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journal-article
Elsevier BV
Drug Discovery Today (78)
References
57
Referenced
63
10.2174/092986708783330566
/ Curr. Med. Chem. / Theoretical and practical considerations in virtual screening: a beaten field? by Kontoyianni (2008)10.2174/138920306778559377
/ Curr. Protein Pept. Sci. / Receptor-based computational screening of compound databases: the main docking-scoring engines by Sperandio (2006)10.1093/nar/gkl999
/ Nucleic Acids Res. / BindingDB: a web-accessible database of experimentally determined protein–ligand binding affinities by Liu (2007)10.1021/jm030580l
/ J. Med. Chem. / The PDBbind database: collection of binding affinities for protein–ligand complexes with known three-dimensional structures by Wang (2004)10.1093/nar/gkj067
/ Nucleic Acids Res. / DrugBank: a comprehensive resource for in silico drug discovery and exploration by Wishart (2006)- PubChem, National Center for Biotechnology Information (NCBI), National Library of Medicine, USA
10.1038/sj.bjp.0707515
/ Br. J. Pharmacol. / Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go by Moitessier (2008)10.1021/ci8001167
/ J. Chem. Inf. Model. / MedusaScore: an accurate force field-based scoring function for virtual drug screening by Yin (2008)10.1021/ci7004719
/ J. Chem. Inf. Model. / An improved PMF scoring function for universally predicting the interactions of a ligand with protein, DNA, and RNA by Zhao (2008)10.1016/j.drudis.2006.03.009
/ Drug Discov. Today / Consensus scoring for protein–ligand interactions by Feher (2006)10.1016/S1359-6446(05)03717-7
/ Drug Discov. Today / Structure-based development of target-specific compound libraries by Orry (2006)10.1002/prot.22058
/ Proteins / SFCscore: scoring functions for affinity prediction of protein–ligand complexes by Sotriffer (2008)10.1021/ci049733j
/ J. Chem. Inf. Comput. Sci. / An extensive test of 14 scoring functions using the PDBbind refined set of 800 protein–ligand complexes by Wang (2004)10.1021/jm050362n
/ J. Med. Chem. / A critical assessment of docking programs and scoring functions by Warren (2006)10.2174/138955708783331540
/ Mini Rev. Med. Chem. / Essential factors for successful virtual screening by Seifert (2008){'key': '10.1016/j.drudis.2009.03.013_bib16', 'series-title': 'The Elements of Statistical Learning: Data Mining, Inference and Prediction', 'author': 'Hastie', 'year': '2001'}
/ The Elements of Statistical Learning: Data Mining, Inference and Prediction by Hastie (2001)10.1016/S0004-3702(96)00034-3
/ Artif. Intell. / Solving the multiple instance problem with axis-parallel rectangles by Dietterich (1997)10.1021/jm049092j
/ J. Med. Chem. / Virtual screening workflow development guided by the “receiver operating characteristic” curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4 by Triballeau (2005)10.1021/ci7004548
/ J. Chem. Inf. Model. / AutoShim: empirically corrected scoring functions for quantitative docking with a crystal structure and IC50 training data by Martin (2008)10.1021/ci700455u
/ J. Chem. Inf. Model. / Surrogate AutoShim: predocking into a universal ensemble kinase receptor for three dimensional activity prediction, very quickly, without a crystal structure by Martin (2008)10.1021/ci7004669
/ J. Chem. Inf. Model. / AIScore: chemically diverse empirical scoring function employing quantum chemical binding energies of hydrogen-bonded complexes by Raub (2008)10.1021/ci800103u
/ J. Chem. Inf. Model. / BALLDock/SLICK: a new method for protein–carbohydrate docking by Kerzmann (2008)10.1021/ja055804z
/ J. Am. Chem. Soc. / Combination of a modified scoring function with two-dimensional descriptors for calculation of binding affinities of bulky, flexible ligands to proteins by Hetényi (2006)10.1021/ci7002472
/ J. Chem. Inf. Model. / Consensus adaptation of fields for molecular comparison (AFMoC) models incorporate ligand and receptor conformational variability into tailor-made scoring functions by Breu (2007)10.1021/ci8000452
/ J. Chem. Inf. Model. / Using buriedness to improve discrimination between actives and inactives in docking by O’Boyle (2008)10.1016/S1093-3263(03)00125-6
/ J. Mol. Graph. Model. / Analysis and optimization of structure-based virtual screening protocols. New methods and old problems in scoring function design by Smith (2003)10.1007/s10822-008-9174-y
/ J. Comput. Aided Mol. Des. / Customizing scoring functions for docking by Pham (2008)10.1021/jm050040j
/ J. Med. Chem. / Parameter estimation for scoring protein–ligand interactions using negative training data by Pham (2006)10.1148/radiology.148.3.6878708
/ Radiology / A method of comparing the areas under receiver operating characteristic curves derived from the same cases by Hanley (1983)10.1021/ci700345n
/ J. Chem. Inf. Model. / Optimizing the signal-to-noise ratio of scoring functions for protein–ligand docking by Seifert (2008)10.1021/jm0608356
