QSAR and QM/MM approaches applied to drug metabolism prediction

Mini-Reviews in Medicinal Chemistry

Volumn 12, Issue 6, 2012, Pages 573-582

  Braga, R C ^a   Andrade, C H ^a  

^a FEDERAL UNIVERSITY OF GOIÁS   (Brazil)

Author keywords

Cytochrome P450; Docking; Drug metabolism prediction; QM MM; QSAR

Indexed keywords

CYTOCHROME P450;

ALGORITHM; ANALYTIC METHOD; DRUG CLEARANCE; DRUG METABOLISM; HIGH THROUGHPUT SCREENING; IC 50; LIVER METABOLISM; MOLECULAR DOCKING; PREDICTION; QUANTITATIVE STRUCTURE ACTIVITY RELATION; QUANTUM MECHANICS MOLECULAR MECHANICS; REVIEW;

EID: 84861138534     PISSN: 13895575     EISSN: 18755607     Source Type: Journal    
DOI: 10.2174/138955712800493807     Document Type: Review

References (108)

1. van de Waterbeemd, H.; Gifford, E., ADMET in silico modelling: towards prediction paradise? Nat. Rev. Drug Discovery, 2003, 2, (3), 192-204.
2. Li, A.P., Screening for human ADME/Tox drug properties in drug discovery. Drug Discov. Today, 2001, 6, (7), 357-366.
3. Kumar, G.N.; Surapaneni, S., Role of drug metabolism in drug discovery and development. Med. Res. Rev., 2001, 21, (5), 397-411.
4. de Montellano, P.R.O., Cytochrome P450: Structure, Mechanism, and Biochemistry. 2010, 664.
5. Miners, J.O.; Smith, P.A.; Sorich, M.J.; McKinnon, R.A.; Mackenzie, P.I., Predicting human drug glucuronidation parameters: application of in vitro and in silico modeling approaches. Annu. Rev. Pharmacol. Toxicol., 2004, 44, 1-25.
6. Wang, J.F.; Chou, K.C., Molecular modeling of cytochrome P450 and drug metabolism. Curr. Drug Metab., 2010, 11, (4), 342-346.
7. Czodrowski, P.; Kriegl, J.M.; Scheuerer, S.; Fox, T., Computational approaches to predict drug metabolism. Expert Opin. Drug Metab. Toxicol., 2009, 5, 15-27.
8. de Groot, M.J., Designing better drugs: predicting cytochrome P450 metabolism. Drug Discov. Today, 2006, 11, 601-606.
9. Khan, M.T.H., Predictions of the ADMET properties of candidate drug molecules utilizing different QSAR/QSPR modelling approaches. Curr. Drug Metab., 2010, 11, 285-295.
10. Kirton, S.B.; Murray, C.W.; Verdonk, M.L.; Taylor, R.D., Prediction of binding modes for ligands in the cytochromes p450 and other heme-containing proteins. Proteins, 2005, 58, (4), 836-844.
11. Sun, H.; Scott, D.O., Structure-based drug metabolism predictions for drug design. Chem. Biol. Drug Des., 2010, 75, 3-17.
12. Roy, P.P.; Roy, K., Pharmacophore mapping, molecular docking and QSAR studies of structurally diverse compounds as CYP2B6 inhibitors. Mol. Simul., 2010, 36, (11), 887-905.
13. Senn, H.M.; Thiel, W., QM/MM Methods for Biomolecular Systems. Angew. Chem., Int. Ed., 2009, 48, (7), 1198-1229.
14. Ridder, L.; Mulholland, A.J.; Rietjens, I.M.C.M.; Vervoort, J., A quantum mechanical/molecular mechanical study of the hydroxylation of phenol and halogenated derivatives by phenol hydroxylase. J. Am. Chem. Soc., 2000, 122, (36), 8728-8738.
