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Computational modeling of P450s for toxicity prediction

Pages 1211-1231 | Published online: 25 Aug 2011

Bibliography

  • Gasteiger H, Engel T. Chemoinformatics: a textbook. Willey-VHC, Weinheim; 2003
  • Prentis RA, Lis Y, Walker SR. Pharmaceutical innovation by the seven UK-owned pharmaceutical companies (1964 – 1985). Br J Clin Pharmacol 1988;25(3):387-96
  • Schuster D, Laggner C, Langer T. Why drugs fail–a study on side effects in new chemical entities. Curr Pharm Des 2005;11(27):3545-59
  • DiMasi JA, Hansen RW, Grabowski HG. The price of innovation: new estimates of drug development costs. J Health Econ 2003;22(2):151-85
  • Poggesi I. Predicting human pharmacokinetics from preclinical data. Curr Opin Drug Discov Devel 2004;7(1):100-11
  • Crivori P, Poggesi I. Computation approach for predicting CYP-related metabolism properties in the screening of new drugs. Eur J Med Chem 2006;41:795-808
  • Susnow RG, Dixon SL. Use of robust classification techniques for the prediction of human cytochrome P450 2D6 inhibition. J Chem Inf Comput Sci 2003;43(4):1308-15
  • van de Waterbeemd H, Gifford E. ADMET in silico modelling: towards prediction paradise? Nat Rev Drug Discov 2003;2(3):192-204
  • Freitas RF, Bauab RL, Montanari CA. 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
  • Afzelius L, Hasselgren Arnby C, Broo A, State-of-the-art tools for computational site of metabolism predictions: comparative analysis, mechanistical insights, and future applications. Drug Metab Rev 2007;39(1):61-86
  • Yano JK, Hsu MH, Griffin KJ, Structures of human microsomal cytochrome P450 2A6 complexed with coumarin and methoxsalen. Nat Struct Mol Biol 2005;12(9):822-3
  • Kamataki T, Maeda K, Yamazoe Y, A high-spin form of cytochrome P-450 highly purified from polychlorinated biphenyl-treated rats. Catalytic characterization and immunochemical quantitation in liver microsomes. Mol Pharmacol 1983;24(1):146-55
  • Butler MA, Iwasaki M, Guengerich FP, Human cytochrome P-450PA (P-450IA2), the phenacetin O-deethylase, is primarily responsible for the hepatic 3-demethylation of caffeine and N-oxidation of carcinogenic arylamines. Proc Natl Acad Sci USA 1989;86(20):7696-700
  • Aoyama T, Gelboin HV, Gonzalez FJ. Mutagenic activation of 2-amino-3-methylimidazo[4,5-f]quinoline by complementary DNA-expressed human liver P-450. Cancer Res 1990;50(7):2060-3
  • Yanagawa Y, Sawada M, Deguchi T, Stable expression of human CYP1A2 and N-acetyltransferases in Chinese hamster CHL cells: mutagenic activation of 2-amino-3-methylimidazo[4,5-f]quinoline and 2-amino-3,8-dimethylimidazo[4,5-f]quinoxaline. Cancer Res 1994;54(13):3422-7
  • Kleiner HE, Reed MJ, DiGiovanni J. Naturally occurring coumarins inhibit human cytochromes P450 and block benzo[a]pyrene and 7,12-dimethylbenz[a]anthracene DNA adduct formation in MCF-7 cells. Chem Res Toxicol 2003;16(3):415-22
  • Hukkanen J, Jacob P III, Benowitz NL. Metabolism and disposition kinetics of nicotine. Pharmacol Rev 2005;57(1):79-115
  • Lee AM, Tyndale RF. Drugs and genotypes: how pharmacogenetic information could improve smoking cessation treatment. J Psychopharmacol 2006;20(4 Suppl):7-14
  • Ho MK, Tyndale RF. Overview of the pharmacogenomics of cigarette smoking. Pharmacogenomics J 2007;7(2):81-98
  • Lewis DF. 57 varieties: the human cytochromes P450. Pharmacogenomics 2004;5(3):305-18
  • Guengerich FP, Kim DH, Iwasaki M. Role of human cytochrome P-450 IIE1 in the oxidation of many low molecular weight cancer suspects. Chem Res Toxicol 1991;4(2):168-79
  • Jalaie M, Arimoto R, Gifford E, Prediction of drug-like molecular properties: modeling cytochrome p450 interactions. Methods Mol Biol 2004;275:449-520
