A surface-integral model for log Pow

Journal of Chemical Information and Modeling

Volumn 50, Issue 3, 2010, Pages 429-436

  Kramer, Christian ^a,b   Beck, Bernd ^b   Clark, Timothy ^a,c  

^a UNIVERSITY OF ERLANGEN NUREMBERG   (Germany)

^b BOEHRINGER INGELHEIM PHARMA GMBH AND CO KG   (Germany)

^c UNIVERSITY OF PORTSMOUTH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATION THEORY; ELECTRON AFFINITY; IONIZATION POTENTIAL; MOLECULAR ORBITALS;

CONFORMATIONAL ENSEMBLE; IN-HOUSE VALIDATIONS; LOCAL HYDROPHOBICITY; LOCAL IONIZATION ENERGIES; MOLECULAR ELECTROSTATIC POTENTIALS; OCTANOL-WATER PARTITION COEFFICIENT; POLYNOMIAL EXPANSION; SEMI-EMPIRICAL MOLECULAR ORBITAL THEORY;

3D MODELING;

DRUG; OCTANOL; WATER;

ALGORITHM; ARTICLE; CHEMICAL MODEL; CHEMISTRY; HYDROPHOBICITY; SOLUBILITY;

ALGORITHMS; HYDROPHOBICITY; MODELS, CHEMICAL; OCTANOLS; PHARMACEUTICAL PREPARATIONS; SOLUBILITY; WATER;

EID: 77949812231     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci900431f     Document Type: Article

References (77)