15. Ranaghan, K.E.; Mulholland, A.J., Investigations of enzymecatalysed reactions with combined quantum mechanics/molecular mechanics (QM/MM) methods. Int. Rev. Phys. Chem., 2010, 29, (1), 65-133.
16. Bathelt, C.M.; Zurek, J.; Mulholland, A.J.; Harvey, J.N., Electronic structure of compound I in human isoforms of cytochrome P450 from QM/MM modeling. J. Am. Chem. Soc., 2005, 127, (37), 12900-12908.
17. Smith, P.A.; Sorich, M.J.; McKinnon, R.A.; Miners, J.O., Pharmacophore and quantitative structure-activity relationship modeling: complementary approaches for the rationalization and prediction of UDP-glucuronosyltransferase 1A4 substrate selectivity. J. Med. Chem., 2003, 46, (9), 1617-1626.
18. Madden, J.C.; Cronin, M.T., Structure-based methods for the prediction of drug metabolism. Expert Opin. Drug Metab. Toxicol., 2006, 2, (4), 545-557.
19. Ekins, S.; de Groot, M.J.; Jones, J.P., Pharmacophore and threedimensional quantitative structure activity relationship methods for modeling cytochrome p450 active sites. Drug Metab. Dispos., 2001, 29, (7), 936-944.
20. de Groot, M.J.; Ekins, S., Pharmacophore modeling of cytochromes P450. Adv. Drug Delivery Rev., 2002, 54, 367-383.
21. Hansch, C.; Fujita, T., Rho-Sigma-Pi Analysis. A method for the correlation of biological activity and chemical structure. J. Am. Chem. Soc., 1964, 86, (8), 1616-1626.
22. Free, S.M., Jr.; Wilson, J.W., A Mathematical Contribution to Structure-Activity Studies. J. Med. Chem., 1964, 7, 395-399.
23. Andrade, C.H.; Pasqualoto, K.F.; Ferreira, E.I.; Hopfinger, A.J., 4D-QSAR: perspectives in drug design. Molecules, 2010, 15, (5), 3281-3294.
24. Cohen, N. Guidebook on molecular modeling in drug design. Academic Press: London, 1996.
25. Kubinyi, H. In Methods and Principles in Medicinal Chemistry. Mannhold, R.; Kroogsgard-Larsen, P.; Timmerman, H., Eds.; Wiley-VCH: Weinheim, 1993; Vol. 1, p 240.
26. Terfloth, L. Gasteiger, J.; Thomas Engel, D., Eds.; Wiley-VCH: Weinheim, 2003, pp 401-437.
27. Marshall, G.R.; Cramer, R.D., 3rd, Three-dimensional structureactivity relationships. Trends Pharmacol. Sci., 1988, 9, (8), 285-289.
28. Hopfinger, A.; Wang, S.; Tokarski, J.; Jin, B.; Albuquerque, M.; Madhav, P.; Duraiswami, C. Construction of 3D-QSAR models using the 4D-QSAR analysis formalism. J. Am. Chem. Soc. 1997, 119, 10509-10524.
29. Vedani, A.; Briem, H.; Dobler, M.; Dollinger, H.; McMasters, D.R., Multiple-conformation and protonation-state representation in 4D-QSAR: the neurokinin-1 receptor system. J. Med. Chem., 2000, 43, (23), 4416-4427.
30. Esposito, E.X., Hopfinger, A. J., Madura, J. D. In Chemoinformatics: concepts, methods, and tools for drug discovery. Bajorath, J., Ed.; Humana Press: Totowa, 2004; Vol. 275, pp 131-213.
31. Santos-Filho, O.A.; Hopfinger, A.J.; Cherkasov, A.; de Alencastro, R.B., The receptor-dependent QSAR paradigm: an overview of the current state of the art. Medicinal chemistry (Shāriqah (United Arab Emirates), 2009, 5, (4), 359-366.