  • Roy K, Roy PP. QSAR of cytochrome inhibitors. Expert Opin Drug Metab Toxicol 2009;5(10):1245-66
  • Wienkers LC, Heath TG. Predicting in vivo drug interactions from in vitro drug discovery data. Nat Rev Drug Discov 2005;4(10):825-33
  • Lill MA, Dobler M, Vedani A. Prediction of small-molecule binding to cytochrome P450 3A4: flexible docking combined with multidimensional QSAR. ChemMedChem 2006;1(1):73-81
  • Ekins S, de Groot MJ, Jones JP. Pharmacophore and three-dimensional quantitative structure activity relationship methods for modeling cytochrome p450 active sites. Drug Metab Dispos 2001;29(7):936-44
  • Ekins S, Berbaum J, Harrison RK. Generation and validation of rapid computational filters for cyp2d6 and cyp3a4. Drug Metab Dispos 2003;31(9):1077-80
  • Manga N, Duffy JC, Rowe PH, Structure-based methods for the prediction of the dominant P450 enzyme in human drug biotransformation: consideration of CYP3A4, CYP2C9, CYP2D6. SAR QSAR Environ Res 2005;16(1):43-61
  • Guengerich FP. Cytochrome p450 and chemical toxicology. Chem Res Toxicol 2008;21(1):70-83
  • Anzenbacher P, Anzenbacherova E. Cytochrome P450 and metabolism of xenobiotics. Cell Mol Life Sci 2001;58(4):737-47
  • Scripture CD, Figg WD. Drug interactions in cancer therapy. Nat Rev Cancer 2006;6(7):546-58
  • Kriegl JM, Arnhold T, Beck B, A support vector machine approach to classify human cytochrome P450 3A4 inhibitors. J Comput Aided Mol Des 2005;19(3):189-201
  • Cheng JW, Frishman WH, Aronow WS. Updates on cytochrome P450-mediated cardiovascular drug interactions. Am J Ther 2009;16(2):155-63
  • Cheng JW, Frishman WH, Aronow WS. Updates on cytochrome p450-mediated cardiovascular drug interactions. Dis Mon 2010;56(3):163-79
  • Flockhart DA. Drug interactions: cytochrome P450 drug interaction table. Indiana University School of Medicine (2007). Available from: http://medicine.iupui.edu/clinpharm/ddis/table.asp [Cited 20 January 2011]
  • Ingelman-Sundberg M. Genetic polymorphisms of cytochrome P450 2D6 (CYP2D6): clinical consequences, evolutionary aspects and functional diversity. Pharmacogenomics J 2005;5(1):6-13
  • Ekins S, Bravi G, Wikel JH, Three-dimensional-quantitative structure activity relationship analysis of cytochrome P-450 3A4 substrates. J Pharmacol Exp Ther 1999;291(1):424-33
  • Ekins S, Bravi G, Ring BJ, Three-dimensional quantitative structure activity relationship analyses of substrates for CYP2B6. J Pharmacol Exp Ther 1999;288(1):21-9
  • Lewis DF, Modi S, Dickins M. Structure-activity relationship for human cytochrome P450 substrates and inhibitors. Drug Metab Rev 2002;34(1-2):69-82
  • Haji-Momenian S, Rieger JM, Macdonald TL, Comparative molecular field analysis and QSAR on substrates binding to cytochrome p450 2D6. Bioorg Med Chem 2003;11(24):5545-54
  • Balakin KV, Ekins S, Bugrim A, Kohonen maps for prediction of binding to human cytochrome P450 3A4. Drug Metab Dispos 2004;32(10):1183-9
  • Yap CW, Chen YZ. Prediction of cytochrome P450 3A4, 2D6, and 2C9 inhibitors and substrates by using support vector machines. J Chem Inf Model 2005;45(4):982-92
  • Rada R. Evolutionary search: gradients and information. Biosystems 1982;15(2):169-77
  • Nigsch F, Mitchell JB. How to winnow actives from inactives: introducing molecular orthogonal sparse bigrams (MOSBs) and multiclass Winnow. J Chem Inf Model 2008;48(2):306-18
  • Bender A, Mussa HY, Glen RC, Similarity searching of chemical databases using atom environment descriptors (MOLPRINT 2D): evaluation of performance. J Chem Inf Comput Sci 2004;44(5):1708-18
  • 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):1688-701
  • Frank E, Hall M, Trigg L, Data mining in bioinformatics using Weka. Bioinformatics 2004;20(15):2479-81
