In Silico ADME/Tox Predictions

[1]  P. Leeson,et al.  The influence of drug-like concepts on decision-making in medicinal chemistry , 2007, Nature Reviews Drug Discovery.

[2]  György M Keseru,et al.  A neural network based virtual screening of cytochrome P450 3A4 inhibitors. , 2002, Bioorganic & medicinal chemistry letters.

[3]  Jayaraman Chandrasekhar,et al.  Strategies and tactics for optimizing the Hit-to-Lead process and beyond--a computational chemistry perspective. , 2008, Drug discovery today.

[4]  Stephen R. Johnson,et al.  Molecular properties that influence the oral bioavailability of drug candidates. , 2002, Journal of medicinal chemistry.

[5]  G. Rishton.  Reactive compounds and in vitro false positives in HTS , 1997 .

[6]  Li Di,et al.  Biological assay challenges from compound solubility: strategies for bioassay optimization. , 2006, Drug discovery today.

[7]  Ayman El-Kattan,et al.  Impact of physiological, physicochemical and biopharmaceutical factors in absorption and metabolism mechanisms on the drug oral bioavailability of rats and humans. , 2007, Expert opinion on drug metabolism & toxicology.

[8]  Fumiyoshi Yamashita,et al.  In silico approaches for predicting ADME properties of drugs. , 2004, Drug metabolism and pharmacokinetics.

[9]  Paul D Lyne,et al.  Structure-based virtual screening: an overview. , 2002, Drug discovery today.

[10]  Ann M Richard,et al.  Future of toxicology--predictive toxicology: An expanded view of "chemical toxicity". , 2006, Chemical research in toxicology.

[11]  Daniel P Vercauteren,et al.  Recursive partitioning for the prediction of cytochromes P450 2D6 and 1A2 inhibition: importance of the quality of the dataset. , 2006, Journal of medicinal chemistry.

[12]  Alessandro Pedretti,et al.  Assessing drug-likeness--what are we missing? , 2008, Drug discovery today.

[13]  S. Walker,et al.  Pharmaceutical innovation by the seven UK-owned pharmaceutical companies (1964-1985). , 1988, British journal of clinical pharmacology.

[14]  H. Kubinyi.  Drug research: myths, hype and reality , 2003, Nature Reviews Drug Discovery.

[15]  Michael C. Hutter,et al.  Bioisosteric Similarity of Molecules Based on Structural Alignment and Observed Chemical Replacements in Drugs , 2009, J. Chem. Inf. Model..

[16]  Takayuki Ito,et al.  Novel Hierarchical Classification and Visualization Method for Multiobjective Optimization of Drug Properties: Application to Structure-Activity Relationship Analysis of Cytochrome P450 Metabolism , 2008, J. Chem. Inf. Model..

[17]  Li Xing,et al.  Influence of molecular flexibility and polar surface area metrics on oral bioavailability in the rat. , 2004, Journal of medicinal chemistry.

[18]  Esther F. Schmid,et al.  Keynote review: Is declining innovation in the pharmaceutical industry a myth? , 2005, Drug discovery today.

[19]  Gonzalo Colmenarejo,et al.  In silico prediction of drug‐binding strengths to human serum albumin , 2003, Medicinal research reviews.

[20]  Martin Serrano,et al.  Nucleic Acids Research Advance Access published October 18, 2007 ChemBank: a small-molecule screening and , 2007 .

[21]  P Wexler,et al.  TOXNET: the National Library of Medicine's toxicology database. , 1995, American family physician.

[22]  R B Altman,et al.  The PharmGKB: integration, aggregation, and annotation of pharmacogenomic data and knowledge , 2007, Clinical pharmacology and therapeutics.

[23]  N J Abbott,et al.  Assays to predict drug permeation across the blood-brain barrier, and distribution to brain. , 2008, Current drug metabolism.

[24]  Gerhard Klebe,et al.  Relibase: design and development of a database for comprehensive analysis of protein-ligand interactions. , 2003, Journal of molecular biology.

[25]  Caroline A. Lee,et al.  Drug–Drug Interactions Mediated Through P‐Glycoprotein: Clinical Relevance and In Vitro–In Vivo Correlation Using Digoxin as a Probe Drug , 2009, Clinical pharmacology and therapeutics.

[26]  Andrew M Davis,et al.  Predictive ADMET studies, the challenges and the opportunities. , 2004, Current opinion in chemical biology.

[27]  Tudor I. Oprea,et al.  Integrating virtual screening in lead discovery. , 2004, Current opinion in chemical biology.

[28]  Mathias Dunkel,et al.  SuperLigands – a database of ligand structures derived from the Protein Data Bank , 2005, BMC Bioinformatics.

[29]  Ling Yang,et al.  Classification of Substrates and Inhibitors of P-Glycoprotein Using Unsupervised Machine Learning Approach , 2005, J. Chem. Inf. Model..

[30]  P Chiba,et al.  Future directions for drug transporter modelling , 2007, Xenobiotica; the fate of foreign compounds in biological systems.

[31]  Kurt Hornik,et al.  Support Vector Machines in R , 2006 .

[32]  Stephen R. Johnson,et al.  Recent progress in the computational prediction of aqueous solubility and absorption , 2006, The AAPS Journal.

[33]  Dominic P. Williams,et al.  Idiosyncratic toxicity: the role of toxicophores and bioactivation. , 2003, Drug discovery today.

[34]  Maurizio Recanatini,et al.  hERG-related drug toxicity and models for predicting hERG liability and QT prolongation , 2009, Expert opinion on drug metabolism & toxicology.

[35]  R Daniel Benz,et al.  Landscape of current toxicity databases and database standards. , 2006, Current opinion in drug discovery & development.

[36]  P. Bork,et al.  Drug Target Identification Using Side-Effect Similarity , 2008, Science.

[37]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[38]  Hans Lennernäs,et al.  Modeling gastrointestinal drug absorption requires more in vivo biopharmaceutical data: experience from in vivo dissolution and permeability studies in humans. , 2007, Current drug metabolism.

[39]  Franco Lombardo,et al.  In silico ADME prediction: data, models, facts and myths. , 2003, Mini reviews in medicinal chemistry.

[40]  Frederick P. Roth,et al.  Chemical substructures that enrich for biological activity , 2008, Bioinform..