1. Pixner, P.; Heiden, W.; Merx, H.; Moeckel, G.; Moeller, A.; Brickmann, J. Empirical Method for the Quantification and Localization of Molecular Hydrophobicity. J. Chem Inf Comp Sci 1994, 34, 1309-1319.
2. Politzer, P.; Murray, J. S.; Concha, M. C. σ-Hole bonding between like atoms; a fallacy of atomic charges. J. Mol. Model. 2008, 14, 659-665.
3. Abraham, R. J.; Hudson, B. D.; Kermode, M. W.; Nines, J. R. A General Calculation of Molecular Solvation Energies. J. Chem. Soc. Faraday Trans. 1988, 84, 1911-1917.
4. Audry, E.; Dubost, J. P.; Colleter, J. C.; Dallet, P. A new approach of structure activity relationships: The "potential of molecular lipophilicity". Eur. J. Med. Chem. 1986, 27 (1), 71-72.
5. Ghose, A. K.; Crippen, G. M. Atomic physicochemical parameters for three-dimensional structure-directed quantitative structure-activity relationships. I. Partition coefficients as a measure of hydrophobicity. J. Comput. Chem. 1986, 7, 565-677.
6. Heiden, W.; Goetze, T.; Brickmann, J. Fast generation of molecular surfaces from 3D data fields with an enhanced "marching cube" algorithm. J. Comput. Chem. 1993, 14, 246-250.
7. (a) Richards, N. G. J.; Williams, P. B.; Tute, M. In Empirical Methods for Computing Molecular Partition Coefficients. I. Upon the Need to Model the Specific Hydration of Polar Groups in Fragment-Based Approaches; Löwdin, P., Ed.; International Journal of Quantum Chemistry: Quantum Biology Symposium, 1991; pp 299-316;
8. (b) Richards, N. G. J.; Williams, P. B.; Tute, M. Empirical methods for computing molecular partition coefficients: II. Inclusion of conformational flexibility within fragment-based approaches. Int. J. Quantum Chem. 1992, 44, 219-233.
9. Jäger, R.; Kast, S. M.; Brickmann, J. Parameterization Strategy for the MolFESD Concept: Quantitative Surface Representation of Local Hydrophobicity. J. Chem. Inf. Comp. Sci. 2003, 43 (1), 237-247.
10. Ehresmann, B.; de Groot, M. J.; Alex, A.; Clark, T. New Molecular Descriptors Based on Local Properties at the Molecular Surface and a Boiling-Point Model Derived from Them. J. Chem. Inf. Comp. Sci. 2004, 43, 658-668.
11. Ehresmann, B.; de Groot, M. J.; Clark, T. Surface-Integral QSPR Models: Local Energy Properties. J. Chem. Inf. Model 2005, 45, 1053-1060.
12. (a) Dearden, J. C. Partitioning and lipophilicity in quantitative structureactivity relationships. Environ. Health Perspect. 1985, 67, 203-228.
13. (b) Hansch, C.; Leo, A.; Hoekman, D., Hydrophobic, Electronic, and Steric Constants; American Chemical Society: Washington DC, 1995;
14. (c) Kubinyi, H. Lipophilicity and drug activity. Prog. Drug Res. 1979, 23, 97-198.
15. (d) Lipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Res. 1997, 23, 3-25.
16. (e) Pliska, V.; Testa, B.; van de Waterbeemd, H., Lipophilicity in Drug Action and Toxicology; Wiley-VCH: Weinheim, 1997;
17. (f) van de Waterbeemd, H.; Smith, D. A.; Beaumont, K.; Walker, D. K. Property-based design: optimization of drug absorption and pharmacokinetics. J. Med. Chem. 2001, 44 (9), 1313-1333.
18. (g) Tetko, I. V., Prediction of physicochemical properties. In Computational Toxicology: Risk Assessment for Pharmaceutical and Environmental Chemicals; Ekins, S., Ed.; John Wiley & Sons, Inc: New Jersey, 2007; pp 241-275;
19. (h) Dearden, J. C. In silico prediction of ADMET properties: how far have we come. Expert Opin. Drug Metab. Toxicol. 2007, 3 (5), 635-639.
20. (i) Gleeson, M. P. Generation of a set of simple, interpretable ADMET rules of thumb. J. Med. Chem. 2008, 57 (4), 817-834
21. (j) Sangster, J. Octanol-water partition coefficients of simple organic compounds. J. Phys. Chem. Ref. Data 1989, 78 (3), 1111-1229.
22. (k) Hansch, C.; Bjorkroth, J. P.; Leo, A. Hydrophobicity and central nervous system agents: on the principle of minimal hydrophobicity in drug design. J. Pharm. Sci. 1987, 76, 663-687.
23. Hansch, C.; Leo, A., Fundamentals and Applications in Chemistry and Biology; American Chemical Society: Washington, DC, 1995.
24. Reulecke, I.; Lange, G.; Albrecht, J.; Klein, R.; Rarey, M. Towards an Integrated Description of Hydrogen Bonding and Dehydration: Decreasing False Positives in Virtual Screening with the HYDE Scoring Function. ChemMedChem 2008, 3, 885-897.
25. (a) Mannhold, R.; Poda, G. I.; Ostermann, C.; Tetko, I. V. Calculation of molecular lipophilicity; State-of-the-art and comparison of log P methods on more than 96 000 compounds. J. Pharm. Sci. 2008, 98, 861-893.