/ J. Med. Chem. / Benchmarking sets for molecular docking by Huang (2006)10.1021/ci050036g
/ J. Chem. Inf. Model. / POEM: parameter optimization using ensemble methods: application to target specific scoring functions by Antes (2005)10.1021/ci6005596
/ J. Chem. Inf. Model. / A multivariate approach to investigate docking parameters’ effects on docking performance by Andersson (2007)10.1021/ci700116z
/ J. Chem. Inf. Model. / Supervised scoring models with docked ligand conformations for structure-based virtual screening by Teramoto (2007)10.1002/jcc.20504
/ J. Comput. Chem. / An iterative knowledge-based scoring function to predict protein–ligand interactions: I. Derivation of interaction potentials by Huang (2006)10.1002/jcc.20505
/ J. Comput. Chem. / An iterative knowledge-based scoring function to predict protein–ligand interactions: II. Validation of the scoring function by Huang (2006)10.1021/ci700134p
/ J. Chem. Inf. Model. / DrugScoreRNA – knowledge-based scoring function to predict RNA–ligand interactions by Pfeffer (2007)10.1021/ci600253e
/ J. Chem. Inf. Model. / A common reference framework for analyzing/comparing proteins and ligands. Fingerprints for ligands and proteins (FLAP): theory and application by Baroni (2007)10.1021/jm800710x
/ J. Med. Chem. / Selective structure-based virtual screening for full and partial agonists of the beta2 adrenergic receptor by de Graaf (2008)10.1021/ci8000265
/ J. Chem. Inf. Model. / Homology model-based virtual screening for GPCR ligands using docking and target-biased scoring by Radestock (2008)10.1021/ci8003607
/ J. Chem. Inf. Model. / Knowledge based identification of potent antitubercular compounds using structure based virtual screening and structure interaction fingerprints by Kumar (2009)10.1021/jm701367r
/ J. Med. Chem. / Development and experimental validation of a docking strategy for the generation of kinase-targeted libraries by Gozalbes (2008)10.1021/jm0700262
/ J. Med. Chem. / Target specific virtual screening: optimization of an estrogen receptor screening platform by Knox (2007)10.1007/s10822-008-9197-4
/ J. Comput. Aided Mol. Des. / Special issue on evaluation of computational methods by Stouch (2008)10.1007/s10822-007-9166-3
/ J. Comput. Aided Mol. Des. / How to do an evaluation: pitfalls and traps by Hawkins (2008)10.1021/ci034289q
/ J. Chem. Inf. Comput. Sci. / Virtual screening using protein–ligand docking: avoiding artificial enrichment by Verdonk (2004){'key': '10.1016/j.drudis.2009.03.013_bib47', 'series-title': 'Statistik', 'author': 'Bortz', 'year': '2005'}
/ Statistik by Bortz (2005)- Eldred, M.S. et al. (2006) DAKOTA, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis: Version 4.0 Developers Manual, Sandia Technical Report SAND2006-4056. URL http://www.cs.sandia.gov/DAKOTA/index.html (accessed Sep 18, 2008)
10.1023/A:1008306431147
/ J. Global Optim. / Efficient global optimization of expensive black-box functions by Jones (2004){'key': '10.1016/j.drudis.2009.03.013_bib50', 'first-page': '444', 'article-title': 'Binary QSAR: a new method for the determination of quantitative structure activity relationships', 'author': 'Labute', 'year': '1999', 'journal-title': 'Pac. Symp. Biocomput.'}
/ Pac. Symp. Biocomput. / Binary QSAR: a new method for the determination of quantitative structure activity relationships by Labute (1999)10.1007/s10822-008-9170-2
/ J. Comput. Aided Mol. Des. / What do we know and when do we know it? by Nicholls (2008)10.1109/4235.585893
/ IEEE Trans. Evol. Comput. / No free lunch theorems for optimization by Wolpert (1997){'key': '10.1016/j.drudis.2009.03.013_bib53', 'series-title': 'Proceedings of the 11th International Conference on Machine Learning', 'first-page': '259', 'article-title': 'A conservation law for generalization performance', 'author': 'Schaffer', 'year': '1994'}
/ Proceedings of the 11th International Conference on Machine Learning / A conservation law for generalization performance by Schaffer (1994)10.1007/BF00941892
/ J. Optim. Theory Appl. / Lipschitzian optimization without the lipschitz constant by Jones (1993)10.1023/A:1017930332101
/ J. Global Optim. / A locally-biased form of the DIRECT algorithm by Gablonsky (2001)- ROCKIT, Version 1.1b (2007) Kurt Rossmann Laboratories for Radiological Image Research, University of Chicago. http://www-radiology.uchicago.edu/krl/roc_soft6.html (accessed Sep 18, 2009)
10.1021/jm0303195
/ J. Med. Chem. / Classification of kinase inhibitors using a Bayesian model by Xia (2004)
Dates
Type | When |
---|---|
Created | 16 years, 4 months ago (April 7, 2009, 6:21 a.m.) |
Deposited | 3 years, 10 months ago (Oct. 3, 2021, 8:59 p.m.) |
Indexed | 4 weeks, 1 day ago (Aug. 2, 2025, 1:06 a.m.) |
Issued | 16 years, 2 months ago (June 1, 2009) |
Published | 16 years, 2 months ago (June 1, 2009) |
Published Print | 16 years, 2 months ago (June 1, 2009) |