[41]  Ulrich A K Betz,et al.  How many genomics targets can a portfolio afford? , 2005, Drug discovery today.

[42]  Christophe G. Lambert,et al.  Analysis of a Large Structure/Biological Activity Data Set Using Recursive Partitioning , 1999, J. Chem. Inf. Comput. Sci..

[43]  B. Shoichet,et al.  A specific mechanism of nonspecific inhibition. , 2003, Journal of medicinal chemistry.

[44]  Thomas Girke,et al.  ChemMine. A Compound Mining Database for Chemical Genomics1 , 2005, Plant Physiology.

[45]  R Daniel Benz,et al.  Toxicological and clinical computational analysis and the US FDA/CDER , 2007, Expert opinion on drug metabolism & toxicology.

[46]  Sean Ekins,et al.  Techniques: application of systems biology to absorption, distribution, metabolism, excretion and toxicity. , 2005, Trends in pharmacological sciences.

[47]  Michael M Gottesman,et al.  Structure of a multidrug transporter , 2009, Nature Biotechnology.

[48]  M. Hutter,et al.  In silico prediction of drug properties. , 2009, Current medicinal chemistry.

[49]  Jordi Mestres,et al.  Coverage and bias in chemical library design. , 2008, Current opinion in chemical biology.

[50]  Thomas E. Exner,et al.  Influence of Protonation, Tautomeric, and Stereoisomeric States on Protein-Ligand Docking Results , 2009, J. Chem. Inf. Model..

[51]  György M. Keserü,et al.  The influence of lead discovery strategies on the properties of drug candidates , 2009, Nature Reviews Drug Discovery.

[52]  Li Di,et al.  Application of pharmaceutical profiling assays for optimization of drug-like properties. , 2005, Current opinion in drug discovery & development.

[53]  A. Dokoumetzidis,et al.  Modelling and simulation in drug absorption processes , 2007, Xenobiotica; the fate of foreign compounds in biological systems.

[54]  A. Li,et al.  Screening for human ADME/Tox drug properties in drug discovery. , 2001, Drug discovery today.

[55]  Joo Chuan Tong,et al.  CLEVER: pipeline for designing in silico chemical libraries. , 2009, Journal of molecular graphics & modelling.

[56]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[57]  Alexander Alanine,et al.  Lead generation--enhancing the success of drug discovery by investing in the hit to lead process. , 2003, Combinatorial chemistry & high throughput screening.

[58]  Frank R. Burden,et al.  Predictive Human Intestinal Absorption QSAR Models Using Bayesian Regularized Neural Networks , 2005 .

[59]  Joo Chuan Tong,et al.  Allergen Atlas: a comprehensive knowledge center and analysis resource for allergen information , 2009, Bioinform..

[60]  Yojiro Sakiyama,et al.  The use of machine learning and nonlinear statistical tools for ADME prediction , 2009 .

[61]  W Patrick Walters,et al.  Prediction of 'drug-likeness'. , 2002, Advanced drug delivery reviews.

[62]  J. Kramer,et al.  The application of discovery toxicology and pathology towards the design of safer pharmaceutical lead candidates , 2007, Nature Reviews Drug Discovery.

[63]  Lei Yao,et al.  ATDB: a uni-database platform for animal toxins , 2007, Nucleic Acids Res..

[64]  J Ashby,et al.  Prediction of rodent carcinogenicity for 44 chemicals: results. , 1994, Mutagenesis.

[65]  Hao Zhu,et al.  ESP: A Method To Predict Toxicity and Pharmacological Properties of Chemicals Using Multiple MCASE Databases , 2004, J. Chem. Inf. Model..

[66]  Minoru Kanehisa,et al.  The KEGG database. , 2002, Novartis Foundation symposium.

[67]  Yue Weng,et al.  Structure of P-Glycoprotein Reveals a Molecular Basis for Poly-Specific Drug Binding , 2009, Science.

[68]  H. Mewes,et al.  Can we estimate the accuracy of ADME-Tox predictions? , 2006, Drug discovery today.

[69]  W. L. Jorgensen,et al.  Prediction of drug solubility from structure. , 2002, Advanced drug delivery reviews.

[70]  Gabriele Cruciani,et al.  A pharmacophore hypothesis for P-glycoprotein substrate recognition using GRIND-based 3D-QSAR. , 2005, Journal of medicinal chemistry.

[71]  Cédric Merlot,et al.  In silico methods for early toxicity assessment. , 2008, Current opinion in drug discovery & development.

[72]  D. E. Clark.  In silico prediction of blood-brain barrier permeation. , 2003, Drug discovery today.

[73]  Michael S Lajiness,et al.  Assessment of the consistency of medicinal chemists in reviewing sets of compounds. , 2004, Journal of medicinal chemistry.

[74]  I. Poggesi,et al.  Computational models for identifying potential P-glycoprotein substrates and inhibitors. , 2006, Molecular pharmaceutics.

[75]  A. Beresford,et al.  ADME/PK as part of a rational approach to drug discovery. , 2000, Drug discovery today.

[76]  Nigel Greene,et al.  Computer systems for the prediction of toxicity: an update. , 2002, Advanced drug delivery reviews.

[77]  Dragos Horvath,et al.  Molecular similarity and property similarity. , 2004, Current topics in medicinal chemistry.

[78]  Sanjivanjit K. Bhal,et al.  The Rule of Five revisited: applying log D in place of log P in drug-likeness filters. , 2007, Molecular pharmaceutics.

[79]  G. Rishton,et al.  Failure and success in modern drug discovery: guiding principles in the establishment of high probability of success drug discovery organizations. , 2005, Medicinal chemistry (Shariqah (United Arab Emirates)).

[80]  George W. A. Milne,et al.  An Integrated in Silico Analysis of Drug-Binding to Human Serum Albumin , 2006, J. Chem. Inf. Model..

[81]  P Willett,et al.  Chemoinformatics - similarity and diversity in chemical libraries. , 2000, Current opinion in biotechnology.

[82]  Yuan Zhao,et al.  Computation of Octanol-Water Partition Coefficients by Guiding an Additive Model with Knowledge , 2007, J. Chem. Inf. Model..

[83]  Bernd Beck,et al.  Multivariate modeling of cytochrome P450 3A4 inhibition. , 2005, European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences.