  • Vasanthanathan P, Taboureau O, Oostenbrink C, Classification of cytochrome P450 1A2 inhibitors and noninhibitors by machine learning techniques. Drug Metab Dispos 2009;37(3):658-64
  • Lipinski CA, Lombardo F, Dominy BW, Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 2001;46(1-3):3-26
  • Mishra NK, Agarwal S, Raghava GP. Prediction of cytochrome P450 isoform responsible for metabolizing a drug molecule. BMC Pharmacol 2010;10:8
  • Wishart DS, Knox C, Guo AC, DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res 2008;36(Database issue):D901-6
  • Joachims T. Making large scale SVM learning practical. In Bernhard Schölkopf, editor. Advances in kernel methods: support vector learning. The MIT Press, USA; 1999. p. 169-84
  • Wang Q, Halpert JR. Combined three-dimensional quantitative structure-activity relationship analysis of cytochrome P450 2B6 substrates and protein homology modeling. Drug Metab Dispos 2002;30(1):86-95
  • De Rienzo F, Fanelli F, Menziani MC, Theoretical investigation of substrate specificity for cytochromes P450 IA2, P450 IID6 and P450 IIIA4. J Comput Aided Mol Des 2000;14(1):93-116
  • Snyder R, Sangar R, Wang J, Three-dimensional quantitative structure activity relationship for Cyp2d6 substrates. QSAR 2002;21(4):357-68
  • Rossato G, Ernst B, Smiesko M, Probing small-molecule binding to cytochrome P450 2D6 and 2C9: an in silico protocol for generating toxicity alerts. ChemMedChem 2010;5(12):2088-101
  • Stjernschantz E, Oostenbrink C. Improved ligand-protein binding affinity predictions using multiple binding modes. Biophys J 2010;98(11):2682-91
  • Zuegge J, Fechner U, Roche O, A fast virtual screening filter for cytochrome P450 3A4 inhibition liability of compound libraries. QSAR 2002;21(3):249-56
  • Molnar L, Keseru GM. A neural network based virtual screening of cytochrome P450 3A4 inhibitors. Bioorg Med Chem Lett 2002;12(3):419-21
  • Moon T, Chi MH, Kim D-H, Quantitative structure-activity relationships (qsar) study of flavonoid derivatives for inhibition of cytochrome P450 1A2. QSAR 2000;19(3):257-63
  • O'Brien SE, de Groot MJ. Greater than the sum of its parts: combining models for useful ADMET prediction. J Med Chem 2005;48(4):1287-91
  • Poso A, Gynther J, Juvonen R. A comparative molecular field analysis of cytochrome P450 2A5 and 2A6 inhibitors. J Comput Aided Mol Des 2001;15(3):195-202
  • Asikainen A, Tarhanen J, Poso A, Predictive value of comparative molecular field analysis modelling of naphthalene inhibition of human CYP2A6 and mouse CYP2A5 enzymes. Toxicol In Vitro 2003;17(4):449-55
  • Rahnasto M, Raunio H, Poso A, Quantitative structure-activity relationship analysis of inhibitors of the nicotine metabolizing CYP2A6 enzyme. J Med Chem 2005;48(2):440-9
  • Ekins S, Bravi G, Binkley S, Three- and four-dimensional quantitative structure activity relationship analyses of cytochrome P-450 3A4 inhibitors. J Pharmacol Exp Ther 1999;290(1):429-38
  • Ekins S, Bravi G, Binkley S, Three- and four-dimensional-quantitative structure activity relationship (3D/4D-QSAR) analyses of CYP2C9 inhibitors. Drug Metab Dispos 2000;28(8):994-1002
  • Jones JP, He M, Trager WF, Three-dimensional quantitative structure-activity relationship for inhibitors of cytochrome P4502C9. Drug Metab Dispos 1996;24(1):1-6
  • Rao S, Aoyama R, Schrag M, A refined 3-dimensional QSAR of cytochrome P450 2C9: computational predictions of drug interactions. J Med Chem 2000;43(15):2789-96
  • Afzelius L, Masimirembwa CM, Karlen A, Discriminant and quantitative PLS analysis of competitive CYP2C9 inhibitors versus non-inhibitors using alignment independent GRIND descriptors. J Comput Aided Mol Des 2002;16(7):443-58
