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Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection.
Golbraikh A, Tropsha A. Golbraikh A, et al. J Comput Aided Mol Des. 2002 May-Jun;16(5-6):357-69. doi: 10.1023/a:1020869118689. J Comput Aided Mol Des. 2002. PMID: 12489684
The latter can be defined as the ability of a model to predict accurately the target property (e.g., biological activity) of compounds that were not used for model development. ...For quantitative description of these criteria, we use molecular dataset diversity indices in …
The latter can be defined as the ability of a model to predict accurately the target property (e.g., biological activity) of compound …
QSAR modeling using chirality descriptors derived from molecular topology.
Golbraikh A, Tropsha A. Golbraikh A, et al. J Chem Inf Comput Sci. 2003 Jan-Feb;43(1):144-54. doi: 10.1021/ci025516b. J Chem Inf Comput Sci. 2003. PMID: 12546547
To circumvent this problem, we recently introduced chirality descriptors derived from molecular graphs and applied them in QSAR studies of ecdysteroids (Golbraikh A.; Bonchev, D.; Tropsha, A. J. Chem. Inf. Comput. Sci. 2001,41, 147-158). In this paper, we ext …
To circumvent this problem, we recently introduced chirality descriptors derived from molecular graphs and applied them in QSAR studies of e …
Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection.
Golbraikh A, Tropsha A. Golbraikh A, et al. Mol Divers. 2002;5(4):231-43. doi: 10.1023/a:1021372108686. Mol Divers. 2002. PMID: 12549674
The latter can be defined as the ability of a model to predict accurately the target property (e.g., biological activity) of compounds that were not used for model development. ...For quantitative description of these criteria, we use molecular dataset diversity indices in …
The latter can be defined as the ability of a model to predict accurately the target property (e.g., biological activity) of compound …
Rational selection of training and test sets for the development of validated QSAR models.
Golbraikh A, Shen M, Xiao Z, Xiao YD, Lee KH, Tropsha A. Golbraikh A, et al. J Comput Aided Mol Des. 2003 Feb-Apr;17(2-4):241-53. doi: 10.1023/a:1025386326946. J Comput Aided Mol Des. 2003. PMID: 13677490
Using k nearest neighbors (kNN) variable selection QSAR method for the analysis of several datasets, we have demonstrated recently that the widely accepted leave-one-out (LOO) cross-validated R2 (q2) is an inadequate characteristic to assess the predictive ability of the models [ …
Using k nearest neighbors (kNN) variable selection QSAR method for the analysis of several datasets, we have demonstrated recently that the …
Combinatorial QSAR modeling of P-glycoprotein substrates.
de Cerqueira Lima P, Golbraikh A, Oloff S, Xiao Y, Tropsha A. de Cerqueira Lima P, et al. Among authors: golbraikh a. J Chem Inf Model. 2006 May-Jun;46(3):1245-54. doi: 10.1021/ci0504317. J Chem Inf Model. 2006. PMID: 16711744
Quantitative structure-activity (property) relationship (QSAR/QSPR) models are typically generated with a single modeling technique using one type of molecular descriptors. Recently, we have begun to explore a combinatorial QSAR approach which employs various combin …
Quantitative structure-activity (property) relationship (QSAR/QSPR) models are typically generated with a single modeling technique u …
42 results