[84]  Alex M Aronov,et al.  Predictive in silico modeling for hERG channel blockers. , 2005, Drug discovery today.

[85]  Jiunn H Lin,et al.  Pharmacokinetics of biotech drugs: peptides, proteins and monoclonal antibodies. , 2009, Current drug metabolism.

[86]  Richard J. Marhöfer,et al.  A Comparative Chemogenomics Strategy to Predict Potential Drug Targets in the Metazoan Pathogen, Schistosoma mansoni , 2009, PloS one.

[87]  L. Molnár,et al.  Recent advances in the prediction of blood-brain partitioning from molecular structure. , 2003, Journal of pharmaceutical sciences.

[88]  Y. Martin,et al.  A bioavailability score. , 2005, Journal of medicinal chemistry.

[89]  F. Lombardo,et al.  Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings , 1997 .

[90]  G. Grass,et al.  Physiologically-based pharmacokinetic simulation modelling. , 2002, Advanced drug delivery reviews.

[91]  A. Barabasi,et al.  Drug—target network , 2007, Nature Biotechnology.

[92]  Dennis A Smith,et al.  Pharmaceutical R&D in the spotlight: why is there still unmet medical need? , 2007, Drug discovery today.

[93]  A. Ghose,et al.  A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. , 1999, Journal of combinatorial chemistry.

[94]  Sean Ekins,et al.  Comprehensive computational assessment of ADME properties using mapping techniques. , 2005, Current drug discovery technologies.

[95]  Wolfgang Guba,et al.  Integrating molecular design resources within modern drug discovery research: the Roche experience. , 2006, Drug discovery today.

[96]  Panos Macheras,et al.  In silico prediction of ADME and pharmacokinetics. Report of an expert meeting organised by COST B15. , 2002, European Journal of Pharmaceutical Sciences.

[97]  Simon J. F. Macdonald,et al.  Analysis of the Calculated Physicochemical Properties of Respiratory Drugs: Can We Design for Inhaled Drugs Yet? , 2009, J. Chem. Inf. Model..

[98]  J. Blake,et al.  Chemoinformatics - predicting the physicochemical properties of 'drug-like' molecules. , 2000, Current opinion in biotechnology.

[99]  I. Muegge,et al.  "Holistic" in silico methods to estimate the systemic and CNS bioavailabilities of potential chemotherapeutic agents. , 2001, Current topics in medicinal chemistry.

[100]  Jennifer Fostel,et al.  Predictive ADME-Tox , 2005, Expert opinion on drug metabolism & toxicology.

[101]  Michal Linial,et al.  ClanTox: a classifier of short animal toxins , 2009, Nucleic Acids Res..

[102]  Sebastian Polak,et al.  Population-Based Mechanistic Prediction of Oral Drug Absorption , 2009, The AAPS Journal.

[103]  Steven L. Salzberg,et al.  Book Review: C4.5: Programs for Machine Learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993 , 1994, Machine Learning.

[104]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[105]  Mark S. Johnson,et al.  Generating Conformer Ensembles Using a Multiobjective Genetic Algorithm , 2007, J. Chem. Inf. Model..

[106]  Supawadee Ingsriswang,et al.  sMOL Explorer: an open source, web-enabled database and exploration tool for Small MOLecules datasets , 2007, Bioinform..

[107]  Akash Khandelwal,et al.  In silico ADME modelling 2: computational models to predict human serum albumin binding affinity using ant colony systems. , 2006, Bioorganic & medicinal chemistry.

[108]  Alessandro Pedretti,et al.  Musings on ADME Predictions and Structure–Activity Relations , 2005, Chemistry & biodiversity.

[109]  György M Keseru,et al.  Current in vitro and in silico models of blood-brain barrier penetration: a practical view. , 2009, Current opinion in drug discovery & development.

[110]  Ingebrigt Sylte,et al.  Structure and localisation of drug binding sites on neurotransmitter transporters , 2009, Journal of molecular modeling.

[111]  Adriano D. Andricopulo,et al.  PK/DB: database for pharmacokinetic properties and predictive in silico ADME models , 2008, Bioinform..

[112]  J Devillers,et al.  Simulating lipophilicity of organic molecules with a back-propagation neural network. , 1998, Journal of pharmaceutical sciences.

[113]  I. Muegge,et al.  Simple selection criteria for drug-like chemical matter. , 2001, Journal of medicinal chemistry.

[114]  Tudor I. Oprea,et al.  Systems chemical biology. , 2007 .

[115]  Alexander Hillisch,et al.  Improving the hit-to-lead process: data-driven assessment of drug-like and lead-like screening hits. , 2006, Drug discovery today.

[116]  Antje Chang,et al.  BRENDA, AMENDA and FRENDA the enzyme information system: new content and tools in 2009 , 2008, Nucleic Acids Res..

[117]  Alexander Golbraikh,et al.  Combinatorial QSAR Modeling of P-Glycoprotein Substrates , 2006, J. Chem. Inf. Model..

[118]  Jeremy R. Greenwood,et al.  Epik: a software program for pKa prediction and protonation state generation for drug-like molecules , 2007, J. Comput. Aided Mol. Des..

[119]  J J Baldwin,et al.  Prediction of drug absorption using multivariate statistics. , 2000, Journal of medicinal chemistry.

[120]  Raimond B G Ravelli,et al.  The 1.8-A crystal structure of alpha1-acid glycoprotein (Orosomucoid) solved by UV RIP reveals the broad drug-binding activity of this human plasma lipocalin. , 2008, Journal of molecular biology.

[121]  Antony J. Williams,et al.  A perspective of publicly accessible/open-access chemistry databases. , 2008, Drug discovery today.

[122]  Meindert Danhof,et al.  Tailor-made drug treatment for children: creation of an infrastructure for data-sharing and population PK-PD modeling. , 2009, Drug discovery today.

[123]  Franco Lombardo,et al.  A recursive-partitioning model for blood–brain barrier permeation , 2005, J. Comput. Aided Mol. Des..

[124]  Lin He,et al.  SePreSA: a server for the prediction of populations susceptible to serious adverse drug reactions implementing the methodology of a chemical–protein interactome , 2009, Nucleic Acids Res..

