What has virtual screening ever done for drug discovery?

Bibliography

• Clark DE. What has computer-aided molecular design ever done for drug discovery? Expert Opin Drug Discov 2006;1:103-10
   PubMed Web of Science ®Google Scholar
• Horvath D. A virtual screening approach applied to the search for trypanothione reductase inhibitors. J Med Chem 1997;40:2412-23
   PubMed Web of Science ®Google Scholar
• Bruns RF, Simmons RMA, Howbert JJ, et al. Virtual screening as a tool for evaluating chemical libraries. Presented at Small Molecule Libraries for Drug Discovery; 23 – 25 January 1995; La Jolla, CA, USA
   Google Scholar
• Walters WP, Stahl MT, Murcko MA. Virtual screening – an overview. Drug Discov Today 1998;3:160-78
   Web of Science ®Google Scholar
• Kontoyianni M, Madhav P, Suchanek E, Seibel W. Theoretical and practical considerations in virtual screening: a beaten field? Curr Med Chem 2008;15:107-16
   PubMed Web of Science ®Google Scholar
• Seifert MHJ, Lang M. Essential factors for successful virtual screening. Mini Rev Med Chem 2008;8:63-72
   PubMed Web of Science ®Google Scholar
• Cavasotto CN, Orry AJW. Ligand docking and structure-based virtual screening in drug discovery. Curr Top Med Chem 2007;7:1006-14
   PubMed Web of Science ®Google Scholar
• Muegge I, Oloff S. Advances in virtual screening. Drug Discov Today Technol 2006;3:405-11
   Google Scholar
• McGaughey GB, Sheridan RP, Bayly CI, et al. Comparison of topological, shape, and docking methods in virtual screening. J Chem Inf Model 2007;47:1504-19
   PubMed Web of Science ®Google Scholar
• Data from Prous Integrity database. Available from: www.prous.com
   Google Scholar
• Hartman GD, Egbertson MS, Halczenko W, et al. Non-peptide fibrinogen receptor antagonists. 1. Discovery and design of exosite inhibitors. J Med Chem 1992;35:4640-52
   PubMed Web of Science ®Google Scholar
• de Paulis T. Drug evaluation: PRX-00023, a selective 5-HT1A receptor agonist for depression. Curr Opin Investig Drugs 2007;8:78-86
   PubMed Web of Science ®Google Scholar
• Iyer GR, Reinhard JF Jr, Oshana S, et al. Tolerability, pharmacokinetics, and neuroendocrine effects of PRX-00023, a novel 5-HT1A agonist, in healthy subjects. J Clin Pharmacol 2007;47:817-24
   PubMed Web of Science ®Google Scholar
• EPIX Pharmaceuticals Press Release, 20 March 2008. Available from: http://investor.epixpharma.com/phoenix.zhtml?c=91717&p=irol-newsArticle&ID=1120491&highlight=
   Google Scholar
• Becker OM, Dhanoa DS, Marantz Y, et al. An integrated in silico 3D model-driven discovery of a novel, potent, and selective amidosulfonamide 5-HT1A agonist (PRX-00023) for the treatment of anxiety and depression J Med Chem 2006;49:3116-35
   PubMed Web of Science ®Google Scholar
• Becker OM, Marantz Y, Shacham S, et al. G protein-coupled receptors: in silico drug discovery in 3D. Proc Natl Acad Sci USA 2004;101:11304-9
   PubMed Web of Science ®Google Scholar
• Shacham S, Marantz Y, Bar-Haim S, et al. PREDICT modeling and in silico screening for G-protein coupled receptors. Proteins 2004;57:51-86
   PubMed Web of Science ®Google Scholar
• Shacham S, Topf M, Avisar N, et al. Modeling the 3D structure of GPCRs from sequence. Med Res Rev 2001;21:472-83
   PubMed Web of Science ®Google Scholar
• Ewing TJA, Makino S, Skillman AG, Kuntz ID. DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases. J Comput Aided Mol Des 2001;15:411-28
   PubMed Web of Science ®Google Scholar
• Saha AK, Becker OM, Noiman S, et al. PRX-03140: the discovery and development of a novel 5-HT4 partial agonist for the treatment of Alzheimer's disease [MEDI-201]. Abstracts of Papers, 231st ACS National Meeting; 26 – 30 March 2006; Atlanta, GA, USA
   Google Scholar
• EPIX Pharmaceuticals Press Release, 5 May 2008. Available from: http://investor.epixpharma.com/phoenix.zhtml?c=91717&p=irol-newsArticle&ID=1139418&highlight=
   Google Scholar
• Dhanoa DS, Noiman S, Becker O, et al. Preparation of piperazinylindoles as serotonin modulating compounds. WO2004/080407