  • Afzelius L, Zamora I, Masimirembwa CM, Conformer- and alignment-independent model for predicting structurally diverse competitive CYP2C9 inhibitors. J Med Chem 2004;47(4):907-14
  • Strobl GR, von Kruedener S, Stockigt J, Development of a pharmacophore for inhibition of human liver cytochrome P-450 2D6: molecular modeling and inhibition studies. J Med Chem 1993;36(9):1136-45
  • Ekins S, Bravi G, Binkley S, Three and four dimensional-quantitative structure activity relationship (3D/4D-QSAR) analyses of CYP2D6 inhibitors. Pharmacogenetics 1999;9(4):477-89
  • Crivori P, Poggesi I. Predictive model for identifying potential CYP2D6 inhibitors. Basic Clin Pharmacol Toxicol 2005;96(3):251-3
  • Kriegl JM, Eriksson L, Arnhold T, Multivariate modeling of cytochrome P450 3A4 inhibition. Eur J Pharm Sci 2005;24(5):451-63
  • Burton J, Danloy E, Vercauteren DP. Fragment-based prediction of cytochromes P450 2D6 and 1A2 inhibition by recursive partitioning. SAR QSAR Environ Res 2009;20(3-4):185-205
  • Xie Z, Zhang T, Wang JF, The computational model to predict accurately inhibitory activity for inhibitors towards CYP3A4. Comput Biol Med 2010;40(11-12):845-52
  • Korhonen LE, Rahnasto M, Mahonen NJ, Predictive three-dimensional quantitative structure-activity relationship of cytochrome P450 1A2 inhibitors. J Med Chem 2005;48(11):3808-15
  • Chohan KK, Paine SW, Mistry J, A rapid computational filter for cytochrome P450 1A2 inhibition potential of compound libraries. J Med Chem 2005;48(16):5154-61
  • Wanchana S, Yamashita F, Hashida M. QSAR analysis of the inhibition of recombinant CYP 3A4 activity by structurally diverse compounds using a genetic algorithm-combined partial least squares method. Pharm Res 2003;20(9):1401-8
  • Itokawa D, Nishioka T, Fukushima J. Quantitative structure activity relationship study of binding affinity of azole compounds with CYP2AB and CYP3A4. QSAR Comb Sci 2007;26:628-36
  • Roy K, Roy PP. Exploring QSARs for binding affinity of azoles with CYP2B and CYP3A enzymes using GFA and G/PLS techniques. Chem Biol Drug Des 2008;71(5):464-73
  • Roy K, Roy PP. 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):1941-51
  • Roy PP, Roy K. Exploring QSAR for CYP11B2 binding affinity and CYP11B2/CYP11B1 selectivity of diverse functional compounds using GFA and G/PLS techniques. J Enzyme Inhib Med Chem 2010;25(3):354-69
  • Jensen BF, Vind C, Padkjaer SB, In silico prediction of cytochrome P450 2D6 and 3A4 inhibition using Gaussian kernel weighted k-nearest neighbor and extended connectivity fingerprints, including structural fragment analysis of inhibitors versus noninhibitors. J Med Chem 2007;50(3):501-11
  • Van Damme S, Bultinck P. Conceptual DFT properties-based 3D QSAR: analysis of inhibitors of the nicotine metabolizing CYP2A6 enzyme. J Comput Chem 2009;30(12):1749-57
  • Gleeson MP, Davis AM, Chohan KK, Generation of in-silico cytochrome P450 1A2, 2C9, 2C19, 2D6, and 3A4 inhibition QSAR models. J Comput Aided Mol Des 2007;21(10-11):559-73
  • Kemp CA, Flanagan JU, van Eldik AJ, Validation of model of cytochrome P450 2D6: an in silico tool for predicting metabolism and inhibition. J Med Chem 2004;47(22):5340-6
  • Vasanthanathan P, Olsen L, Jorgensen FS, Computational prediction of binding affinity for CYP1A2-ligand complexes using empirical free energy calculations. Drug Metab Dispos 2010;38(8):1347-54
  • Afzelius L, Zamora I, Ridderstrom M, Competitive CYP2C9 inhibitors: enzyme inhibition studies, protein homology modeling, and three-dimensional quantitative structure-activity relationship analysis. Mol Pharmacol 2001;59(4):909-19