[125]  Emil Alexov,et al.  Calculating the Protonation States of Proteins and Small Molecules: Implications to Ligand-Receptor Interactions , 2008 .

[126]  R. Mannhold,et al.  Calculation of molecular lipophilicity: state of the art and comparison of methods on more than 96000 compounds , 2009, Journal of pharmaceutical sciences.

[127]  Magid Abou-Gharbia.  Discovery of innovative small molecule therapeutics. , 2009, Journal of medicinal chemistry.

[128]  Mathias Wawer,et al.  Navigating structure-activity landscapes. , 2009, Drug discovery today.

[129]  Igor V. Tetko,et al.  Virtual Computational Chemistry Laboratory – Design and Description , 2005, J. Comput. Aided Mol. Des..

[130]  Praveen M. Bahadduri,et al.  Rapid Identification of P-glycoprotein Substrates and Inhibitors , 2006, Drug Metabolism and Disposition.

[131]  M. Verdonk,et al.  A comparison of physicochemical property profiles of marketed oral drugs and orally bioavailable anti-cancer protein kinase inhibitors in clinical development. , 2007, Current topics in medicinal chemistry.

[132]  Patricia Rodriguez-Tomé,et al.  MMsINC: a large-scale chemoinformatics database , 2008, Nucleic Acids Res..

[133]  S. Ekins,et al.  In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling , 2007, British journal of pharmacology.

[134]  Jacques Chomilier,et al.  RPBS: a web resource for structural bioinformatics , 2005, Nucleic Acids Res..

[135]  M. Otagiri,et al.  Practical aspects of the ligand-binding and enzymatic properties of human serum albumin. , 2002, Biological & pharmaceutical bulletin.

[136]  Werner J Geldenhuys,et al.  Molecular modeling of blood-brain barrier nutrient transporters: in silico basis for evaluation of potential drug delivery to the central nervous system. , 2006, Life sciences.

[137]  M. Pavan,et al.  Publicly-accessible QSAR software tools developed by the Joint Research Centre , 2008, SAR and QSAR in environmental research.

[138]  William J. Welsh,et al.  New Predictive Models for Blood–Brain Barrier Permeability of Drug-like Molecules , 2008, Pharmaceutical Research.

[139]  J. Irwin,et al.  Benchmarking sets for molecular docking. , 2006, Journal of medicinal chemistry.

[140]  Luis A. Diago,et al.  Setting up a large set of protein-ligand PDB complexes for the development and validation of knowledge-based docking algorithms , 2006, BMC Bioinformatics.

[141]  Gert Vriend,et al.  Correcting ligands, metabolites, and pathways , 2006, BMC Bioinformatics.

[142]  Stéphane Bourg,et al.  Collections of Compounds – How to Deal with them? , 2008 .

[143]  Gang Chen,et al.  A New Rapid and Effective Chemistry Space Filter in Recognizing a Druglike Database , 2005, J. Chem. Inf. Model..

[144]  Tudor I. Oprea,et al.  Property distribution of drug-related chemical databases* , 2000, J. Comput. Aided Mol. Des..

[145]  Jóhannes Reynisson,et al.  Investigation of the incidence of "undesirable" molecular moieties for high-throughput screening compound libraries in marketed drug compounds. , 2009, European journal of medicinal chemistry.

[146]  Peter Gedeck,et al.  Exploiting QSAR models in lead optimization. , 2008, Current opinion in drug discovery & development.

[147]  J. Irwin,et al.  Docking and chemoinformatic screens for new ligands and targets. , 2009, Current opinion in biotechnology.

[148]  Wolfgang Muster,et al.  Computational toxicology in drug development. , 2008, Drug discovery today.

[149]  H. van de Waterbeemd,et al.  Property-based design: optimization of drug absorption and pharmacokinetics. , 2001, Journal of medicinal chemistry.

[150]  Pierre Bruneau,et al.  Search for Predictive Generic Model of Aqueous Solubility Using Bayesian Neural Nets , 2001, J. Chem. Inf. Comput. Sci..

[151]  M. Dickson,et al.  Key factors in the rising cost of new drug discovery and development , 2004, Nature Reviews Drug Discovery.

[152]  Balázs Sarkadi,et al.  The role of ABC transporters in drug absorption, distribution, metabolism, excretion and toxicity (ADME-Tox). , 2008, Drug discovery today.

[153]  M Paul Gleeson,et al.  In silico human and rat Vss quantitative structure-activity relationship models. , 2006, Journal of medicinal chemistry.

[154]  Rieko Arimoto,et al.  Development of CYP3A4 Inhibition Models: Comparisons of Machine-Learning Techniques and Molecular Descriptors , 2005, Journal of biomolecular screening.

[155]  P. Ertl,et al.  Computational approaches to determine drug solubility. , 2007, Advanced drug delivery reviews.

[156]  James F Blake,et al.  Identification and evaluation of molecular properties related to preclinical optimization and clinical fate. , 2005, Medicinal chemistry (Shariqah (United Arab Emirates)).

[157]  Mathias Dunkel,et al.  SuperDrug: a conformational drug database , 2005, Bioinform..

[158]  Tingjun Hou,et al.  ADME Evaluation in Drug Discovery, 8. The Prediction of Human Intestinal Absorption by a Support Vector Machine , 2007, J. Chem. Inf. Model..

[159]  Mathias Dunkel,et al.  SuperNatural: a searchable database of available natural compounds , 2005, Nucleic Acids Res..

[160]  Daniel C. Liebler,et al.  Elucidating mechanisms of drug-induced toxicity , 2005, Nature Reviews Drug Discovery.

[161]  Tudor I. Oprea,et al.  Pursuing the leadlikeness concept in pharmaceutical research. , 2004, Current opinion in chemical biology.

[162]  Olivier Sperandio,et al.  Design of protein–membrane interaction inhibitors by virtual ligand screening, proof of concept with the C2 domain of factor V , 2007, Proceedings of the National Academy of Sciences.

[163]  Jacques Chomilier,et al.  Frog: a FRee Online druG 3D conformation generator , 2007, Nucleic Acids Res..

[164]  T. Kennedy.  Managing the drug discovery/development interface , 1997 .

[165]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[166]  John C Dearden,et al.  In silico prediction of ADMET properties: how far have we come? , 2007, Expert opinion on drug metabolism & toxicology.

