Computational Approaches to Drug Discovery and Development

[1]  J. Bajorath,et al.  Docking and scoring in virtual screening for drug discovery: methods and applications , 2004, Nature Reviews Drug Discovery.

[2]  Karla K Kopec,et al.  Target identification and validation in drug discovery: the role of proteomics. , 2005, Biochemical pharmacology.

[3]  Y.Z. Chen,et al.  Ligand–protein inverse docking and its potential use in the computer search of protein targets of a small molecule , 2001, Proteins.

[4]  Yu Zhao,et al.  Chemical components from Ceratostigma willmottianum , 1997 .

[5]  Jian Zhang,et al.  Focused combinatorial library design based on structural diversity, druglikeness and binding affinity score. , 2005, Journal of combinatorial chemistry.

[6]  P. Brown,et al.  Drug target validation and identification of secondary drug target effects using DNA microarrays , 1998, Nature Medicine.

[7]  J. Drews Drug discovery: a historical perspective. , 2000, Science.

[8]  G. Klebe Virtual ligand screening: strategies, perspectives and limitations , 2006, Drug Discovery Today.

[9]  Brian K. Shoichet,et al.  Virtual screening of chemical libraries , 2004, Nature.

[10]  Chris de Graaf,et al.  Cytochrome p450 in silico: an integrative modeling approach. , 2005, Journal of medicinal chemistry.

[11]  Anirban Dutta,et al.  In Silico Identification of Potential Therapeutic Targets in the Human Pathogen Helicobacter Pylori , 2006, Silico Biol..

[12]  Brian K Shoichet,et al.  Prediction of protein-ligand interactions. Docking and scoring: successes and gaps. , 2006, Journal of medicinal chemistry.

[13]  G. Grass,et al.  Simulation models to predict oral drug absorption from in vitro data , 1997 .

[14]  Yang Li,et al.  Structure-based discovery of potassium channel blockers from natural products: virtual screening and electrophysiological assay testing. , 2003, Chemistry & biology.

[15]  G L Amidon,et al.  Transport approaches to the biopharmaceutical design of oral drug delivery systems: prediction of intestinal absorption. , 1996, Advanced drug delivery reviews.

[16]  Berith F. Jensen,et al.  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. , 2007, Journal of medicinal chemistry.

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

[18]  R. W. Hansen,et al.  The price of innovation: new estimates of drug development costs. , 2003, Journal of health economics.

[19]  N. Paul,et al.  Recovering the true targets of specific ligands by virtual screening of the protein data bank , 2004, Proteins.

[20]  B. Stockwell Exploring biology with small organic molecules , 2004, Nature.

[21]  V. Poroikov,et al.  Prediction of biological activity spectra for substances: evaluation on the diverse sets of drug-like structures. , 2003, Current medicinal chemistry.

[22]  B Agoram,et al.  Predicting the impact of physiological and biochemical processes on oral drug bioavailability. , 2001, Advanced drug delivery reviews.

[23]  Xiaomin Luo,et al.  Mutagenic probability estimation of chemical compounds by a novel molecular electrophilicity vector and support vector machine , 2006, Bioinform..

[24]  Xiaomin Luo,et al.  PDTD: a web-accessible protein database for drug target identification , 2008, BMC Bioinformatics.

[25]  J M Blaney,et al.  A geometric approach to macromolecule-ligand interactions. , 1982, Journal of molecular biology.

[26]  David S Wishart,et al.  Computational systems biology in drug discovery and development: methods and applications. , 2007, Drug discovery today.

[27]  Edgar Jacoby,et al.  Annotating and mining the ligand-target chemogenomics knowledge space , 2004 .

[28]  Y. Kurogi,et al.  Discovery of novel mesangial cell proliferation inhibitors using a three-dimensional database searching method. , 2001, Journal of medicinal chemistry.

[29]  Campbell McInnes,et al.  Virtual screening strategies in drug discovery. , 2007, Current opinion in chemical biology.

[30]  Davide Provasi,et al.  Design of HIV‐1‐PR inhibitors that do not create resistance: Blocking the folding of single monomers , 2005, Protein science : a publication of the Protein Society.

[31]  Jeffrey P. Jones,et al.  Line-walking method for predicting the inhibition of P450 drug metabolism. , 2006, Journal of medicinal chemistry.

[32]  Thierry Lavé,et al.  Prediction of intestinal absorption: comparative assessment of GASTROPLUS and IDEA. , 2002, European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences.

[33]  D. Lauffenburger,et al.  Applying computational modeling to drug discovery and development. , 2006, Drug discovery today.

[34]  A. Hopkins,et al.  The druggable genome , 2002, Nature Reviews Drug Discovery.

[35]  Jian Zhang,et al.  Peptide deformylase is a potential target for anti‐Helicobacter pylori drugs: Reverse docking, enzymatic assay, and X‐ray crystallography validation , 2006, Protein science : a publication of the Protein Society.

[36]  Honglin Li,et al.  GAsDock: a new approach for rapid flexible docking based on an improved multi-population genetic algorithm. , 2004, Bioorganic & medicinal chemistry letters.

[37]  Christopher R. Corbeil,et al.  Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go , 2008, British journal of pharmacology.

[38]  David E. Golan,et al.  Protein therapeutics: a summary and pharmacological classification , 2008, Nature Reviews Drug Discovery.