   Google Scholar
• Dhanoa DS, Chen D, Becker O, et al. Preparation of arylpiperazinyl sulfonamides as 5-HT, in particular 5-HT1, receptor agonists and antagonists for treating CNS disorders, especially anxiety and related diseases. WO2004/069794
   Google Scholar
• EPIX Pharmaceuticals Press Release, 7 August 2007. Available from: http://investor.epixpharma.com/phoenix.zhtml?c=91717&p=irol-newsArticle&ID=1037446&highlight=
   Google Scholar
• Dhanoa DS, Becker O, Noiman S, et al. Preparation of piperidinylamino(benzo)thienopyrimidines as serotonin 5-HT2 receptor ligands. WO2004/089312
   Google Scholar
• Seifert MHJ, Schmitt F, Herz T, Kramer B. ProPose: a docking engine based on a fully configurable protein-ligand interaction model. J Mol Model 2004;10:342-57
   PubMed Web of Science ®Google Scholar
• Seifert MHJ. ProPose: steered virtual screening by simultaneous protein-ligand docking and ligand-ligand alignment. J Chem Inf Model 2005;45:449-60
   PubMed Web of Science ®Google Scholar
• Leban J, Saeb W, Garcia G, et al. Discovery of a novel series of DHODH inhibitors by a docking procedure and QSAR refinement. Bioorg Med Chem Lett 2004;14:55-8
   PubMed Web of Science ®Google Scholar
• Leban J, Kralik M, Mies J, et al. species specificity, and cellular activity of cyclopentene dicarboxylic acid amides as DHODH inhibitors. Bioorg Med Chem Lett 2005;15:4854-7
   PubMed Web of Science ®Google Scholar
• Leban J, Kralik M, Mies J, et al. Biphenyl-4-ylcarbamoyl thiophenecarboxylic acids as potent DHODH inhibitors. Bioorg Med Chem Lett 2006;16:267-70
   PubMed Web of Science ®Google Scholar
• Baumgartner R, Walloschek M, Kralik M, et al. Dual binding mode of a novel series of DHODH inhibitors. J Med Chem 2006;49:1239-47
   PubMed Web of Science ®Google Scholar
• Kramer B, Leban J. Preparation of 2-arylcarbamoyl-cyclopent-1-enecarboxylic acids as anti-inflammatory, immunomodulatory and anti-proliferatory agents. WO2003/006424
   Google Scholar
• Leban J, Kralik M. A preparation of cycloalkenedicarboxylic acid derivatives, useful as dihydroorotate dehydrogenase (DHODH) inhibitors. WO2004/056746
   Google Scholar
• Leban J, Kramer B, Baumgartner R, et al. Dihydroorotate dehydrogenase (DHODH) inhibitors and method for their identification. WO2004/056747
   Google Scholar
• Leban J, Tasler S. Preparation of aromatic compounds as anti-inflammatory, immunomodulatory and antiproliferative agents. WO2004/056797
   Google Scholar
• 4SC Press Release, 29 November 2007. Available from: http://www.4sc.de/en/index.php?csrc=http://www.4sc.de/en/products/c_products_01.html
   Google Scholar
• Lam PYS, Jadhav PK, Eyermann CJ, et al. Rational design of potent, bioavailable, nonpeptide cyclic ureas as HIV protease inhibitors. Science 1994;263:380-4
   PubMed Web of Science ®Google Scholar
• Eyermann CJ, Jadhav PK, Hodge CN, et al. The role of computer-aided and structure-based design techniques in the discovery and optimization of cyclic urea inhibitors of HIV protease. Adv Amino Acid Mimetics Peptidomimetics 1997;1:1-40
   Google Scholar
• Vedananda TR, Sabio M, Stanton J, et al. From virtual screening to clinic: discovery of cevoglitazar [MEDI-022]. Abstracts of Papers, 235th ACS National Meeting; 6 – 10 April 2008; New Orleans, LA, USA
   Google Scholar
• EPIX Pharmaceuticals Press Release, 18 April 2007. Available from: http://investor.epixpharma.com/phoenix.zhtml?c=91717&p=irol-newsArticle&ID=986761&highlight=
   Google Scholar
• EPIX Pharmaceuticals Press Release, 29 October 2007. Available from: http://investor.epixpharma.com/phoenix.zhtml?c=91717&p=irol-newsArticle&ID=1068749&highlight=
   Google Scholar
• Becker O, Lobera M, Melendez RE, et al. Preparation of piperazinylarylamines as 5-HT6 modulators. WO2006/081332
   Google Scholar
• Rajamani R, Good AC. Ranking poses in structure-based lead discovery and optimization: current trends in scoring function development. Curr Opin Drug Discov Dev 2007;10:308-15