[167]  Ferenc Zsila,et al.  Determination of human serum alpha1-acid glycoprotein and albumin binding of various marketed and preclinical kinase inhibitors. , 2009, Current medicinal chemistry.

[168]  Christoph Helma,et al.  Lazy structure-activity relationships (lazar) for the prediction of rodent carcinogenicity and Salmonella mutagenicity , 2006, Molecular Diversity.

[169]  Wolfgang Guba,et al.  Development of a virtual screening method for identification of "frequent hitters" in compound libraries. , 2002, Journal of medicinal chemistry.

[170]  Yan Li,et al.  Modeling K(m) values using electrotopological state: substrates for cytochrome P450 3A4-mediated metabolism. , 2005, Bioorganic & medicinal chemistry letters.

[171]  Li Di,et al.  Pharmaceutical profiling in drug discovery. , 2003, Drug discovery today.

[172]  Michael C. Hutter,et al.  Gradual in Silico Filtering for Druglike Substances , 2008, J. Chem. Inf. Model..

[173]  G. Rishton.  Nonleadlikeness and leadlikeness in biochemical screening. , 2003, Drug discovery today.

[174]  M. Cheeseman,et al.  Structure-activity relationship analysis tools: validation and applicability in predicting carcinogens. , 2008, Regulatory toxicology and pharmacology : RTP.

[175]  M. Hashida,et al.  Molecular and pharmacokinetic properties of 222 commercially available oral drugs in humans. , 2001, Biological & pharmaceutical bulletin.

[176]  C. Laggner,et al.  Why drugs fail--a study on side effects in new chemical entities. , 2005 .

[177]  John Cl Erve,et al.  Chemical toxicology: reactive intermediates and their role in pharmacology and toxicology , 2006, Expert opinion on drug metabolism & toxicology.

[178]  T. Ritchie,et al.  The impact of aromatic ring count on compound developability--are too many aromatic rings a liability in drug design? , 2009, Drug discovery today.

[179]  Hugo O. Villar,et al.  Substructural Analysis in Drug Discovery , 2007 .

[180]  Sean Ekins,et al.  A Turning Point For Blood–Brain Barrier Modeling , 2009, Pharmaceutical Research.

[181]  Brian K. Shoichet,et al.  ZINC - A Free Database of Commercially Available Compounds for Virtual Screening , 2005, J. Chem. Inf. Model..

[182]  D. Hawkins,et al.  Analysis of a 2(9) full factorial chemical library. , 1995, Journal of medicinal chemistry.

[183]  U Norinder,et al.  Theoretical calculation and prediction of P-glycoprotein-interacting drugs using MolSurf parametrization and PLS statistics. , 2000, European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences.

[184]  Richard J. Povinelli,et al.  Synergistic Use of Compound Properties and Docking Scores in Neural Network Modeling of CYP2D6 Binding: Predicting Affinity and Conformational Sampling , 2006, J. Chem. Inf. Model..

[185]  Gajendra P. S. Raghava,et al.  HaptenDB: a comprehensive database of haptens, carrier proteins and anti-hapten antibodies , 2006, Bioinform..

[186]  S. Hitchcock,et al.  Blood-brain barrier permeability considerations for CNS-targeted compound library design. , 2008, Current opinion in chemical biology.

[187]  M. Jamei,et al.  A framework for assessing inter-individual variability in pharmacokinetics using virtual human populations and integrating general knowledge of physical chemistry, biology, anatomy, physiology and genetics: A tale of 'bottom-up' vs 'top-down' recognition of covariates. , 2009, Drug metabolism and pharmacokinetics.

[188]  P. Leeson,et al.  A comparison of physiochemical property profiles of development and marketed oral drugs. , 2003, Journal of medicinal chemistry.

[189]  Maykel Pérez González,et al.  Applications of 2D descriptors in drug design: a DRAGON tale. , 2008, Current topics in medicinal chemistry.

[190]  D. Sanderson,et al.  Computer Prediction of Possible Toxic Action from Chemical Structure; The DEREK System , 1991, Human & experimental toxicology.

[191]  Jun Xu,et al.  Drug-like Index: A New Approach To Measure Drug-like Compounds and Their Diversity , 2000, J. Chem. Inf. Comput. Sci..

[192]  Thomas J. Vidmar,et al.  Predicting Drug Absorption: How Nature Made It a Difficult Problem , 2002, Journal of Pharmacology and Experimental Therapeutics.

[193]  Iris Grossman,et al.  ADME pharmacogenetics: current practices and future outlook , 2009 .

[194]  Yojiro Sakiyama,et al.  Predicting human liver microsomal stability with machine learning techniques. , 2008, Journal of molecular graphics & modelling.

[195]  I. Tetko,et al.  ISIDA - Platform for Virtual Screening Based on Fragment and Pharmacophoric Descriptors , 2008 .

[196]  Alan M Palmer,et al.  CNS drug discovery: challenges and solutions. , 2005, Drug news & perspectives.

[197]  Wei Zhang,et al.  Recent advances in computational prediction of drug absorption and permeability in drug discovery. , 2006, Current medicinal chemistry.

[198]  Scott Summerfield,et al.  Discovery DMPK: changing paradigms in the eighties, nineties and noughties , 2009, Expert opinion on drug discovery.

[199]  Ulrich Rester,et al.  From virtuality to reality - Virtual screening in lead discovery and lead optimization: a medicinal chemistry perspective. , 2008, Current opinion in drug discovery & development.

[200]  Tudor I. Oprea,et al.  Is There a Difference between Leads and Drugs? A Historical Perspective , 2001, J. Chem. Inf. Comput. Sci..

[201]  Kristian Rother,et al.  SuperHapten: a comprehensive database for small immunogenic compounds , 2006, Nucleic Acids Res..

[202]  David S. Wishart,et al.  DrugBank: a comprehensive resource for in silico drug discovery and exploration , 2005, Nucleic Acids Res..

[203]  Thomas Sander,et al.  OSIRIS, an Entirely in-House Developed Drug Discovery Informatics System , 2009, J. Chem. Inf. Model..

[204]  Søren Brunak,et al.  Prediction of pH-Dependent Aqueous Solubility of Druglike Molecules , 2006, J. Chem. Inf. Model..