26. (b) Klopman, G.; Zhu, H. Recent methodologies for the estimation of n-octanol/water partition coefficients and their use in prediction of membrane transport properties of drugs. Mini Rev. Med. Chem. 2005, 5 (2), 127-133.
27. Sangster, J., Octanol-Water Partition Coefficients: Fundamentals and Physical Chemistry; John Wiley & Sons Ltd.: Chichester, 1997; Vol.2.
28. Bhal, S. K.; Kassam, K.; Peirson, I. G.; Pearl, G. M. The Rule of Five revisited: applying log D in place of log P in drug-likeness filters. Mol. Pharm. 2007, 4 (4), 556-560.
29. (a) Leo, A.; Hansch, C.; Elkins, D. Partition coefficients and their uses. Chem. Rev. 1971, 71 (6), 525-616.
30. (b) Leahy, D. E.; Taylor, P. J.; Wait, A. R.; Model Solvent Systems for QSAR Part, I. Propylene Glycol Dipelargonate (PGDP). A new Standard Solvent for use in Partition Coefficient Determination. Quant. Struct-Act. Relat. 1989, 8(1), 17-31.
31. Schulte, J.; Dürr, J.; Ritter, S.; Hauthal, W. H.; Quitzsch, K.; Maurer, G. Partition Coefficients for Environmentally Important, Multifunctional Organic Compounds in Hexane + Water. J. Chem. Eng. Data 1998, 43 (1), 69-73.
32. Dearden, J. C.; Bresnen, G. M. The Measurement of Partition Coefficients. QSAR Comb. Sci 2006, 7 (3), 133-144.
33. Valko, K. Application of high-performance liquid chromatography based measurements of lipophilicity to model biological distribution. J. Chromatogr. A 2004, 1037 (1-2), 299-310.
34. Takacs-Novak, K.; Avdeef, A. Interlaboratory study of log P determination by shake-flask and Potentiometrie methods. J. Pharm. Biomed. Anal. 1996, 14 (11), 1405-1413.
35. The physical properties database (PHYSPROP). Syracuse research corporation.
36. CrossFire Beilstein; Elsevier: Frankfurt, 2009; Vol.71.
37. Sangster, J., LOGKOW -A databank of evaluated octanol-water partition coefficients (Log P); Sangster Research Laboratories: Montreal, Quebec, accessed 11/23/2008.
38. Nys, G. G.; Rekker, R. F. The concept of hydrophobic fragmental constants (f-values). II. Extension of its applicability to the calculation of lipophilicities of aromatic and hetero-aromatic structures. Chim. Ther. 1973, 9, 361-374.
39. Liu, R.; Zhou, D. Using Molecular Fingerprint as Descriptors in the QSPR study of Lipophilicity. J. Chem. Inf. Model. 2008, 48 (3), 542-549.
40. Hughes, L. D.; Palmer, D. S.; Nigsch, F.; Mitchell, J. B. Why are some properties more difficult to predict than others? A study of QSPR models of solubility, melting point, and Log P. J. Chem. Inf. Model. 2008, 48 (1), 220-232.
41. (a) Breindl, A.; Beck, B.; Clark, T.; Glen, R. C. Prediction of the n-octanol/Water Partition Coefficient, logP, Using a Combination of Semiempirical MO-Calculations and a Neural Network. J. Mol. Model. 1997, 3, 142-155.
42. (b) Tetko, I. V.; Tanchuk, V. Y.; Villa, A. E. Prediction of n-octanol/water partition coefficients from PHYSPROP database using artificial neural networks and E-state indices. J. Chem. Inf. Comp. Sci. 2001, 41, 1407-1421.
43. (a) Huuskonen, J. J.; Villa, A. E.; Tetko, I. V. Prediction of partition coefficient based on atom-type electrotopological state indices. J. Pharm. Sci. 1999, 88 (2), 229-233.
44. (b) Livingstone, D. J.; Ford, M. G.; Huuskonen, J. J.; Salt, D. W. Simultaneous prediction of aqueous solubility and octanol/water partition coefficient based on descriptors derived from molecular structure. J. Comput. Aided Mol. Des. 2001, 15 (8), 741-752.
45. (a) Klopman, G.; Li, J.-Y.; Wang, S.; Dimayuga, M. Computer Automated log P Calculations Based on an Extended Group Contribution Approach. J. Chem. Inf. Comp. Sci. 1994, 34 (4), 752-781.
46. (b) Meylan, W. M.; Howard, P. H. Estimating log P with atom/fragments and water solubility with log P. Perspect Drug Discov. Des. 2000, 19 (1), 67-84.
47. (c) Leo, A.; Jow, P. Y.; Silipo, C.; Hansch, C. Calculation of hydrophobic constant (log P) from π and f constants. J. Med. Chem. 1975, 18 (9), 865-868.
48. (d) Petrauskas, A. A.; Kolovanov, E. A. ACD/Log P method description. Perspect Drug Discov. Des. 2000, 79 (1), 99-116.
49. Haeberlein, M.; Brinck, T. Prediction of water-octanol partition coefficients using theoretical descriptors derived from the molecular surface area and the electrostatic potential. J. Chem. Soc., Perkin Trans. 1997, 2, 289-294.
50. Chuman, H.; Mori, A.; Tanaka, H.; Yamagami, C.; Fujita, T. Analyses of the partition coefficient, log P, using ab initio MO parameter and accessible surface area of solute molecules. J. Pharm. Sci. 2004, 93 (11), 2681-2697.
