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1
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10644293897
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Application of ALOGPS to predict 1-octanol/ water distribution coefficients, logP, and logD of AstraZeneca inhouse database
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Tetko, I. V.; Bruneau, P. Application of ALOGPS to Predict 1-Octanol/ Water Distribution Coefficients, logP, and logD of AstraZeneca Inhouse Database. J. Pharm. Sci. 2004, 93, 3103-3110.
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Tetko, I.V.1
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7444258512
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Application of ALOGPS 2.1 to predict logD distribution coefficient for pfizer proprietary compounds
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Tetko, I. V.; Poda, G. I. Application of ALOGPS 2.1 to Predict logD Distribution Coefficient for Pfizer Proprietary Compounds. J. Med. Chem. 2004, 47, 5601-5604.
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Tetko, I.V.1
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84956748673
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Prediction of physical properties
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Bohm, H. J., Schneider, G., Eds.; Wiley-VCH: Chichester
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Morris, J. J.; Bruneau, P. Prediction of Physical Properties. In Virtual Screening for Bioactiue Molecules; Bohm, H. J., Schneider, G., Eds.; Wiley-VCH: Chichester, 2000; pp 33-58
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Morris, J.J.1
Bruneau, P.2
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4
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0042416598
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In silico ADME/Tox: Why models fail
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Stouch, T. R.; Kenyon, J. R.; Johnson, S. R.; Xue-Qing; Doweyko, A.; Li Y. In Silico ADME/Tox: Why Models Fail. J. Comput.-Aided Mol. Des. 2003, 17, 83-92.
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Stouch, T.R.1
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Theoretical property predictions
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Livingstone, D.J.1
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8
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84858916022
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Advanced Chemical Development Inc., 133 Richmond Sreet West, Suite 605, Toronto, Ontario, Canada M5H 2L3 (www.acdlabs.com).
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10
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0043031339
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Predicting ADME properties and side effects: The BioPrint approach
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Krejsa, C. M.; Horwath, D.; Rogalski, S. L.; Penzotti, J. E.; Mao, B.; Barbosa, F.; Migeon, J. C. Predicting ADME Properties and Side Effects: The BioPrint Approach. Curr. Opin. Drug Discovery Dev. 2003, 6, 4.
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11
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84858916023
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http://www.lib.uchicago.edu/SCI/SCIpharm2004/2.2FrederiqueBar-bosa.pdf.
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12
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0035526164
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Search for predictive generic model of aqueous solubility using Bayesian neural nets
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Bruneau, P. Search for Predictive Generic Model of Aqueous Solubility Using Bayesian Neural Nets. J. Chem. Inf. Comput. Sci. 2001, 41, 1605-1616.
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Bruneau, P.1
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26444580313
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Distributed by SAS Institute Inc.
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JMP version 5.1.1. Distributed by SAS Institute Inc. http://www.JMP.com.
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JMP Version 5.1.1
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15
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12444281776
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Bayesian neural nets for modeling in drug discovery
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Winkler, D. A.; Burden, F. R. Bayesian Neural Nets for Modeling in Drug Discovery. DDT:Biosilico 2004, 2, 104-111.
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Winkler, D.A.1
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Robust QSAR models using Byesian regularized neural networks
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Burden, F.R.1
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Modelling blood-brain barrier partitioning using Bayesian neural nets
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Winkler, D. A.; Burden, F. R. Modelling Blood-Brain Barrier Partitioning Using Bayesian Neural Nets. J. Mol. Graphics Modell. 2004, 22, 499-505.
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Bayesian automatic relevance determination algorithms for classifying genetic expression data
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Li, Y.; Campbell, C.; Tipping, M. Bayesian Automatic Relevance Determination Algorithms for Classifying Genetic Expression Data. Bioinformatics 2002, 18, 1332-1339.
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Gaussian process: An efficient technique to solve quantitative structure - Property relationship problems
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Enot, D. P.; Gautier, R.; Le Marouillle, J. Y. Gaussian Process: An Efficient Technique to Solve Quantitative Structure - Property Relationship Problems. SAR QSAR. Environ. Res. 2001, 72, 461-469.
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Use of automatic relevance determination in QSAR studies using Bayesian neural nets
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A distance measure between models: A tool for similarity/diversity analysis of model populations
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Todeschini, R.; Consonni, V.; Pavan, M. A Distance Measure Between Models: A Tool for Similarity/Diversity Analysis of Model Populations. Chemom. Intell. Lab. Syst. 2004, 70, 55-61.
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Dimension related distance and its application in QSAR/QSPR model error estimation
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Xu, Y.; Gao, H. Dimension Related Distance and its Application in QSAR/QSPR Model Error Estimation. QSAR Comb. Sci. 2003, 22, 422-429.
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85128251229
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note
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i-ȳ) where y is a vector of values for a particular point, ȳ is the vector of means of each variable, and S is the covariance matrix of the variables.
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27
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33745681934
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Not published
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Not published.
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28
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84858923569
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Daylight Inc., Mission Viejo, California, USA.http://www.daylight.com/ dayhtml/doc/theory/theory.smarts.html.
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0036557849
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Neural network studies. 4. Introduction to associative neural networks
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Application of associative neural networks for prediction of lipophilicity in ALOGPS 2.1 program
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Tetko, I. V.; Tanchuk V. Y. Application of Associative Neural Networks for Prediction of Lipophilicity in ALOGPS 2.1 Program. J. Chem. Inf. Comput. Sci. 2002, 42, 1136-1145.
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Nonlinear prediction of quantitative structure - Activity relationships
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33745696212
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note
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A measurement is more likely repeated if an apparently abnormal result is obtained. Thus, high ranges in duplicated measurements do not indicate a global variability of the experimental methodology.
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33
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28944440134
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Studying the explanatory capacity of artificial neural networks for understanding environmental chemical quantitative structure - Activity relationship models
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ASAP, Web release October 13
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Yang, L.; Wang, P.; Jiang, Y.; Chen, J. Studying the Explanatory Capacity of Artificial Neural Networks for Understanding Environmental Chemical Quantitative Structure - Activity Relationship Models. J. Chem. Inf. Comput. Sci. ASAP, Web release October 13, 2005.
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Yang, L.1
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