   PubMed Web of Science ®Google Scholar
• Jain AN. Scoring functions for protein-ligand docking. Curr Protein Pept Sci 2006;7:407-20
   PubMed Web of Science ®Google Scholar
• Friesner RA, Murphy RB, Repasky MP, et al. Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J Med Chem 2006;49:6177-96
   PubMed Web of Science ®Google Scholar
• Huang N, Jacobson MP. Physics-based methods for studying protein-ligand interactions. Curr Opin Drug Discov Dev 2007;10:325-31
   PubMed Web of Science ®Google Scholar
• Brown SP, Muchmore SW. Rapid estimation of relative protein-ligand binding affinities using a high-throughput version of MM-PBSA. J Chem Inf Model 2007;47:1493-503
   PubMed Web of Science ®Google Scholar
• Lyne PD, Lamb ML, Saeh JC. Accurate prediction of the relative potencies of members of a series of kinase inhibitors using molecular docking and MM-GBSA scoring. J Med Chem 2006;49:4805-8
   PubMed Web of Science ®Google Scholar
• Klon AE, Glick M, Thoma M, et al. Finding more needles in the haystack: a simple and efficient method for improving high-throughput docking results. J Med Chem 2004;47:2743-9
   PubMed Web of Science ®Google Scholar
• Klon AE, Glick M, Davies JW. Application of machine learning to improve the results of high-throughput docking against the HIV-1 protease. J Chem Inf Comput Sci 2004;44:2216-24
   PubMedGoogle Scholar
• Cavasotto CN, Orry AJW, Abagyan RA. The challenge of considering receptor flexibility in ligand docking and virtual screening. Curr Comput Aided Drug Des 2005;1:423-40
   Web of Science ®Google Scholar
• Barril X, Fradera X. Incorporating protein flexibility into docking and structure-based drug design. Expert Opin Drug Discov 2006;1:335-49
   PubMed Web of Science ®Google Scholar
• Zavodszky MI, Kuhn LA. Side-chain flexibility in protein-ligand binding: The minimal rotation hypothesis. Protein Sci 2005;14:1104-14
   Google Scholar
• Claussen H, Buning C, Rarey M, Lengauer T. FlexE: efficient molecular docking considering protein structure variations. J Mol Biol 2001;308:377-95
   PubMed Web of Science ®Google Scholar
• Cavasotto CN, Abagyan RA. Protein flexibility in ligand docking and virtual screening to protein kinases. J Mol Biol 2004;337:209-25
   PubMed Web of Science ®Google Scholar
• Kovacs JA, Cavasotto CN, Abagyan R. Conformational sampling of protein flexibility in generalized coordinates: application to ligand docking. J Comput Theor Nanosci 2005;2:354-61
   Web of Science ®Google Scholar
• Sherman W, Day T, Jacobson MP, et al. Novel procedure for modeling ligand/receptor induced fit effects. J Med Chem 2006;49:534-53
   PubMed Web of Science ®Google Scholar
• Bottegoni G, Kufareva I, Totrov M, Abagyan R. A new method for ligand docking to flexible receptors by dual alanine scanning and refinement (SCARE). J Comput Aided Mol Des 2008;22:311-25
   PubMedGoogle Scholar
• Li Z, Lazaridis T. Water at biomolecular binding interfaces. Phys Chem Chem Phys 2007;9:573-81
   PubMed Web of Science ®Google Scholar
• Levy Y, Onuchic JN. Water mediation in protein folding and molecular recognition. Ann Rev Biophys Biomol Struct 2006;35:389-415
   PubMedGoogle Scholar
• Mancera RL. Molecular modeling of hydration in drug design. Curr Opin Drug Discov Dev 2007;10:275-80
   PubMed Web of Science ®Google Scholar
• Roberts BC, Mancera RL. Ligand-protein docking with water molecules. J Chem Inf Model 2008;48:397-408
   PubMed Web of Science ®Google Scholar
• Ladbury JE. Just add water! The effect of water on the specificity of protein-ligand binding sites and its potential application to drug design. Chem Biol 1996;3:973-80
   PubMed Web of Science ®Google Scholar
• Homans S. Water W. water everywhere – except where it matters? Drug Discov Today 2007;12:534-9
   PubMed Web of Science ®Google Scholar
• Raymer ML, Sanschagrin PC, Punch WF, et al. Predicting conserved water-mediated and polar ligand interactions in protein using a K-nearest-neighbors genetic algorithm. J Mol Biol 1997;265:445-64