32. Pan, D.; Tseng, Y.; Hopfinger, A.J., Quantitative structure-based design: formalism and application of receptor-dependent RD-4DQSAR analysis to a set of glucose analogue inhibitors of glycogen phosphorylase. J. Chem. Inf. Comput. Sci., 2003, 43, (5), 1591-1607.
33. Vedani, A.; Dobler, M., 5D-QSAR: the key for simulating induced fit? J. Med. Chem., 2002, 45, (11), 2139-2149.
34. Vedani, A.; Dobler, M.; Lill, M.A., Combining protein modeling and 6D-QSAR. Simulating the binding of structurally diverse ligands to the estrogen receptor. J. Med. Chem., 2005, 48, (11), 3700-3703.
35. Todeschini, R.; Consonni, V. Handbook of molecular descriptors. Wiley-VCH: Weinheim; Chichester, 2000.
36. Li, H.; Sun, J.; Fan, X.; Sui, X.; Zhang, L.; Wang, Y.; He, Z., Considerations and recent advances in QSAR models for cytochrome P450-mediated drug metabolism prediction. J. Comput.-Aided Mol. Des., 2008, 22, (11), 843-855.
37. Bugrim, A.; Nikolskaya, T.; Nikolsky, Y., Early prediction of drug metabolism and toxicity: systems biology approach and modeling. Drug Discov. Today, 2004, 9, (3), 127-135.
38. Golbraikh, A.; Tropsha, A., Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection. J. Comput.-Aided Mol. Des., 2002, 16, (5-6), 357-369.
39. Dudek, A.Z.; Arodz, T.; Galvez, J., Computational methods in developing quantitative structure-activity relationships (QSAR): a review. Comb. Chem. High Throughput Screening, 2006, 9, (3), 213-228.
40. Cramer III, R.D.; Patterson, D.E.; Bunce, J.D., Comparative Molecular Field Analyses (CoMFA). 1. Effect of Shape on Binding of Steroids to Carrier Proteins. J. Am. Chem. Soc., 1988, 110, 5959-5967.
41. Kearsley, S.K.; Smith, G.M., An alternative method for the alignment of molecular structures: Maximizing electrostatic and steric overlap. Tetrahedron Comput. Methodol., 1990, 3, 615-633
42. Cruciani, G.; Crivori, P.; Carrupt, P.A.; Testa, B., Molecular fields in quantitative structure-permeation relationships: the VolSurf approach. J. Mol. Struct. Theochem, 2000, 503, (1-2), 17-30.
43. Crivori, P.; Zamora, I.; Speed, B.; Orrenius, C.; Poggesi, I., Model based on GRID-derived descriptors for estimating CYP3A4 enzyme stability of potential drug candidates. J. Comput.-Aided Mol. Des., 2004, 18, (3), 155-166.
44. Hou, T.; Wang, J.; Zhang, W.; Wang, W.; Xu, X., Recent advances in computational prediction of drug absorption and permeability in drug discovery. Curr. Med. Chem., 2006, 13, (22), 2653-2667.
45. Dunn Iii, W.J.; Scott, D.R.; Glen, W.G., Principal components analysis and partial least squares regression. Tetrahedron Comput. Methodol., 1989, 2, (6), 349-376.
46. de Julian-Ortiz, J.V.; Garcia-Domenech, R.; Galvez, J.; Pogliani, L., Predictability and prediction of lowest observed adverse effect levels in a structurally heterogeneous set of chemicals. SAR QSAR Environ. Res., 2005, 16, (3), 263-272.
47. Prado-Prado, F.J.; Gonzalez-Diaz, H.; de la Vega, O.M.; Ubeira, F.M.; Chou, K.C., Unified QSAR approach to antimicrobials. Part 3: first multi-tasking QSAR model for input-coded prediction, structural back-projection, and complex networks clustering of antiprotozoal compounds. Bioorg. Med. Chem., 2008, 16, (11), 5871-5880.
