In silico prediction of ADMET properties: how far have we come?

Bibliography

• CROS AFA: Thesis, University of Strasbourg (1863).
   Google Scholar
• MILLS EJ: On melting point and boiling point as related to composition. Phil. Mag. (1884) 17(9):173-187.
   Google Scholar
• HANSCH C, QUINLAN JE, LAWRENCE GL: The linear free-energy relationship between partition coefficients and aqueous solubility of organic liquids. J. Org. Chem. (1968) 33(1):347-350.
   Web of Science ®Google Scholar
• TETKO IV, BRUNEAU P, MEWES H-W, ROHRER DC, PODA GI: Can we estimate the accuracy of ADMET predictions? Drug Discov. Today (2006) 11(15-16):700-707.
   PubMed Web of Science ®Google Scholar
• VAN DE WATERBEEMD H, GIFFORD E: ADMET in silico modelling: towards prediction paradise? Nat. Rev. Drug Discov. (2003) 2(3):192-204.
   PubMed Web of Science ®Google Scholar
• LOMBARDO F, GIFFORD E, SHALAEVA MY: In silico ADME prediction: data, models, facts and myths. Mini Rev. Med. Chem. (2003) 3(8):861-875.
   PubMed Web of Science ®Google Scholar
• GOLA J, OBREZANOVA O, CHAMPNESS E, SEGALL M: ADMET property prediction: the state of the art and current challenges. QSAR Comb. Sci. (2006) 25(12):1172-1180.
   Google Scholar
• KENNEDY T: Managing the drug discovery/development interface. Drug Discov. Today (1997) 2(10):436-444.
   Web of Science ®Google Scholar
• LIPINSKI CA, LOMBARDO F, DOMINY BW, FEENEY PJ: Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. (1997) 23(1):3-25.
   Web of Science ®Google Scholar
• BERESFORD AP, SELICK HE, TARBIT MH: The emerging importance of predictive ADME simulation in drug discovery. Drug Discov. Today (2002) 7(2):109-116.
   PubMed Web of Science ®Google Scholar
• DIMASI JA: New drug innovation and pharmaceutical industry structure: trends in the output of pharmaceutical firms. Drug Infect. J. (2000) 34(4):1169-1194.
   Web of Science ®Google Scholar
• VAN DE WATERBEEMD H: From in vivo to in vitro/in silico ADME: progress and challenges. Expert Opin. Drug Metab. Toxicol. (2005) 1(1):1-4.
   PubMedGoogle Scholar
• BIDAULT Y: A flexible approach for optimising in silico ADME/Tox characterisation of lead candidates. Expert Opin. Drug Metab. Toxicol. (2006) 2(1):157-168.
   PubMed Web of Science ®Google Scholar
• BERGSTRÖM CAS, STRAFFORD M, LAZOROVA L, AVDEEF A, LUTHMAN K, ARTURSSON P: Absorption classification of oral drugs based on molecular surface properties. J. Med. Chem. (2003) 46(4):558-570.
   PubMed Web of Science ®Google Scholar
• BRIGGS KA: Vitic – a data source for (quantitative) structure–activity modelling. Toxicology (2007) 234(2-3):113-114.
   PubMed Web of Science ®Google Scholar
• TODESCHINI R, CONSONNI V: Handbook of Molecular Properties. Wiley-VCH Verlag, Weinheim, Germany (2000).
   Google Scholar
• DEARDEN JC: Physico-chemical descriptors. In: Practical Applications of Quantitative Structure–Activity Relationships (QSAR) in Environmental Chemistry and Toxicology. Karcher W, Devillers J (Eds), Kluwer Academic Publishers, Dordrecht, Amsterdam (1990):25-59.
   Google Scholar
• 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.
   PubMed Web of Science ®Google Scholar
• VAN DE WATERBEEMD H, DE GROOT M: Can the internet help to meet the challenges in ADME and e-ADME? SAR QSAR Environ. Res. (2002) 13(3-4):391-401.
   PubMed Web of Science ®Google Scholar
• ABRAHAM MH, CHADHA HS, MITCHELL RC: Hydrogen-bonding. 33. Factors that influence the distribution of solutes between blood and brain. J. Pharm. Sci. (1994) 83(9):1257-1268.
   PubMed Web of Science ®Google Scholar
• LEWIS R, ERTL P, JACOBY E, TINTELNOT-BLOMLEY M, GEDECK P: Computational chemistry at Novartis. Chimia (2005) 59(7-8):545-549.
