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Items: 1 to 20 of 104

1.

QSAR with experimental and predictive distributions: an information theoretic approach for assessing model quality.

Wood DJ, Carlsson L, Eklund M, Norinder U, Stålring J.

J Comput Aided Mol Des. 2013 Mar;27(3):203-19. doi: 10.1007/s10822-013-9639-5. Epub 2013 Mar 16.

2.

Assessment of machine learning reliability methods for quantifying the applicability domain of QSAR regression models.

Toplak M, Močnik R, Polajnar M, Bosnić Z, Carlsson L, Hasselgren C, Demšar J, Boyer S, Zupan B, Stålring J.

J Chem Inf Model. 2014 Feb 24;54(2):431-41. doi: 10.1021/ci4006595. Epub 2014 Feb 11.

PMID:
24490838
3.

Evolutionary computation and QSAR research.

Aguiar-Pulido V, Gestal M, Cruz-Monteagudo M, Rabuñal JR, Dorado J, Munteanu CR.

Curr Comput Aided Drug Des. 2013 Jun;9(2):206-25. Review.

PMID:
23700999
4.

Using beta binomials to estimate classification uncertainty for ensemble models.

Clark RD, Liang W, Lee AC, Lawless MS, Fraczkiewicz R, Waldman M.

J Cheminform. 2014 Jun 22;6:34. doi: 10.1186/1758-2946-6-34. eCollection 2014.

5.

Uncertainty in QSAR predictions.

Sahlin U.

Altern Lab Anim. 2013 Mar;41(1):111-25.

PMID:
23614548
6.

kScore: a novel machine learning approach that is not dependent on the data structure of the training set.

Oloff S, Muegge I.

J Comput Aided Mol Des. 2007 Jan-Mar;21(1-3):87-95. Epub 2007 Feb 28.

PMID:
17333481
7.

Critical assessment of QSAR models of environmental toxicity against Tetrahymena pyriformis: focusing on applicability domain and overfitting by variable selection.

Tetko IV, Sushko I, Pandey AK, Zhu H, Tropsha A, Papa E, Oberg T, Todeschini R, Fourches D, Varnek A.

J Chem Inf Model. 2008 Sep;48(9):1733-46. doi: 10.1021/ci800151m. Epub 2008 Aug 26.

PMID:
18729318
8.

Assessing the reliability of a QSAR model's predictions.

He L, Jurs PC.

J Mol Graph Model. 2005 Jun;23(6):503-23.

PMID:
15896992
9.

Combinatorial QSAR of ambergris fragrance compounds.

Kovatcheva A, Golbraikh A, Oloff S, Xiao YD, Zheng W, Wolschann P, Buchbauer G, Tropsha A.

J Chem Inf Comput Sci. 2004 Mar-Apr;44(2):582-95.

PMID:
15032539
10.

Predictive QSAR modeling of phosphodiesterase 4 inhibitors.

Kovalishyn V, Tanchuk V, Charochkina L, Semenuta I, Prokopenko V.

J Mol Graph Model. 2012 Feb;32:32-8. doi: 10.1016/j.jmgm.2011.10.001. Epub 2011 Oct 14.

PMID:
22023934
11.
12.

Classifier calibration using splined empirical probabilities in clinical risk prediction.

Gaudoin R, Montana G, Jones S, Aylin P, Bottle A.

Health Care Manag Sci. 2015 Jun;18(2):156-65. doi: 10.1007/s10729-014-9267-1. Epub 2014 Feb 21.

PMID:
24557734
13.

Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection.

Golbraikh A, Tropsha A.

J Comput Aided Mol Des. 2002 May-Jun;16(5-6):357-69.

PMID:
12489684
14.

QSAR workbench: automating QSAR modeling to drive compound design.

Cox R, Green DV, Luscombe CN, Malcolm N, Pickett SD.

J Comput Aided Mol Des. 2013 Apr;27(4):321-36. doi: 10.1007/s10822-013-9648-4. Epub 2013 Apr 25.

15.

Critically Assessing the Predictive Power of QSAR Models for Human Liver Microsomal Stability.

Liu R, Schyman P, Wallqvist A.

J Chem Inf Model. 2015 Aug 24;55(8):1566-75. doi: 10.1021/acs.jcim.5b00255. Epub 2015 Jul 29.

PMID:
26170251
16.

Quantitative structure-activity relationship models that stand the test of time.

Davis AM, Wood DJ.

Mol Pharm. 2013 Apr 1;10(4):1183-90. doi: 10.1021/mp300466n. Epub 2013 Feb 14.

PMID:
23316903
17.

Binary classification of a large collection of environmental chemicals from estrogen receptor assays by quantitative structure-activity relationship and machine learning methods.

Zang Q, Rotroff DM, Judson RS.

J Chem Inf Model. 2013 Dec 23;53(12):3244-61. doi: 10.1021/ci400527b. Epub 2013 Dec 11.

PMID:
24279462
18.

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.

J Comput Aided Mol Des. 2003 Feb-Apr;17(2-4):241-53.

PMID:
13677490
19.

QSAR multi-target in drug discovery: a review.

Zanni R, Gálvez-Llompart M, Gálvez J, García-Domenech R.

Curr Comput Aided Drug Des. 2014;10(2):129-36. Review.

PMID:
24724898
20.

Molecule kernels: a descriptor- and alignment-free quantitative structure-activity relationship approach.

Mohr JA, Jain BJ, Obermayer K.

J Chem Inf Model. 2008 Sep;48(9):1868-81. doi: 10.1021/ci800144y. Epub 2008 Sep 4.

PMID:
18767832

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