48. Hemmateenejad, B., Correlation ranking procedure for factor selection in PC-ANN modeling and application to ADMETox evaluation. Chemom. Intell. Lab. Syst., 2005, 75, (2), 231-245.
49. Rogers, D.; Hopfinger, A.J., Application of Genetic Function Approximation to Quantitative Structure-Activity Relationships and Quantitative Structure-Property Relationships. J. Chem. Inf. Comput. Sci., 1994, 34, (4), 854-866.
50. Goulon, A.; Picot, T.; Duprat, A.; Dreyfus, G., Predicting activities without computing descriptors: graph machines for QSAR. SAR QSAR Environ. Res., 2007, 18, (1), 141-153.
51. Hirst, J.D.; King, R.D.; Sternberg, M.J., Quantitative structureactivity relationships by neural networks and inductive logic programming. II. The inhibition of dihydrofolate reductase by triazines. J. Comput.-Aided Mol. Des., 1994, 8, (4), 421-432.
52. King, R.D.; Muggleton, S.; Lewis, R.A.; Sternberg, M.J., Drug design by machine learning: the use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase. Proc. Natl. Acad. Sci. U. S. A., 1992, 89, (23), 11322-11326.
53. Zheng, W.; Tropsha, A., Novel variable selection quantitative structure--property relationship approach based on the k-nearestneighbor principle. J. Chem. Inf. Comput. Sci., 2000, 40, (1), 185-194.
54. Yuan, H.; Wang, Y.; Cheng, Y., Local and global quantitative structure-activity relationship modeling and prediction for the baseline toxicity. J. Chem. Inf. Model., 2007, 47, (1), 159-169.
55. Fernandez, M.; Caballero, J., Bayesian-regularized genetic neural networks applied to the modeling of non-peptide antagonists for the human luteinizing hormone-releasing hormone receptor. J. Mol. Graphics Modell., 2006, 25, (4), 410-422.
56. Ren, S.; Kim, H., Comparative assessment of multiresponse regression methods for predicting the mechanisms of toxic action of phenols. J. Chem. Inf. Comput. Sci., 2003, 43, (6), 2106-2110.
57. Obrezanova, O.; Segall, M.D., Gaussian processes for classification: QSAR modeling of ADMET and target activity. J. Chem. Inf. Model., 2010, 50, (6), 1053-1061.
58. Roy, K.; Roy, P.P., Exploring QSAR and QAAR for inhibitors of cytochrome P450 2A6 and 2A5 enzymes using GFA and G/PLS techniques. Eur. J. Med. Chem., 2009, 44, (5), 1941-1951.
59. Gleeson, M.P.; Davis, A.M.; Chohan, K.K.; Paine, S.W.; Boyer, S.; Gavaghan, C.L.; Arnby, C.H.; Kankkonen, C.; Albertson, N., Generation of in-silico cytochrome P450 1A2, 2C9, 2C19, 2D6, and 3A4 inhibition QSAR models. J. Comput.-Aided Mol. Des., 2007, 21, (10-11), 559-573.
60. Hansch, C.; Mekapati, S.B.; Kurup, A.; Verma, R.P., QSAR of cytochrome P450. Drug Metab. Rev., 2004, 36, (1), 105-156.
61. Hansch, C.; Leo, A.; Mekapati, S.B.; Kurup, A., QSAR and ADME. Bioorg. Med. Chem., 2004, 12, 3391-3400.
62. Wienkers, L.C.; Heath, T.G., Predicting in vivo drug interactions from in vitro drug discovery data. Nat. Rev. Drug Discov., 2005, 4, (10), 825-833.
63. Czernik, P.J.; Little, J.M.; Barone, G.W.; Raufman, J.P.; Radominska-Pandya, A., Glucuronidation of estrogens and retinoic acid and expression of UDP-glucuronosyltransferase 2B7 in human intestinal mucosa. Drug Metab. Dispos., 2000, 28, (10), 1210-1216.