[205]  Katharine A. Briggs.  Vitic—A data source for (quantitative) structure–activity relationship modelling , 2007 .

[206]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[207]  Bin Chen,et al.  Gaining Insight into Off-Target Mediated Effects of Drug Candidates with a Comprehensive Systems Chemical Biology Analysis , 2009, J. Chem. Inf. Model..

[208]  Alexander Hillisch,et al.  In Silico ADMET Traffic Lights as a Tool for the Prioritization of HTS Hits , 2006, ChemMedChem.

[209]  G. Bemis,et al.  The properties of known drugs. 1. Molecular frameworks. , 1996, Journal of medicinal chemistry.

[210]  Markus A Lill,et al.  The challenge of predicting drug toxicity in silico. , 2006, Basic & clinical pharmacology & toxicology.

[211]  Alexandre Varnek,et al.  Building a chemical space based on fragment descriptors. , 2008, Combinatorial chemistry & high throughput screening.

[212]  D. Butina,et al.  Predicting ADME properties in silico: methods and models. , 2002, Drug discovery today.

[213]  Ann Richard,et al.  ACToR--Aggregated Computational Toxicology Resource. , 2008, Toxicology and applied pharmacology.

[214]  John H. Van Drie,et al.  Computer-aided drug design: the next 20 years , 2007, J. Comput. Aided Mol. Des..

[215]  Claudio N. Cavasotto,et al.  High-throughput and in silico screenings in drug discovery , 2009, Expert opinion on drug discovery.

[216]  R. Krauss,et al.  When good drugs go bad , 2007, Nature.

[217]  B. Shoichet,et al.  Identification and prediction of promiscuous aggregating inhibitors among known drugs. , 2003, Journal of medicinal chemistry.

[218]  Tingjun Hou,et al.  ADME Evaluation in Drug Discovery, 6. Can Oral Bioavailability in Humans Be Effectively Predicted by Simple Molecular Property-Based Rules? , 2007, J. Chem. Inf. Model..

[219]  J. Taskinen,et al.  Neural network modeling for estimation of the aqueous solubility of structurally related drugs. , 1997, Journal of pharmaceutical sciences.

[220]  Meir Glick,et al.  Streamlining lead discovery by aligning in silico and high-throughput screening. , 2006, Current opinion in chemical biology.

[221]  Seung-Hoon Choi,et al.  Artificial neural network models for prediction of intestinal permeability of oligopeptides , 2007, BMC Bioinformatics.

[222]  I. Muegge.  Selection criteria for drug‐like compounds , 2003, Medicinal research reviews.

[223]  Sean Ekins,et al.  Pharmacophore-based discovery of ligands for drug transporters. , 2006, Advanced drug delivery reviews.

[224]  Anang A Shelat,et al.  The interdependence between screening methods and screening libraries. , 2007, Current opinion in chemical biology.

[225]  W. Patrick Walters,et al.  Filtering databases and chemical libraries , 2004, Molecular Diversity.

[226]  H. Moser,et al.  Physicochemical properties of antibacterial compounds: implications for drug discovery. , 2008, Journal of medicinal chemistry.

[227]  Li Di,et al.  Profiling drug-like properties in discovery research. , 2003, Current opinion in chemical biology.

[228]  Glen Eugene Kellogg,et al.  Web application for studying the free energy of binding and protonation states of protein–ligand complexes based on HINT , 2009, J. Comput. Aided Mol. Des..

[229]  Barry C Jones,et al.  Predicting oral absorption and bioavailability. , 2003, Progress in medicinal chemistry.

[230]  Stuart L. Schreiber,et al.  Query Chem: a Google-powered web search combining text and chemical structures , 2006, Bioinform..

[231]  Andreas Steinmeyer,et al.  The Hit‐to‐Lead Process at Schering AG: Strategic Aspects , 2006, ChemMedChem.

[232]  Igor V. Tetko,et al.  Application of Associative Neural Networks for Prediction of Lipophilicity in ALOGPS 2.1 Program , 2002, J. Chem. Inf. Comput. Sci..

[233]  Pierre Tufféry,et al.  wwLigCSRre: a 3D ligand-based server for hit identification and optimization , 2009, Nucleic Acids Res..

[234]  Gordon M. Crippen,et al.  pKa Prediction of Monoprotic Small Molecules the SMARTS Way , 2008, J. Chem. Inf. Model..

[235]  W. Patrick Walters,et al.  A guide to drug discovery: Designing screens: how to make your hits a hit , 2003, Nature Reviews Drug Discovery.

[236]  Robert Sabatier,et al.  Additive splines for partial least squares regression , 1997 .

[237]  Ajay,et al.  Can we learn to distinguish between "drug-like" and "nondrug-like" molecules? , 1998, Journal of medicinal chemistry.

[238]  Yvan Vander Heyden,et al.  Classification Tree Models for the Prediction of Blood-Brain Barrier Passage of Drugs , 2006, J. Chem. Inf. Model..

[239]  Peter Gedeck,et al.  QSAR - How Good Is It in Practice? Comparison of Descriptor Sets on an Unbiased Cross Section of Corporate Data Sets , 2006, J. Chem. Inf. Model..

[240]  Zukang Feng,et al.  Ligand Depot: a data warehouse for ligands bound to macromolecules , 2004, Bioinform..

[241]  Ivonne M C M Rietjens,et al.  Promises and pitfalls of quantitative structure-activity relationship approaches for predicting metabolism and toxicity. , 2008, Chemical research in toxicology.

[242]  J. F. Wang,et al.  Prediction of P-Glycoprotein Substrates by a Support Vector Machine Approach , 2004, J. Chem. Inf. Model..

[243]  A. Michiel van Rhee,et al.  Use of Recursion Forests in the Sequential Screening Process: Consensus Selection by Multiple Recursion Trees , 2003, J. Chem. Inf. Comput. Sci..

[244]  Sean Ekins,et al.  Novel web-based tools combining chemistry informatics, biology and social networks for drug discovery. , 2009, Drug discovery today.

[245]  Alban Arrault,et al.  Managing, profiling and analyzing a library of 2.6 million compounds gathered from 32 chemical providers , 2006, Molecular Diversity.