   PubMed Web of Science ®Google Scholar
• Garcia-Sosa AT, Mancera RL, Dean PM. WaterScore: a novel method for distinguishing between bound and displaceable water molecules in the crystal structure of the binding site of protein-ligand complexes. J Mol Model 2003;9:172-82
   PubMed Web of Science ®Google Scholar
• Barillari C, Taylor J, Viner R, Essex JW. Classification of water molecules in protein binding sites. J Am Chem Soc 2007;129:2577-87
   PubMed Web of Science ®Google Scholar
• Amadasi A, Surface JA, Spyrakis F, et al. Robust classification of “relevant” water molecules in putative protein binding sites. J Med Chem 2008;51:1063-7
   PubMed Web of Science ®Google Scholar
• Lu Y, Wang R, Yang C-Y, Wang S. Analysis of ligand-bound water molecules in high-resolution crystal structures of protein-ligand complexes. J Chem Inf Model 2007;47:668-75
   PubMed Web of Science ®Google Scholar
• Westhead DR, Clark DE, Murray CW. A comparison of heuristic search algorithms for molecular docking. J Comput Aided Mol Des 1997;11:209-28
   PubMed Web of Science ®Google Scholar
• Rarey M, Kramer B, Lengauer T. The particle concept: placing discrete water molecules during protein-ligand docking predictions. Proteins 1999;34:17-28
   PubMed Web of Science ®Google Scholar
• Verdonk ML, Chessari G, Cole JC, et al. Modeling water molecules in protein-ligand docking using GOLD. J Med Chem 2005;48:6504-15
   PubMed Web of Science ®Google Scholar
• Corbeil CR, Englebienne P, Moitessier N. Docking ligands into flexible and solvated macromolecules. 1. Development and validation of FITTED 1.0. J Chem Inf Model 2007;47:435-49
   PubMed Web of Science ®Google Scholar
• Davis AM, Teague SJ, Kleywegt GJ. Application and limitations of X-ray crystallographic data in structure-based ligand and drug design. Angew Chem Int Ed 2003;42:2718-36
   PubMed Web of Science ®Google Scholar
• Deng Z, Chuaqui C, Singh J. Structural Interaction Fingerprint (SIFt): a novel method for analyzing three-dimensional protein-ligand binding interactions. J Med Chem 2004;47:337-44
   PubMed Web of Science ®Google Scholar
• Waszkowycz B. Towards improving data analysis and compound selection in structure-based virtual screening. Drug Discov Today 2008;13:219-26
   PubMed Web of Science ®Google Scholar
• Hawkins PCD, Skillman AG, Nicholls A. Comparison of shape-matching and docking as virtual screening tools. J Med Chem 2007;50:74-82
   PubMed Web of Science ®Google Scholar
• Sheridan RP. Chemical similarity searches: when is complexity justified? Expert Opin Drug Discov 2007;2:423-30
   PubMed Web of Science ®Google Scholar
• Good AC, Oprea TI. Optimization of CAMD techniques 3. Virtual screening enrichment studies: a help or hindrance in tool selection? J Comput Aided Mol Des 2008;22:169-78
   PubMed Web of Science ®Google Scholar
• Cheeseright T, Mackey M, Rose S, Vinter A. Molecular field technology applied to virtual screening and finding the bioactive conformation. Expert Opin Drug Discov 2007;2:131-44
   PubMed Web of Science ®Google Scholar
• Moffat K, Gillet VJ, Whittle M, et al. A comparison of field-based similarity searching methods: CatShape, FBSS, and ROCS. J Chem Inf Model 2008;48:719-29
   PubMed Web of Science ®Google Scholar
• Wolber G, Seidel T, Bendix F, Langer T. Molecule-pharmacophore superpositioning and pattern matching in computational drug design. Drug Discov Today 2008;13:23-9
   PubMed Web of Science ®Google Scholar
• Dixon SL, Smondyrev AM, Knoll EH, et al. PHASE: a new design for phamacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results. J Comput Aided Mol Des 2006;20:647-71
   PubMed Web of Science ®Google Scholar
• Catalyst is now integrated within the Discovery Studio environment developed and marketed by Accelrys, Inc. Available from: http://accelrys.com/products/discovery-studio/pharmacophores/index.html
   Google Scholar
• Wolpert DH, Macready WG. No free lunch theorems for optimisation. IEEE Trans Evol Comput 1997;1:67-82
   Google Scholar