64. Hum, D.W.; Belanger, A.; Levesque, E.; Barbier, O.; Beaulieu, M.; Albert, C.; Vallee, M.; Guillemette, C.; Tchernof, A.; Turgeon, D.; Dubois, S., Characterization of UDP-glucuronosyltransferases active on steroid hormones. J. Steroid Biochem. Mol. Biol., 1999, 69, (1-6), 413-423.
65. Miley, M.J.; Zielinska, A.K.; Keenan, J.E.; Bratton, S.M.; Radominska-Pandya, A.; Redinbo, M.R., Crystal structure of the cofactor-binding domain of the human phase II drug-metabolism enzyme UDP-glucuronosyltransferase 2B7. J. Mol. Biol., 2007, 369, (2), 498-511.
66. Smith, P.A.; Sorich, M.J.; Low, L.S.; McKinnon, R.A.; Miners, J.O., Towards integrated ADME prediction: past, present and future directions for modelling metabolism by UDPglucuronosyltransferases. J. Mol. Graph. Model., 2004, 22, (6), 507-517.
67. Terfloth, L.; Bienfait, B.; Gasteiger, J., Ligand-Based Models for the Isoform Specificity of Cytochrome P450 3A4, 2D6, and 2C9 Substrates. J. Chem. Inf. Model., 2007, 47, (4), 1688-1701.
68. Wishart, D.S.; Knox, C.; Guo, A.C.; Cheng, D.; Shrivastava, S.; Tzur, D.; Gautam, B.; Hassanali, M., DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res., 2008, 36, (Database issue), D901-906.
69. Mishra, N.K.; Agarwal, S.; Raghava, G.P., Prediction of cytochrome P450 isoform responsible for metabolizing a drug molecule. BMC Pharmacol., 2010, 10, 8.
70. Freitas, R.F.; Bauab, R.L.; Montanari, C.A., Novel Application of 2D and 3D-Similarity Searches To Identify Substrates among Cytochrome P450 2C9, 2D6, and 3A4. J. Chem. Inf. Model., 2010, 50, (1), 97-109.
71. Daggett, V.; Fersht, A., The present view of the mechanism of protein folding. Nat. Rev. Mol. Cell Biol., 2003, 4, (6), 497-502.
72. Morra, G.; Meli, M.; Colombo, G., Molecular dynamics simulations of proteins and peptides: From folding to drug design. Curr. Protein Pept. Sci., 2008, 9, (2), 181-196.
73. Yang, A.Y.C.; Mancera, R.L., Molecular dynamics simulations of ligand-induced backbone conformational changes in the binding site of the periplasmic lysine-, arginine-, ornithine-binding protein. J. Comput.-Aided Mol. Des., 2008, 22, (11), 799-814.
74. Woods, C.J.; Ng, M.H.; Johnston, S.; Murdock, S.E.; Wu, B.; Tai, K.; Fangohr, H.; Jeffreys, P.; Cox, S.; Frey, J.G.; Sansom, M.S.P.; Essex, J.W., Grid computing and biomolecular simulation. Philos T Roy Soc A, 2005, 363, (1833), 2017-2035.
75. McGeagh, J.D.; Ranaghan, K.E.; Mulholland, A.J., Protein dynamics and enzyme catalysis: Insights from simulations. Biochim. Biophys. Acta, Proteins Proteomics, 2011, In Press, Corrected Proof.
76. Paneth, P.; Dybala-Defratyka, A., Kinetics and Dynamics: From Nano-to Bio-Scale. Challenges and Advances in Computational Chemistry and Physics, 2010, (12), 575.
77. Shaik, S.; Kumar, D.; de Visser, S.P.; Altun, A.; Thiel, W., Theoretical perspective on the structure and mechanism of cytochrome P450 enzymes. Chem. Rev., 2005, 105, 2279-2328.