  • Kontijevskis A, Komorowski J, Wikberg JE. Generalized proteochemometric model of multiple cytochrome p450 enzymes and their inhibitors. J Chem Inf Model 2008;48(9):1840-50
  • Dagliyan O, Kavakli IH, Turkay M. Classification of cytochrome P450 inhibitors with respect to binding free energy and pIC50 using common molecular descriptors. J Chem Inf Model 2009;49(10):2403-11
  • Bazeley PS, Prithivi S, Struble CA, Synergistic use of compound properties and docking scores in neural network modeling of CYP2D6 binding: predicting affinity and conformational sampling. J Chem Inf Model 2006;46(6):2698-708
  • Leong MK, Chen YM, Chen HB, Development of a new predictive model for interactions with human cytochrome P450 2A6 using pharmacophore ensemble/support vector machine (PhE/SVM) approach. Pharm Res 2009;26(4):987-1000
  • Mankowski DC, Ekins S. Prediction of human drug metabolizing enzyme induction. Curr Drug Metab 2003;4(5):381-91
  • Lewis DF, Jacobs MN, Dickins M, Quantitative structure–activity relationships for inducers of cytochromes P450 and nuclear receptor ligands involved in P450 regulation within the CYP1, CYP2, CYP3 and CYP4 families. Toxicology 2002;176(1-2):51-7
  • Vedani A, Dobler M. 5D-QSAR: the key for simulating induced fit? J Med Chem 2002;45(11):2139-49
  • Ekins S, Erickson JA. A pharmacophore for human pregnane X receptor ligands. Drug Metab Dispos 2002;30(1):96-9
  • Jacobs MN. In silico tools to aid risk assessment of endocrine disrupting chemicals. Toxicology 2004;205(1-2):43-53
  • Jyrkkarinne J, Makinen J, Gynther J, Molecular determinants of steroid inhibition for the mouse constitutive androstane receptor. J Med Chem 2003;46(22):4687-95
  • Jyrkkarinne J, Windshugel B, Ronkko T, Insights into ligand-elicited activation of human constitutive androstane receptor based on novel agonists and three-dimensional quantitative structure-activity relationship. J Med Chem 2008;51(22):7181-92
  • Ung CY, Li H, Yap CW, In silico prediction of pregnane X receptor activators by machine learning approaches. Mol Pharmacol 2007;71(1):158-68
  • Khandelwal A, Krasowski MD, Reschly EJ, Machine learning methods and docking for predicting human pregnane X receptor activation. Chem Res Toxicol 2008;21(7):1457-67
  • Kortagere S, Chekmarev D, Welsh WJ, Hybrid scoring and classification approaches to predict human pregnane X receptor activators. Pharm Res 2009;26(4):1001-11
  • Ai N, Krasowski MD, Welsh WJ, Understanding nuclear receptors using computational methods. Drug Discov Today 2009;14(9-10):486-94
  • Ekins S, Kortagere S, Iyer M, Challenges predicting ligand-receptor interactions of promiscuous proteins: the nuclear receptor PXR. PLoS Comput Biol 2009;5(12):e1000594
  • Korzekwa KR, Jones JP, Gillette JR. Theoretical studies on cytochrome P-450 mediated hydroxylation: a predictive model for hydrogen atom abstractions. J Am Chem Soc 1990;112(19):7042-6
  • Jones JP, Mysinger M, Korzekwa KR. Computational models for cytochrome P450: a predictive electronic model for aromatic oxidation and hydrogen atom abstraction. Drug Metab Dispos 2002;30(1):7-12
  • Singh SB, Shen LQ, Walker MJ, A model for predicting likely sites of CYP3A4-mediated metabolism on drug-like molecules. J Med Chem 2003;46(8):1330-6
  • Sheridan RP, Korzekwa KR, Torres RA, Empirical regioselectivity models for human cytochromes P450 3A4, 2D6, and 2C9. J Med Chem 2007;50(14):3173-84
  • Zheng M, Luo X, Shen Q, Site of metabolism prediction for six biotransformations mediated by cytochromes P450. Bioinformatics 2009;25(10):1251-8
  • Lewis DF, Lake BG. Molecular modelling of CYP1A subfamily members based on an alignment with CYP102: rationalization of CYP1A substrate specificity in terms of active site amino acid residues. Xenobiotica 1996;26(7):723-53
