. 223–241.
   PubMed Web of Science ®Google Scholar
• W.L. Jorgensen and E.M. Duffy, Prediction of drug solubility from structure, Adv. Drug Delivery Rev. 54 (2002), pp. 355–366.
   PubMed Web of Science ®Google Scholar
• A. Llinas, R.C. Glen, and J.M. Goodman, Solubility challenge: Can you predict solubilities of 32 molecules using a database of 100 reliable measurements? J. Chem. Inf. Model. 48 (2008), pp. 1289–1303.
   PubMed Web of Science ®Google Scholar
• J.C. Dearden, In silico prediction of aqueous solubility, Expert Opin. Drug Discov. 1 (2006), pp. 31–52.
   PubMed Web of Science ®Google Scholar
• P.R. Duchowicz and E.A. Castro, QSPR studies on aqueous solubility of drug-like compounds, Int. J. Mol. Sci. 10 (2009), pp. 2558–2577.
   PubMed Web of Science ®Google Scholar
• J.C. Dearden, P. Rotureau, and G. Fayet, QSPR prediction of physicochemical properties for REACH, SAR QSAR Environ. Res. 24 (2013), pp. 279–318.
   PubMed Web of Science ®Google Scholar
• K.H. Grime, P. Barton, and D.F. McGinnity, Application of in silico, in vitro and preclinical pharmacokinetic data for the effective and efficient prediction of human pharmacokinetics, Mol. Pharmaceutics 10 (2013), pp. 1191–1206.
   PubMed Web of Science ®Google Scholar
• J. Wichard and R. Kühne, Predicting Aqueous Solubility from Structure, J. Univ. Appl. Sci. Mittweida. Proceedings of the 20th IWKM, 28–29 October 2009.
   Google Scholar
• M. Salahinejad, T.C. Le, and D.A. Winkler, Aqueous solubility prediction: Do crystal lattice interactions help? Mol. Pharm. 10 (2013), pp. 2757–2766.
   PubMed Web of Science ®Google Scholar
• O.A. Raevsky, D.E. Polianczyk, V.Y. Grigorev, O.E. Raevskaja, and J.C. Dearden, In silico prediction of aqueous solubility: A comparative study of local and global predictive models, Mol. Inform. 34 (2015), pp. 417–430.
   PubMed Web of Science ®Google Scholar
• O.A. Raevsky, O.E. Raevskaja, and K.J. Schaper, Analysis of water solubility data on the basis of HYBOT descriptors. Part 3. Solubility of solid neutral chemicals and drugs, QSAR Comb. Sci. 23 (2004), pp. 327–343.
   Google Scholar
• K.J. Schaper, B. Kunza, and O.A. Raevsky, Analysis of water solubility data on the basis of HYBOT descriptors. Part 2. Solubility of liquid chemicals and drugs, QSAR Comb. Sci. 22 (2003), pp. 943–958.
   Google Scholar
• O.A. Raevsky, V.Y. Grigor’ev, D.E. Polianczyk, O.E. Raevskaja, and J.C. Dearden, Calculation of aqueous solubility of crystalline un-ionized organic chemicals and drugs based on structural similarity and physicochemical descriptors, J. Chem. Inf. Model. 54 (2014), pp. 683–691.
   PubMed Web of Science ®Google Scholar
• F. Chevillard, D. Lagorce, C. Reynès, B.O. Villoutreix, P. Vayer, and M.A. Miteva, In silico prediction of aqueous solubility: A multimodel protocol based on chemical similarity, Mol. Pharm. 9 (2012), pp. 3127–3135.
   PubMed Web of Science ®Google Scholar
• O.A. Raevsky, S.V. Trepalin, H.P. Trepalina, V.A. Gerasimenko, and O.E. Raevskaja, SLIPPER-2001 – Software for predicting molecular properties on the basis of physicochemical descriptors and structural similarity, J. Chem. Inf. Comput. Sci. 42 (2002), pp. 540–549.
   PubMedGoogle Scholar
• N.T. Hansen, I. Kouskoumvekaki, F.S. Jørgensen, S. Brunak, and S.O. Jónsdóttir, Prediction of pH-dependent aqueous solubility of druglike molecules, J. Chem. Inf. Model. 46 (2006), pp. 2601–2609.
   PubMed Web of Science ®Google Scholar
• A.C. Lee and G.M. Crippen, Predicting pKa, J. Chem. Inf. Model. 49 (2009), pp. 2013–2033.
   PubMed Web of Science ®Google Scholar
• T. Cheng, Q. Li, Y. Wang, and S.H. Bryant, Binary classification of aqueous solubility using support vector machines with reduction and recombination feature selection, J. Chem. Inf. Model. 51 (2011), pp. 229–236.
   PubMed Web of Science ®Google Scholar
• O. Obrezanova, G. Csanyi, J.M.R. Gola, and M.D. Segall, Gaussian processes: A method for automatic QSAR modeling of ADME properties, J. Chem. Inf. Model. 47 (2007), pp. 1847–1857.
   PubMed Web of Science ®Google Scholar
• D.S. Palmer and J.B.O. Mitchell, Is experimental data quality the limiting factor in predicting the aqueous solubility of druglike molecules? Mol. Pharm. 11 (2014), pp. 2962–2972.
   PubMed Web of Science ®Google Scholar
• G.I. Poda, D.P. Landsittel, K. Brumbaugh, D.S. Sharp, H.F. Frasch, and E. Demchuk, Random sampling or ‘random’ model in skin flux measurements? [Commentary on “Investigation of the mechanism of flux across human skin in vitro by quantitative structure-permeability relationships”], Eur. J. Pharm. Sci. 14 (2001), pp. 197–200.