78. Shaik, S.; Cohen, S.; Wang, Y.; Chen, H.; Kumar, D.; Thiel, W., P450 enzymes: their structure, reactivity, and selectivity-modeled by QM/MM calculations. Chem. Rev., 2010, 110, 949-1017.
79. Siegbahn, P.E.; Borowski, T., Modeling enzymatic reactions involving transition metals. Acc. Chem. Res., 2006, 39, (10), 729-738.
80. 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, (13), 3902-3909.
81. Stewart, J.J.P., Optimization of parameters for semiempirical methods I. Method. J. Comput. Chem., 1989, 10, (2), 209-220.
82. Mulholland, A.J., Modelling enzyme reaction mechanisms, specificity and catalysis. Drug Discov. Today, 2005, 10, (20), 1393-1402.
83. Lonsdale, R.; Ranaghan, K.E.; Mulholland, A.J., Computational enzymology. Chemical Communication, 2010, 46, (14), 2354-2372.
84. Menikarachchi, L.C.; Gascon, J.A., QM/MM Approaches in Medicinal Chemistry Research. Curr. Top. Med. Chem., 2010, 10, (1), 46-54.
85. Field, M.J., Simulating enzyme reactions: Challenges and perspectives. J. Comput. Chem., 2002, 23, (1), 48-58.
86. Toscano, M.D.; Woycechowsky, K.J.; Hilvert, D., Minimalist active-site redesign: Teaching old enzymes new tricks. Angew. Chem., Int. Ed., 2007, 46, (18), 3212-3236.
87. Roca, M.; Vardi-Kilshtain, A.; Warshel, A., Toward Accurate Screening in Computer-Aided Enzyme Design. Biochemistry, 2009, 48, (14), 3046-3056.
88. Sono, M.; Roach, M.P.; Coulter, E.D.; Dawson, J.H., Hemecontaining oxygenases. Chem. Rev., 1996, 96, (7), 2841-2887.
89. de Montellano, P.R.O.; De Voss, J.J., Oxidizing species in the mechanism of cytochrome P450. Nat. Prod. Rep., 2002, 19, (4), 477-493.
90. Harvey, J.N.; Bathelt, C.M.; Mulholland, A.J., QM/MM modeling of compound I active species in cytochrome P450, Cytochrome c Peroxidase, and Ascorbate Peroxidase. J. Comput. Chem., 2006, 27, (12), 1352-1362.
91. Yoshizawa, K., Theoretical study on kinetic isotope effects in the C-H bond activation of alkanes by iron-oxo complexes. Coord. Chem. Rev., 2002, 226, (1-2), 251-259.
92. Eleftheriou, M.; Germain, R.; Royyuru, A.; Zhou, R.H., Thermal denaturing of mutant lysozyme with both OPLSAA and CHARMM force fields. Biophys. J., 2007, 193A-193A.
93. Lii, J.H.; Allinger, N.L., Molecular Mechanics-the Mm3 Force-Field for Hydrocarbons.3. The Vanderwaals Potentials and Crystal Data for Aliphatic and Aromatic-Hydrocarbons. J. Am. Chem. Soc., 1989, 111, (23), 8576-8582.
94. Lii, J.H.; Allinger, N.L., Molecular Mechanics-the Mm3 Force-Field for Hydrocarbons.2. Vibrational Frequencies and Thermodynamics. J. Am. Chem. Soc., 1989, 111, (23), 8566-8575.
95. Allinger, N.L.; Yuh, Y.H.; Lii, J.H., Molecular Mechanics-the Mm3 Force-Field for Hydrocarbons.1. J. Am. Chem. Soc., 1989, 111, (23), 8551-8566.