  • Lewis DF, Eddershaw PJ, Goldfarb PS, Molecular modelling of CYP3A4 from an alignment with CYP102: identification of key interactions between putative active site residues and CYP3A-specific chemicals. Xenobiotica 1996;26(10):1067-86
  • Lewis DF, Eddershaw PJ, Goldfarb PS, Molecular modelling of cytochrome P4502D6 (CYP2D6) based on an alignment with CYP102: structural studies on specific CYP2D6 substrate metabolism. Xenobiotica 1997;27(4):319-39
  • de Groot MJ, Vermeulen NP, Kramer JD, A three-dimensional protein model for human cytochrome P450 2D6 based on the crystal structures of P450 101, P450 102, and P450 108. Chem Res Toxicol 1996;9(7):1079-91
  • Modi S, Paine MJ, Sutcliffe MJ, A model for human cytochrome P450 2D6 based on homology modeling and NMR studies of substrate binding. Biochemistry 1996;35(14):4540-50
  • de Groot MJ, Ackland MJ, Horne VA, Novel approach to predicting P450-mediated drug metabolism: development of a combined protein and pharmacophore model for CYP2D6. J Med Chem 1999;42(9):1515-24
  • de Groot MJ, Ackland MJ, Horne VA, A novel approach to predicting P450 mediated drug metabolism. CYP2D6 catalyzed N-dealkylation reactions and qualitative metabolite predictions using a combined protein and pharmacophore model for CYP2D6. J Med Chem 1999;42(20):4062-70
  • de Groot MJ, Alex AA, Jones BC. Development of a combined protein and pharmacophore model for cytochrome P450 2C9. J Med Chem 2002;45(10):1983-93
  • Zamora I, Afzelius L, Cruciani G. Predicting drug metabolism: a site of metabolism prediction tool applied to the cytochrome P450 2C9. J Med Chem 2003;46(12):2313-24
  • Vasanthanathan P, Hritz J, Taboureau O, Virtual screening and prediction of site of metabolism for cytochrome P450 1A2 ligands. J Chem Inf Model 2009;49(1):43-52
  • Rydberg P, Vasanthanathan P, Oostenbrink C, Fast prediction of cytochrome P450 mediated drug metabolism. ChemMedChem 2009;4(12):2070-9
  • Klopman G, Tu M, Talafous J. META. 3. A genetic algorithm for metabolic transform priorities optimization. J Chem Inf Comput Sci 1997;37(2):329-34
  • Talafous J, Sayre LM, Mieyal JJ, META. 2. A dictionary model of mammalian xenobiotic metabolism. J Chem Inf Comput Sci 1994;34(6):1326-33
  • Klopman G, Dimayuga M, Talafous J. META. 1. A program for the evaluation of metabolic transformation of chemicals. J Chem Inf Comput Sci 1994;34(6):1320-5
  • Testa B, Balmat A-L, Long A. Predicting drug metabolism: concepts and challenges. Pure Appl Chem 2004;76(5):907-14
  • Cruciani G, Carosati E, De Boeck B, MetaSite: understanding metabolism in human cytochromes from the perspective of the chemist. J Med Chem 2005;48(22):6970-9
  • Rydberg P, Gloriam DE, Olsen L. The SMARTCyp cytochrome P450 metabolism prediction server. Bioinformatics 2010;26(23):2988-9
  • Motta P, Pons N, Pagliarusco S, Casopitant: in-vitro data and SimCypTM simulation to predict in-vivo metabolic interactions involving Cytochrome P450 3A4. Drug Metab Dispos 2011;39(3):363-72
  • Vedani A, Smiesko M, Spreafico M, VirtualToxLab – in silico prediction of the toxic (endocrine-disrupting) potential of drugs, chemicals and natural products. Two years and 2,000 compounds of experience: a progress report. ALTEX 2009;26(3):167-76
  • Klon AE. Comparison of machine learning algorithms to predict ADME properties using chemical descriptors and molecular fingerprints. eChemInfo. Bryn Mawr College; Philadelphia, PA: 2008
  • DiMaggio PA Jr, Subramani A, Judson RS, A novel framework for predicting in vivo toxicities from in vitro data using optimal methods for dense and sparse matrix reordering and logistic regression. Toxicol Sci 2010;118(1):251-65

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