   Web of Science ®Google Scholar
• SEGALL MD, BERESFORD AP, GOLA JMR, HAWKSLEY D, TARBIT MH: Focus on success: using a probabilistic approach to achieve an optimal balance of compound properties in drug discovery. Expert Opin. Drug Metab. Toxicol. (2006) 2(2):325-337.
   PubMed Web of Science ®Google Scholar
• KLON AE, LOWRIE JF, DILLER DJ: Improved naïve Bayesian modeling of numerical data for absorption, distribution, metabolism and excretion (ADME) property prediction. J. Chem. Inf. Model. (2006) 46(5):1945-1956.
   PubMed Web of Science ®Google Scholar
• BARRETT SJ, LANGDON WB: Advances in the application of machine learning techniques in drug discovery, design and development. In: Applications of Soft Computing Recent Trends (Advances in Soft Computing). Tiwari A, Knowles J, Aveneri E, Dahal K, Roy R (Eds), Springer, Berlin/Heidelberg, Germany (2006):99-110.
   Google Scholar
• KLOPMAN G, ZHU H: Recent methodologies for the estimation of n-octanol/water partition coefficients and their use in the prediction of membrane transport properties of drugs. Mini Rev. Med. Chem. (2005) 5(2):127-133.
   PubMed Web of Science ®Google Scholar
• DEARDEN JC: In silico prediction of aqueous solubility. Expert Opin. Drug Discov. (2006) 1(1):31-52.
   PubMed Web of Science ®Google Scholar
• WAN H, ULANDER J: High-throughput pKa screening and prediction amenable for ADME profiling. Expert Opin. Drug Metab. Toxicol. (2006) 2(1):139-155.
   PubMed Web of Science ®Google Scholar
• DEARDEN JC: Quantitative structure–property relationships for prediction of boiling point, vapor pressure, and melting point. Environ. Toxicol. Chem. (2003) 22(8):1696-1709.
   PubMed Web of Science ®Google Scholar
• KULKARNI SA, ZHU J, BLECHINGER S: In silico techniques for the study and prediction of xenobiotic metabolism: a review. Xenobiotica (2005) 35(10-11):955-973.
   PubMed Web of Science ®Google Scholar
• MADDEN JC, CRONIN MTD: Structure-based methods for the prediction of drug metabolism. Expert Opin. Drug Metab. Toxicol. (2006) 2(4):545-557.
   PubMed Web of Science ®Google Scholar
• JOHNSON SR, ZHENG WF: Recent progress in the computational prediction of aqueous solubility and absorption. AAPS J. (2006) 1(1):E27-E40.
   Google Scholar
• LEAHY DE: Progress in simulation modelling for pharmacokinetics. Curr. Top. Med. Chem. (2003) 3(11):1257-1268.
   PubMed Web of Science ®Google Scholar
• DEARDEN JC: In silico prediction of drug toxicity. J. Comput. Aid. Mol. Des. (2003) 17(2):119-127.
   PubMed Web of Science ®Google Scholar
• LEWIS DFV, ITO Y, GOLDFARB PS: Cytochrome P450 structures and their substrate interactions. Drug Devel. Res. (2005) 66(1):19-24.
   Web of Science ®Google Scholar
• DE GRAAF C, VERMEULEN NPE, FEENSTRA KA: Cytochrome P450 in silico: an integrative modeling approach. J. Med. Chem. (2005) 48(8):2725-2755.
   PubMed Web of Science ®Google Scholar
• DEARDEN JC, NETZEVA TI, BIBBY R: A comparison of commercially available software for the prediction of partition coefficient. In: Designing Drugs and Crop Protectants: Processes, Problems and Solutions. Ford M, Livingstone D, Dearden J, van de Waterbeemd H (Eds), Blackwell, Oxford, UK (2003):169-170.
   Google Scholar
• DEARDEN JC, CRONIN MTD, LAPPIN DC: A comparison of commercially available software for the prediction of pKa. J. Pharm. Pharmacol. (2007) 59(Suppl. 1):A-7.
   Google Scholar
• BANIK GM: In silico ADME-Tox prediction: the more, the merrier. Curr. Drug Discov. (2004) 4:31-34.
   Google Scholar
• ABSHEAR T, BANIK GM, D'SOUZA ML, NEDWED K, PENG C: A model validation and consensus building environment. SAR QSAR Environ. Res. (2006) 17(3):311-321.
   PubMed Web of Science ®Google Scholar