51. (a) Dewar, M. J. S.; Zoebisch, E. G.; Healy, E. F.; Stewart, J. J. P. Development and use of quantum mechanical molecular models. 76. AM1: a new general purpose quantum mechanical molecular model. J. Am. Chem. Soc. 1985, 107, 3902-3909.
52. (b) Holder, A. J., AMI. In Encyclopedia of Computational Chemistry; Schleyer, P. v. R., Allinger, N. L., Clark, T., Gasteiger, J., Kollman, P. A., Schaefer, H. F., III, Schreiner, P. R., Eds.; Wiley: Chichester, 1998; pp 8-11.
53. Lombardo, F.; Shalaeva, M. Y.; Tupper, K. A.; Gao, F.; Abraham, M. H. ElogPoct: A Tool for Lipophilicity Determination in Drug Discovery. J. Med. Chem. 2000, 43, 2922-2928.
54. CORINA 3.4; Molecular Networks Inc.: Erlangen, Germany, 2006.
55. Clark, T.; Alex, A.; Beck, A.; Burkhardt, F.; Chandrasekhar, J.; Gedeck, P.; Horn, A. H. C.; Hutter, M.; Martin, B.; Rauhut, G.; Sauer, W.; Schindler, T.; Steinke, T. VAMP 8.2, available from Accelrys Inc.; Erlangen: San Diego, USA, 2002.
56. ParaSurf09, CEPOS Insilico Ltd.: Erlangen, Germany, 2009.
57. Meyer, A. Y. The size of molecules. Chem. Soc. Rev. 1985, 15, 449-475.
58. Clark, T.; Byler, K. G.; de Groot, M. J., Biological Communication via Molecular Surfaces. In Molecular Interactions- Bringing Chemistry to Life; Proceedings of the International Beilstein Workshop, Bozen, Italy, May 15-19, 2006 (Logos Verlag:), Berlin, 2008; pp 129-146.
59. Labute, P. Molecular Operating Environment, 2008.10; Chemical Computing Group: Montreal, Quebec, Canada, 2008.
60. Efroymson, M. A., Multiple regression analysis. In Mathematical Methods for Digital Computers; Ralston, A., Milf, H. A., Eds. Wiley: New York, 1960; Vol.1, pp 191-203.
61. Polikar, R. Ensemble based systems in decision making. IEEE Circ. Sys. Mag. 2006, 03/06, 21-45.
62. Kramer, C.; Tautermann, C S.; Livingstone, D. J.; Salt, D. W.; Whitley, D. C.; Beck, B.; Clark, T. Sharpening the Toolbox of Computational Chemistry: A New Approximation of Critical F-Values for Multiple Linear Regression. J. Chem. Inf. Model. 2009, 49 (1), 28-34.
63. Kramer, C.; Beck, B.; Kriegl, J. M.; Clark, T. A composite model for hERG blockade. ChemMedChem 2008, 3 (2), 254-265.
64. Breneman, C. M.; Sundling, C. M.; Sukumar, N.; Shen, L.; Katt, W. P.; Embrechts, M. J. New Developments in PEST Shape/Property Hybrid Descriptors. J. Comp. Aided Mol. Des. 2003, 17, 231-240.
65. Breneman, C.; Rhem, M. QSPR analysis of HPLC column capacoty factors for a set of high-energy materials using electronic van der Waals surface property descriptors computed by the transferable atom equivalent method. J. Comput. Chem. 1997, 18, 182-197.
66. ACD/PhysChem Suite 11.0, ACD/Labs: Toronto, Canada.
67. BioByte Inc. ClogP, 4.0; Daylight Chemical Information Systems Inc.: Aliso Viejo, CA, 2008.
68. TSAR, 3.3; Oxford Molecular Ltd., Oxford, UK; now Accelrys Inc., San Diego, CA: 2000.
69. Schroeter, T. S.; Schwaighofer, A.; Mika, S.; Ter Laak, A.; Suelzle, D.; Ganzer, U.; Heinrich, N.; Muller, K. R. Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules. J. Comput. Aided Mol. Des. 2007, 21 (12), 651-664
70. Mobley, D. L.; Dill, K. A.; Chodera, J. D. Treating Entropy and Conformational Changes in Implicit Solvent Simulations of Small Molecules. J. Phys. Chem. B 2008, 111, 938-946.
71. Rauhut, G.; Clark, T.; Steinke, T. A Numerical Self-Consistent Reaction Field (SCRF) Model for Ground and Excited States in NDDO-Based Methods. J. Am. Chem. Soc. 1993, 115, 9174-9181.
72. Craig, P. N., Drug Compendium; Pergamon Press: New York, 1990; Vol.6.
73. Ungell, A.-L.; Nylander, S.; Bergstrand, S.; Sjöberg, A.; Lennernäs, H. Personal Communication, cited in: Membrane Transport of Drugs in Different Regions of the Intestinal Tract of the Rat. J. Pharm. Sci. 1998, 87 (3), 360-366.
74. Leysen, J. E.; Janssen, P. M. F.; Gommeren, W.; Wynants, J.; Pauwels, P. J.; Janssen, P. A. J. Vitro and In Vivo Receptor Binding and Effects on Monoamine Turnover in Rat Brain Regions of the Novel Antipsychotics Risperidone and Ocaperidone. Mol. Pharmacol. 1992, 41 (3), 494-508.
75. Chalk, A. J.; Beck, B.; Clark, T. A Quantum Mechanical/Neural Net Model for Boiling Points with Error Estimation. J. Chem. Inf. Comp. Sci. 2001, 41, 457-462.
76. Winget, P.; Horn, A. H. C.; Selcuki, C.; Martin, B.; Clark, T. AMI* Parameters for Phosphorous, Sulfur and Chlorine. J. Mol. Model. 2003, 9, 408-414.
77. Stewart, J. J. P. Optimization of Parameters for Semiempirical Methods V: Modification of NDDO Approximations and Application to 70 Elements. J. Mol. Model. 2007, 73, 1173-1213.