   PubMed Web of Science ®Google Scholar
• J.C. Dearden, M.T.D. Cronin, and K.L.E. Kaiser, How not to develop a quantitative structure–activity or structure–property relationship (QSAR/QSPR), SAR QSAR Environ. Res. 20 (2009), pp. 241–266.
   PubMed Web of Science ®Google Scholar
• G.L. Perlovich, A.M. Ryzhakov, N.N. Strakhova, V.P. Kazachenko, K.J. Schaper, and O.A. Raevsky, Thermodynamic aspects of solubility process of some sulfonamides, J. Pharm. Biomed. Anal. 54 (2011), pp. 222–224.
   PubMed Web of Science ®Google Scholar
• G.L. Perlovich, A.M. Ryzhakov, N.N. Strakhova, V.P. Kazachenko, K.J. Schaper, and O.A. Raevsky, Thermodynamic aspects of solubility, solvation and partitioning processes of some sulfonamides, J. Chem. Thermodyn. 43 (2011), pp. 683–689.
   Web of Science ®Google Scholar
• O. A. Raevsky, V. Ju. Grigorev, S. V. Trepalin, HYBOT program, registration by Russian State Patent Agency No. 990090 of 26.02.99.
   Google Scholar
• DRAGON, Version 5.5; Talete srl, Milano, Italy, 2011. http://www.talete.mi.it.
   Google Scholar
• Sybyl-X Molecular Modeling Software Packages, Version 2.0. TRIPOS Associates, Inc; St. Louis, MO, USA: 2012
   Google Scholar
• VolSurf+ manual and software available at: http://www.moldiscovery.com/docs/vsplus/.
   Google Scholar
• G.E. Forsythe, M.A. Malcolm, and C.B. Moler, Computer Methods for Mathematical Computations, Prentice Hall, New Jersey, 1977, pp. 227–235.
   Google Scholar
• L. Breiman and L. Cutler, RF5new; software available at http://www.stat.berkeley.edu/~breiman/RandomForests/cc_examples/prog.f.
   Google Scholar
• L. Breiman, RRforest; software available at: http://www.stat.berkeley.edu/~breiman/RandomForests/reg_examples/RFR.f.
   Google Scholar
• J. Colmenares, fortran-ls-svm; software available at: https://github.com/jbcolme/fortran-ls-svm.
   Google Scholar
• W.A. Warr, Some trends in chemoinformatics, Methods Mol. Biol. 672 (2011), pp. 1–37.
   PubMedGoogle Scholar
• S. Wold and M. Sjostrom, SIMCA: A method for analyzing chemical data in terms of similarity and analogy, in Chemometrics Theory and Application, B.R. Kowalski, ed., American Chemical Society Symposium Series 52, American Chemical Society, Washington, 1977, pp. 243–282.
   Google Scholar
• O.A. Raevsky, A.M. Sapegin, and N.S. Zefirov, Discriminant-regression model, in QSAR: Rational Approaches in the Design of Bioactive Compounds, C. Silipo and A. Vittoria, eds., Elsevier, Amsterdam, 1991, pp. 189–192.
   Google Scholar
• O.A. Raevsky, A.M. Sapegin, and N.S. Zefirov, The QSAR discriminant-regression model, QSAR 13 (1994), pp. 412–418.
   Web of Science ®Google Scholar
• R. Guha, D. Dutta, P.C. Jurs, and T. Chen, Local lazy regression: Making use of the neighborhood to improve QSAR predictions, J. Chem. Inf. Model. 46 (2006), pp. 1836–1847.
   PubMed Web of Science ®Google Scholar
• S. Zhang, A. Golbraikh, S. Oloff, H. Kohn, and A. Tropsha, A novel automated lazy learning QSAR (ALL-QSAR) approach: Method development, applications, and virtual screening of chemical databases using validated ALL-QSAR models, J. Chem. Inf. Model. 46 (2006), pp. 1984–1995.
   PubMed Web of Science ®Google Scholar
• A.P. Hill and R.J. Young, Getting physical in drug discovery: A contemporary perspective on solubility and hydrophobicity, Drug. Discov. Today 15 (2010), pp. 648–654.
   PubMed Web of Science ®Google Scholar
• P. Buchwald and N. Bodor, Soft quaternary anticholinergics: Comprehensive quantitative structure−activity relationship (QSAR) with a linearized biexponential (LinBiExp) model, J. Med. Chem. 49 (2006), pp. 883–891.
   PubMed Web of Science ®Google Scholar
• O.A. Raevsky, K.J. Schaper, H. van de Waterbeemd, and J.W. McFarland, Hydrogen bond contributions to properties and activity of chemicals and drugs, in Molecular Modeling and Prediction of Bioactivity, K. Gundertofte and F.S. Jorgensen, eds., Kluwer Academic/Plenum Publishers, New York, 2000, pp. 221–227.
   Google Scholar
• O.A. Raevsky, Molecular lipophilicity calculations of chemically heterogeneous chemicals and drugs on the basis of structural similarity and physicochemical parameters, SAR QSAR Environ. Res. 12 (2001), pp. 367–381.
   PubMed Web of Science ®Google Scholar
• O.A. Raevsky, V.J. Grigor’ev, E.A. Modina, and A.P. Worth, Prediction of acute toxicity to mice by the arithmetic mean toxicity (AMT) modelling approach, SAR QSAR Environ. Res. 21 (2010), pp. 265–275.
   PubMed Web of Science ®Google Scholar
• O.A. Raevsky, V.Y. Grigor’ev, E.A. Liplavskaya, and A.P. Worth, Prediction of acute rodent toxicity on the basis of chemical structure and physicochemical similarity, Mol. Inf. 30 (2011), pp. 267–275.
   PubMed Web of Science ®Google Scholar