96. Brooks, B.R.; Brooks, C.L.; Mackerell, A.D.; Nilsson, L.; Petrella, R.J.; Roux, B.; Won, Y.; Archontis, G.; Bartels, C.; Boresch, S.; Caflisch, A.; Caves, L.; Cui, Q.; Dinner, A.R.; Feig, M.; Fischer, S.; Gao, J.; Hodoscek, M.; Im, W.; Kuczera, K.; Lazaridis, T.; Ma, J.; Ovchinnikov, V.; Paci, E.; Pastor, R.W.; Post, C.B.; Pu, J.Z.; Schaefer, M.; Tidor, B.; Venable, R.M.; Woodcock, H.L.; Wu, X.; Yang, W.; York, D.M.; Karplus, M., CHARMM: The Biomolecular Simulation Program. J. Comput. Chem., 2009, 30, (10), 1545-1614.
97. Allinger, N.L.; Kok, R.A.; Imam, M.R., Hydrogen-Bonding in Mm2. J. Comput. Chem., 1988, 9, (6), 591-595.
98. Field, M.J.; Bash, P.A.; Karplus, M., A Combined Quantum-Mechanical and Molecular Mechanical Potential for Molecular-Dynamics Simulations. J. Comput. Chem., 1990, 11, (6), 700-733.
99. Woodcock, H.L.; Hodoscek, M.; Sherwood, P.; Lee, Y.S.; Schaefer, H.F.; Brooks, B.R., Exploring the quantum mechanical/molecular mechanical replica path method: a pathway optimization of the chorismate to prephenate Claisen rearrangement catalyzed by chorismate mutase. Theor. Chem. Acc., 2003, 109, (3), 140-148.
100. Lyne, P.D.; Hodoscek, M.; Karplus, M., A hybrid QM-MM potential employing Hartree-Fock or density functional methods in the quantum region. J. Phys. Chem. A, 1999, 103, (18), 3462-3471.
101. Cui, Q.; Elstner, M.; Kaxiras, E.; Frauenheim, T.; Karplus, M., A QM/MM implementation of the self-consistent charge density functional tight binding (SCC-DFTB) method. J. Phys. Chem. B, 2001, 105, (2), 569-585.
102. Bathelt, C.M.; Mulholland, A.J.; Harvey, J.N., QM/MM modeling of benzene hydroxylation in human cytochrome P450 2C9. J. Phys. Chem. A, 2008, 112, (50), 13149-13156.
103. Lonsdale, R.; Harvey, J.N.; Mulholland, A.J., Inclusion of Dispersion Effects Significantly Improves Accuracy of Calculated Reaction Barriers for Cytochrome P450 Catalyzed Reactions. J. Phys. Chem. Lett., 2010, 1, (21), 3232-3237.
104. Harvey, J.N.; Aggarwal, V.K.; Bathelt, C.M.; Carreón-Macedo, J.-L.; Gallagher, T.; Holzmann, N.; Mulholland, A.J.; Robiette, R., QM and QM/MM studies of selectivity in organic and bioorganic chemistry. J. Phys. Org. Chem., 2006, 19, (8-9), 608-615.
105. de Groot, M.J.; Kirton, S.B.; Sutcliffe, M.J., In silico methods for predicting ligand binding determinants of cytochromes P450. Curr. Top. Med. Chem., 2004, 4, (16), 1803-1824.
106. Pirmohamed, M.; Park, B.K., Cytochrome P450 enzyme polymorphisms and adverse drug reactions. Toxicology, 2003, 192, (1), 23-32.
107. Park, J.-Y.; Harris, D., Construction and assessment of models of CYP2E1: predictions of metabolism from docking, molecular dynamics, and density functional theoretical calculations. J. Med. Chem., 2003, 46, (9), 1645-1660.
108. de Graaf, C.; Pospisil, P.; Pos, W.; Folkers, G.; Vermeulen, N.P.E., Binding mode prediction of cytochrome p450 and thymidine kinase protein-ligand complexes by consideration of water and rescoring in automated docking. J. Med. Chem., 2005, 48, (7), 2308-2318.