[246]  D. E. Clark,et al.  In Silico Predictions of Blood-Brain Barrier Penetration: Considerations to “Keep in Mind” , 2005, Journal of Pharmacology and Experimental Therapeutics.

[247]  Ann M Richard,et al.  Distributed structure-searchable toxicity (DSSTox) public database network: a proposal. , 2002, Mutation research.

[248]  Timothy Clark,et al.  In Silico Prediction of Buffer Solubility Based on Quantum-Mechanical and HQSAR- and Topology-Based Descriptors , 2006, J. Chem. Inf. Model..

[249]  I. Kola,et al.  Can the pharmaceutical industry reduce attrition rates? , 2004, Nature Reviews Drug Discovery.

[250]  Lemont B. Kier,et al.  Modeling Blood-Brain Barrier Partitioning Using the Electrotopological State , 2002, J. Chem. Inf. Comput. Sci..

[251]  Timothy Clark,et al.  CypScore: Quantitative Prediction of Reactivity toward Cytochromes P450 Based on Semiempirical Molecular Orbital Theory , 2009, ChemMedChem.

[252]  Michael Darsow,et al.  ChEBI: a database and ontology for chemical entities of biological interest , 2007, Nucleic Acids Res..

[253]  Olivier Sperandio,et al.  FAF-Drugs2: Free ADME/tox filtering tool to assist drug discovery and chemical biology projects , 2008, BMC Bioinformatics.

[254]  H. van de Waterbeemd,et al.  ADMET in silico modelling: towards prediction paradise? , 2003, Nature reviews. Drug discovery.

[255]  Scott Boyer,et al.  Reaction Site Mapping of Xenobiotic Biotransformations , 2007, J. Chem. Inf. Model..

[256]  Alexander Tropsha,et al.  QSAR modeling of human serum protein binding with several modeling techniques utilizing structure-information representation. , 2006, Journal of medicinal chemistry.

[257]  Y T Woo,et al.  Development of structure-activity relationship rules for predicting carcinogenic potential of chemicals. , 1995, Toxicology letters.

[258]  G Wolber,et al.  Enhancing drug discovery through in silico screening: strategies to increase true positives retrieval rates. , 2008, Current medicinal chemistry.

[259]  Alexandre Jacob,et al.  In silico platform for xenobiotics ADME-T pharmacological properties modeling and prediction. Part II: The body in a Hilbertian space. , 2009, Drug discovery today.

[260]  F. Gultekin,et al.  Renal deterioration caused by carcinogens as a consequence of free radical mediated tissue damage: a review of the protective action of melatonin , 2007, Archives of Toxicology.

[261]  K. Ziegelbauer,et al.  Genomics: success or failure to deliver drug targets? , 2005, Current opinion in chemical biology.

[262]  A. Garnier-Suillerot,et al.  A Structural Model for the Open Conformation of the mdr1 P-glycoprotein Based on the MsbA Crystal Structure* , 2003, Journal of Biological Chemistry.

[263]  Ulf Norinder,et al.  Prediction of ADMET Properties , 2006, ChemMedChem.

[264]  R A Ford,et al.  Estimation of toxic hazard--a decision tree approach. , 1978, Food and cosmetics toxicology.

[265]  Taeyoung Yoon,et al.  2-Aryl-3,6-dialkyl-5-dialkylaminopyrimidin-4-ones as novel crf-1 receptor antagonists. , 2003, Bioorganic & medicinal chemistry letters.

[266]  H. Kubinyi,et al.  A scoring scheme for discriminating between drugs and nondrugs. , 1998, Journal of medicinal chemistry.

[267]  Jaina Mistry,et al.  A rapid computational filter for cytochrome P450 1A2 inhibition potential of compound libraries. , 2005, Journal of medicinal chemistry.

[268]  B. Efron.  Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation , 1983 .

[269]  Shinya Ito,et al.  Challenges for drug studies in children: CYP3A phenotyping as example. , 2009, Drug discovery today.

[270]  Meihua Rose Feng,et al.  Assessment of blood-brain barrier penetration: in silico, in vitro and in vivo. , 2002, Current drug metabolism.

[271]  Alex Bateman,et al.  Databases, data tombs and dust in the wind , 2008, Bioinform..

[272]  Yi Wang,et al.  DITOP: drug-induced toxicity related protein database , 2007, Bioinform..

[273]  Tudor I. Oprea,et al.  The Design of Leadlike Combinatorial Libraries. , 1999, Angewandte Chemie.

[274]  Marta Bellini,et al.  Straightforward recursive partitioning model for discarding insoluble compounds in the drug discovery process. , 2008, Journal of medicinal chemistry.

[275]  William J Egan,et al.  Prediction of intestinal permeability. , 2002, Advanced drug delivery reviews.

[276]  Li Di,et al.  Solution stability--plasma, gastrointestinal, bioassay. , 2008, Current drug metabolism.

[277]  A. Beresford,et al.  The emerging importance of predictive ADME simulation in drug discovery. , 2002, Drug discovery today.

[278]  Colin D. Brown,et al.  LogD: lipophilicity for ionisable compounds. , 2008, Chemosphere.

[279]  Ulrike Schmidt,et al.  SuperToxic: a comprehensive database of toxic compounds , 2008, Nucleic Acids Res..

[280]  Michael C Hutter,et al.  Determination of hERG channel blockers using a decision tree. , 2006, Bioorganic & medicinal chemistry.

[281]  Pickett,et al.  Computational methods for the prediction of 'drug-likeness' , 2000, Drug discovery today.

[282]  Matthew Segall,et al.  In silico prediction of ADME properties: are we making progress? , 2004, Current opinion in drug discovery & development.

[283]  Dragos Horvath,et al.  Development and Validation of a Pharmacophore‐Based QSAR Model for the Prediction of CNS Activity , 2009, ChemMedChem.

[284]  M. D. de Groot,et al.  Understanding CYP2D6 interactions. , 2009, Drug discovery today.

[285]  Christian R Noe,et al.  In silico prediction models for blood-brain barrier permeation. , 2004, Current medicinal chemistry.

[286]  Wendy A. Warr,et al.  ChEMBL. An interview with John Overington, team leader, chemogenomics at the European Bioinformatics Institute Outstation of the European Molecular Biology Laboratory (EMBL-EBI) , 2009, J. Comput. Aided Mol. Des..