[39]  G Tiana,et al.  HIV-1 protease folding and the design of drugs which do not create resistance. , 2008, Current opinion in structural biology.

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

[41]  Thierry Langer,et al.  LigandScout: 3-D Pharmacophores Derived from Protein-Bound Ligands and Their Use as Virtual Screening Filters , 2005, J. Chem. Inf. Model..

[42]  Xiaomin Luo,et al.  TarFisDock: a web server for identifying drug targets with docking approach , 2006, Nucleic Acids Res..

[43]  Serge Batalov,et al.  The promise of genomics to identify novel therapeutic targets , 2004, Expert opinion on therapeutic targets.

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

[45]  Scott Markel,et al.  In Silico Technologies in Drug Target Identification and Validation , 2006 .

[46]  A. Fliri,et al.  Biospectra analysis: model proteome characterizations for linking molecular structure and biological response. , 2005, Journal of medicinal chemistry.

[47]  Vladimir Poroikov,et al.  PASS: prediction of activity spectra for biologically active substances , 2000, Bioinform..

[48]  L. Meijer,et al.  Inverse in silico screening for identification of kinase inhibitor targets. , 2007, Chemistry & biology.

[49]  Olivier Sperandio,et al.  Free resources to assist structure-based virtual ligand screening experiments. , 2007, Current protein & peptide science.

[50]  Lars Carlsson,et al.  State-of-the-art Tools for Computational Site of Metabolism Predictions: Comparative Analysis, Mechanistical Insights, and Future Applications , 2007, Drug metabolism reviews.

[51]  Gisbert Schneider,et al.  Computer-based de novo design of drug-like molecules , 2005, Nature Reviews Drug Discovery.

[52]  J. García-Lara,et al.  Staphylococcus aureus: the search for novel targets. , 2005, Drug discovery today.

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

[54]  Li Zhang,et al.  One novel quinoxaline derivative as a potent human cyclophilin A inhibitor shows highly inhibitory activity against mouse spleen cell proliferation , 2006, Bioorganic & Medicinal Chemistry.

[55]  R. Sheridan,et al.  Empirical regioselectivity models for human cytochromes P450 3A4, 2D6, and 2C9. , 2007, Journal of medicinal chemistry.

[56]  Johann Gasteiger,et al.  Ligand-Based Models for the Isoform Specificity of Cytochrome P450 3A4, 2D6, and 2C9 Substrates , 2007, J. Chem. Inf. Model..

[57]  Marc Adenot,et al.  Blood-Brain Barrier Permeation Models: Discriminating between Potential CNS and Non-CNS Drugs Including P-Glycoprotein Substrates , 2004, J. Chem. Inf. Model..

[58]  M T D Cronin,et al.  Structure-Based Methods for the Prediction of the Dominant P450 Enzyme in Human Drug Biotransformation: Consideration of CYP3A4, CYP2C9, CYP2D6 , 2005, SAR and QSAR in environmental research.

[59]  J. An,et al.  Structure-based virtual screening of chemical libraries for drug discovery. , 2006, Current opinion in chemical biology.

[60]  Prabha Garg,et al.  In Silico Prediction of Blood Brain Barrier Permeability: An Artificial Neural Network Model , 2006, J. Chem. Inf. Model..

[61]  B. Shoichet,et al.  Molecular docking and high-throughput screening for novel inhibitors of protein tyrosine phosphatase-1B. , 2002, Journal of medicinal chemistry.

[62]  Teruki Honma,et al.  Recent advances in de novo design strategy for practical lead identification , 2003, Medicinal research reviews.

[63]  Christoph Helma,et al.  In silico predictive toxicology: the state-of-the-art and strategies to predict human health effects. , 2005, Current opinion in drug discovery & development.

[64]  Y. Z. Chen,et al.  Prediction of potential toxicity and side effect protein targets of a small molecule by a ligand-protein inverse docking approach. , 2001, Journal of molecular graphics & modelling.

[65]  Maxime Culot,et al.  Modelling of the blood–brain barrier in drug discovery and development , 2007, Nature Reviews Drug Discovery.

[66]  Chun-Ming Huang,et al.  Proteomics Reveals that Proteins Expressed During the Early Stage of Bacillus anthracis Infection Are Potential Targets for the Development of Vaccines and Drugs , 2004, Genomics, proteomics & bioinformatics.

[67]  Olivier Sperandio,et al.  Receptor-based computational screening of compound databases: the main docking-scoring engines. , 2006, Current protein & peptide science.

[68]  Weiliang Zhu,et al.  Discovering potassium channel blockers from synthetic compound database by using structure-based virtual screening in conjunction with electrophysiological assay. , 2007, Journal of medicinal chemistry.

[69]  Jian Zhang,et al.  Strategy for discovering chemical inhibitors of human cyclophilin a: focused library design, virtual screening, chemical synthesis and bioassay. , 2006, Journal of combinatorial chemistry.

[70]  Thierry Langer,et al.  Molecule-pharmacophore superpositioning and pattern matching in computational drug design. , 2008, Drug discovery today.

[71]  Jianpeng Ma,et al.  Conformational transition of amyloid β-peptide , 2005 .

[72]  Jianpeng Ma,et al.  Inhibitor discovery targeting the intermediate structure of beta-amyloid peptide on the conformational transition pathway: implications in the aggregation mechanism of beta-amyloid peptide. , 2006, Biochemistry.