메뉴 건너뛰기




Volumn 46, Issue 3, 2006, Pages 1379-1387

LogD7.4 modeling using Bayesian regularized neural networks. assessment and correction of the errors of prediction

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SIMULATION; ERROR CORRECTION; IONIZATION; MATHEMATICAL MODELS; NEURAL NETWORKS;

EID: 33745383499     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci0504014     Document Type: Article
Times cited : (64)

References (33)
  • 1
    • 10644293897 scopus 로고    scopus 로고
    • Application of ALOGPS to predict 1-octanol/ water distribution coefficients, logP, and logD of AstraZeneca inhouse database
    • 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.
    • (2004) J. Pharm. Sci. , vol.93 , pp. 3103-3110
    • Tetko, I.V.1    Bruneau, P.2
  • 2
    • 7444258512 scopus 로고    scopus 로고
    • Application of ALOGPS 2.1 to predict logD distribution coefficient for pfizer proprietary compounds
    • 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.
    • (2004) J. Med. Chem. , vol.47 , pp. 5601-5604
    • Tetko, I.V.1    Poda, G.I.2
  • 3
    • 84956748673 scopus 로고    scopus 로고
    • Prediction of physical properties
    • Bohm, H. J., Schneider, G., Eds.; Wiley-VCH: Chichester
    • 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
    • (2000) Virtual Screening for Bioactiue Molecules , pp. 33-58
    • Morris, J.J.1    Bruneau, P.2
  • 6
    • 0038282314 scopus 로고    scopus 로고
    • Theoretical property predictions
    • Livingstone, D. J. Theoretical Property Predictions. Curr. Topics Med. Chem. 2003, 3, 1171-1192.
    • (2003) Curr. Topics Med. Chem. , vol.3 , pp. 1171-1192
    • Livingstone, D.J.1
  • 7
    • 84858919645 scopus 로고    scopus 로고
    • PrologD. CompuDrug Chemistry Ltd. (www.compudrug.com).
  • 8
    • 84858916022 scopus 로고    scopus 로고
    • Advanced Chemical Development Inc., 133 Richmond Sreet West, Suite 605, Toronto, Ontario, Canada M5H 2L3 (www.acdlabs.com).
  • 11
    • 84858916023 scopus 로고    scopus 로고
    • http://www.lib.uchicago.edu/SCI/SCIpharm2004/2.2FrederiqueBar-bosa.pdf.
  • 12
    • 0035526164 scopus 로고    scopus 로고
    • Search for predictive generic model of aqueous solubility using Bayesian neural nets
    • Bruneau, P. Search for Predictive Generic Model of Aqueous Solubility Using Bayesian Neural Nets. J. Chem. Inf. Comput. Sci. 2001, 41, 1605-1616.
    • (2001) J. Chem. Inf. Comput. Sci. , vol.41 , pp. 1605-1616
    • Bruneau, P.1
  • 13
    • 26444580313 scopus 로고    scopus 로고
    • Distributed by SAS Institute Inc.
    • JMP version 5.1.1. Distributed by SAS Institute Inc. http://www.JMP.com.
    • JMP Version 5.1.1
  • 15
    • 12444281776 scopus 로고    scopus 로고
    • Bayesian neural nets for modeling in drug discovery
    • Winkler, D. A.; Burden, F. R. Bayesian Neural Nets for Modeling in Drug Discovery. DDT:Biosilico 2004, 2, 104-111.
    • (2004) DDT:Biosilico , vol.2 , pp. 104-111
    • Winkler, D.A.1    Burden, F.R.2
  • 16
    • 0033549850 scopus 로고    scopus 로고
    • Robust QSAR models using Byesian regularized neural networks
    • Burden, F. R.; Winkler, D. A. Robust QSAR Models Using Bayesian Regularized Neural Networks. J. Med. Chem. 1999, 42, 3183-3187.
    • (1999) J. Med. Chem. , vol.42 , pp. 3183-3187
    • Burden, F.R.1    Winkler, D.A.2
  • 19
    • 2942538155 scopus 로고    scopus 로고
    • Modelling blood-brain barrier partitioning using Bayesian neural nets
    • Winkler, D. A.; Burden, F. R. Modelling Blood-Brain Barrier Partitioning Using Bayesian Neural Nets. J. Mol. Graphics Modell. 2004, 22, 499-505.
    • (2004) J. Mol. Graphics Modell. , vol.22 , pp. 499-505
    • Winkler, D.A.1    Burden, F.R.2
  • 20
    • 0036772522 scopus 로고    scopus 로고
    • Bayesian automatic relevance determination algorithms for classifying genetic expression data