[287]  M C Nicklaus,et al.  Internet resources integrating many small-molecule databases1 , 2008, SAR and QSAR in environmental research.

[288]  Milan Meloun,et al.  Benchmarking and validating algorithms that estimate pKa values of drugs based on their molecular structures , 2007, Analytical and bioanalytical chemistry.

[289]  Henk-Jan Guchelaar,et al.  Cardiotoxicity of cytotoxic drugs. , 2004, Cancer treatment reviews.

[290]  Julen Oyarzabal,et al.  In vivo, in vitro and in silico methods for small molecule transfer across the BBB. , 2009, Journal of pharmaceutical sciences.

[291]  S. Wold.  Nonlinear partial least squares modelling II. Spline inner relation , 1992 .

[292]  Loriano Storchi,et al.  New and Original pKa Prediction Method Using Grid Molecular Interaction Fields , 2007, J. Chem. Inf. Model..

[293]  M. Hämäläinen,et al.  Predicting the intestinal absorption potential of hits and leads. , 2004, Drug discovery today. Technologies.

[294]  G. Bemis,et al.  Properties of known drugs. 2. Side chains. , 1999, Journal of medicinal chemistry.

[295]  Marcin von Grotthuss,et al.  Ligand-Info, Searching for Similar Small Compounds Using Index Profiles , 2003, Bioinform..

[296]  Kairui Feng,et al.  The Simcyp® Population-based ADME Simulator , 2009 .

[297]  Romualdo Benigni,et al.  Predictivity of QSAR , 2008, J. Chem. Inf. Model..

[298]  Albert P. Li.  Human‐Based in vitro Experimental Systems for the Evaluation of Human Drug Safety , 2007 .

[299]  Matthew D. Segall,et al.  Automatic QSAR modeling of ADME properties: blood–brain barrier penetration and aqueous solubility , 2008, J. Comput. Aided Mol. Des..

[300]  Rajarshi Guha,et al.  Utilizing high throughput screening data for predictive toxicology models: protocols and application to MLSCN assays , 2008, J. Comput. Aided Mol. Des..

[301]  Junmei Wang,et al.  Structure – ADME relationship: still a long way to go? , 2008, Expert opinion on drug metabolism & toxicology.

[302]  M. Gleeson.  Generation of a set of simple, interpretable ADMET rules of thumb. , 2008, Journal of medicinal chemistry.

[303]  A. W. Schüttelkopf,et al.  PRODRG: a tool for high-throughput crystallography of protein-ligand complexes. , 2004, Acta crystallographica. Section D, Biological crystallography.

[304]  Sarah N. Hilmer,et al.  ADME-tox issues for the elderly , 2008 .

[305]  W. L. Jorgensen.  The Many Roles of Computation in Drug Discovery , 2004, Science.

[306]  Lawrence X. Yu,et al.  Mechanistic Approaches to Predicting Oral Drug Absorption , 2009, The AAPS Journal.

[307]  László Orfi,et al.  Comparison of predictive ability of water solubility QSPR models generated by MLR, PLS and ANN methods. , 2004, Mini reviews in medicinal chemistry.

[308]  Ingo Muegge.  Synergies of virtual screening approaches. , 2008, Mini reviews in medicinal chemistry.

[309]  Juan M. Luco,et al.  Prediction of the Brain-Blood Distribution of a Large Set of Drugs from Structurally Derived Descriptors Using Partial Least-Squares (PLS) Modeling , 1999, J. Chem. Inf. Comput. Sci..

[310]  Andrew C. Good,et al.  An Empirical Process for the Design of High-Throughput Screening Deck Filters , 2006, J. Chem. Inf. Model..

[311]  G. Klebe,et al.  Merging chemical and biological space: Structural mapping of enzyme binding pocket space , 2009, Proteins.

[312]  Jóhannes Reynisson,et al.  Known drug space as a metric in exploring the boundaries of drug-like chemical space. , 2009, European journal of medicinal chemistry.

[313]  Romualdo Benigni,et al.  Structure alerts for carcinogenicity, and the Salmonella assay system: a novel insight through the chemical relational databases technology. , 2008, Mutation research.

[314]  J. Uetrecht,et al.  Animal models of idiosyncratic drug reactions. , 2004, Chemico-biological interactions.

[315]  R P Mason,et al.  Nitroarene reduction and generation of free radicals by cell-free extracts of wild-type, and nitroreductase-deficient and -enriched Salmonella typhimurium strains used in the umu gene induction assay. , 1999, Toxicology and applied pharmacology.

[316]  A. Nassar,et al.  Improving the decision-making process in structural modification of drug candidates: reducing toxicity. , 2004, Drug discovery today.

[317]  M. Milik,et al.  Mapping adverse drug reactions in chemical space. , 2009, Journal of medicinal chemistry.

[318]  Roman Rosipal,et al.  Overview and Recent Advances in Partial Least Squares , 2005, SLSFS.

[319]  Ying Zhang,et al.  HMDB: the Human Metabolome Database , 2007, Nucleic Acids Res..

[320]  M H Tarbit,et al.  High-throughput approaches for evaluating absorption, distribution, metabolism and excretion properties of lead compounds. , 1998, Current opinion in chemical biology.

[321]  M. Congreve,et al.  A 'rule of three' for fragment-based lead discovery? , 2003, Drug discovery today.

[322]  Li Di,et al.  Pharmaceutical profiling method for lipophilicity and integrity using liquid chromatography-mass spectrometry. , 2003, Journal of chromatography. B, Analytical technologies in the biomedical and life sciences.

[323]  Esther F. Schmid,et al.  Drug withdrawals and the lessons within. , 2006, Current opinion in drug discovery & development.

[324]  Sara Eyal,et al.  Drug interactions at the blood-brain barrier: fact or fantasy? , 2009, Pharmacology & therapeutics.

[325]  Søren Brunak,et al.  Prediction methods and databases within chemoinformatics : Emphasis on drugs and drug candidates , 2005 .

[326]  R. Dewitte,et al.  Avoiding physicochemical artefacts in early ADME-Tox experiments. , 2006, Drug discovery today.

[327]  Ian A. Watson,et al.  Characteristic physical properties and structural fragments of marketed oral drugs. , 2004, Journal of medicinal chemistry.