    • Li, Y.; Campbell, C.; Tipping, M. Bayesian Automatic Relevance Determination Algorithms for Classifying Genetic Expression Data. Bioinformatics 2002, 18, 1332-1339.
    • (2002) Bioinformatics , vol.18 , pp. 1332-1339
    • Li, Y.1    Campbell, C.2    Tipping, M.3
  • 21
    • 0035754780 scopus 로고    scopus 로고
    • Gaussian process: An efficient technique to solve quantitative structure - Property relationship problems
    • 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.
    • (2001) SAR QSAR. Environ. Res. , vol.72 , pp. 461-469
    • Enot, D.P.1    Gautier, R.2    Le Marouillle, J.Y.3
  • 22
    • 0001245212 scopus 로고    scopus 로고
    • Use of automatic relevance determination in QSAR studies using Bayesian neural nets
    • Burden, F. R.; Ford, M. G.; Witley, D. C.; Winkler, D. A. Use of Automatic Relevance Determination in QSAR Studies Using Bayesian Neural Nets. J. Chem. Inf. Comput. Sci. 2000, 40, 1423-1430.
    • (2000) J. Chem. Inf. Comput. Sci. , vol.40 , pp. 1423-1430
    • Burden, F.R.1    Ford, M.G.2    Witley, D.C.3    Winkler, D.A.4
  • 23
    • 0346500732 scopus 로고    scopus 로고
    • A distance measure between models: A tool for similarity/diversity analysis of model populations
    • 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.
    • (2004) Chemom. Intell. Lab. Syst. , vol.70 , pp. 55-61
    • Todeschini, R.1    Consonni, V.2    Pavan, M.3
  • 24
    • 0038443423 scopus 로고    scopus 로고
    • Dimension related distance and its application in QSAR/QSPR model error estimation
    • Xu, Y.; Gao, H. Dimension Related Distance and its Application in QSAR/QSPR Model Error Estimation. QSAR Comb. Sci. 2003, 22, 422-429.
    • (2003) QSAR Comb. Sci. , vol.22 , pp. 422-429
    • Xu, Y.1    Gao, H.2
  • 25
    • 10044263240 scopus 로고    scopus 로고
    • Kearsley similarity to molecules in the training set is a good discriminator for prediction accuracy in QSAR
    • Sheridan, R. P.; Feuston, B. P.; Maiorov, V. N.; Kearsley Similarity to Molecules in the Training Set Is a Good Discriminator for Prediction Accuracy in QSAR. J. Chem. Inf. Comput. Sci. 2004, 44, 1912-1928
    • (2004) J. Chem. Inf. Comput. Sci. , vol.44 , pp. 1912-1928
    • Sheridan, R.P.1    Feuston, B.P.2    Maiorov, V.N.3
  • 26
    • 85128251229 scopus 로고    scopus 로고
    • note
    • 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.
  • 27
    • 33745681934 scopus 로고    scopus 로고
    • Not published
    • Not published.
  • 28
    • 84858923569 scopus 로고    scopus 로고
    • Daylight Inc., Mission Viejo, California, USA.http://www.daylight.com/ dayhtml/doc/theory/theory.smarts.html.
  • 29
    • 0036557849 scopus 로고    scopus 로고
    • Neural network studies. 4. Introduction to associative neural networks
    • Tetko, I. V. Neural Network studies. 4. Introduction to Associative Neural Networks. J. Chem. Inf. Comput. Sci. 2002, 42, 717-728.
    • (2002) J. Chem. Inf. Comput. Sci. , vol.42 , pp. 717-728
    • Tetko, I.V.1
  • 30
    • 0036757804 scopus 로고    scopus 로고
    • Application of associative neural networks for prediction of lipophilicity in ALOGPS 2.1 program
    • 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.
    • (2002) J. Chem. Inf. Comput. Sci. , vol.42 , pp. 1136-1145
    • Tetko, I.V.1    Tanchuk, V.Y.2
  • 32
    • 33745696212 scopus 로고    scopus 로고
    • note
    • 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.
  • 33
    • 28944440134 scopus 로고    scopus 로고
    • Studying the explanatory capacity of artificial neural networks for understanding environmental chemical quantitative structure - Activity relationship models
    • ASAP, Web release October 13
    • 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.
    • (2005) J. Chem. Inf. Comput. Sci.
    • Yang, L.1    Wang, P.2    Jiang, Y.3    Chen, J.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.