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Items: 1 to 20 of 96

1.

QSAR modelling of a large imbalanced aryl hydrocarbon activation dataset by rational and random sampling and screening of 80,086 REACH pre-registered and/or registered substances.

Klimenko K, Rosenberg SA, Dybdahl M, Wedebye EB, Nikolov NG.

PLoS One. 2019 Mar 14;14(3):e0213848. doi: 10.1371/journal.pone.0213848. eCollection 2019.

2.

Does rational selection of training and test sets improve the outcome of QSAR modeling?

Martin TM, Harten P, Young DM, Muratov EN, Golbraikh A, Zhu H, Tropsha A.

J Chem Inf Model. 2012 Oct 22;52(10):2570-8. doi: 10.1021/ci300338w. Epub 2012 Oct 3.

PMID:
23030316
3.

Rank order entropy: why one metric is not enough.

McLellan MR, Ryan MD, Breneman CM.

J Chem Inf Model. 2011 Sep 26;51(9):2302-19. doi: 10.1021/ci200170k. Epub 2011 Aug 29.

4.

The importance of molecular structures, endpoints' values, and predictivity parameters in QSAR research: QSAR analysis of a series of estrogen receptor binders.

Li J, Gramatica P.

Mol Divers. 2010 Nov;14(4):687-96. doi: 10.1007/s11030-009-9212-2. Epub 2009 Nov 17.

PMID:
19921452
5.

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
6.

Ligand-based virtual screening and in silico design of new antimalarial compounds using nonstochastic and stochastic total and atom-type quadratic maps.

Marrero-Ponce Y, Iyarreta-Veitía M, Montero-Torres A, Romero-Zaldivar C, Brandt CA, Avila PE, Kirchgatter K, Machado Y.

J Chem Inf Model. 2005 Jul-Aug;45(4):1082-100.

PMID:
16045304
7.

A comprehensive support vector machine binary hERG classification model based on extensive but biased end point hERG data sets.

Shen MY, Su BH, Esposito EX, Hopfinger AJ, Tseng YJ.

Chem Res Toxicol. 2011 Jun 20;24(6):934-49. doi: 10.1021/tx200099j. Epub 2011 May 6.

PMID:
21504223
8.
9.

Application of predictive QSAR models to database mining: identification and experimental validation of novel anticonvulsant compounds.

Shen M, Béguin C, Golbraikh A, Stables JP, Kohn H, Tropsha A.

J Med Chem. 2004 Apr 22;47(9):2356-64.

PMID:
15084134
10.

PVLOO-Based Training Set Selection Improves the External Predictability of QSAR/QSPR Models.

Dong Y, Xiang B, Du D.

J Chem Inf Model. 2017 May 22;57(5):1055-1067. doi: 10.1021/acs.jcim.7b00029. Epub 2017 Apr 27.

PMID:
28419798
11.

Discovery of novel antimalarial compounds enabled by QSAR-based virtual screening.

Zhang L, Fourches D, Sedykh A, Zhu H, Golbraikh A, Ekins S, Clark J, Connelly MC, Sigal M, Hodges D, Guiguemde A, Guy RK, Tropsha A.

J Chem Inf Model. 2013 Feb 25;53(2):475-92. doi: 10.1021/ci300421n. Epub 2013 Jan 23.

12.

Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymena pyriformis.

Zhu H, Tropsha A, Fourches D, Varnek A, Papa E, Gramatica P, Oberg T, Dao P, Cherkasov A, Tetko IV.

J Chem Inf Model. 2008 Apr;48(4):766-84. doi: 10.1021/ci700443v. Epub 2008 Mar 1.

PMID:
18311912
13.

Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches.

Zhang L, Sedykh A, Tripathi A, Zhu H, Afantitis A, Mouchlis VD, Melagraki G, Rusyn I, Tropsha A.

Toxicol Appl Pharmacol. 2013 Oct 1;272(1):67-76. doi: 10.1016/j.taap.2013.04.032. Epub 2013 May 23.

14.

New public QSAR model for carcinogenicity.

Fjodorova N, Vracko M, Novic M, Roncaglioni A, Benfenati E.

Chem Cent J. 2010 Jul 29;4 Suppl 1:S3. doi: 10.1186/1752-153X-4-S1-S3.

15.

Internal and external validation of the long-term QSARs for neutral organics to fish from ECOSAR™.

de Haas EM, Eikelboom T, Bouwman T.

SAR QSAR Environ Res. 2011 Jul-Sep;22(5-6):545-59. doi: 10.1080/1062936X.2011.569949. Epub 2011 Jul 7.

PMID:
21732893
16.

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
17.
18.

Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do.

Zhao L, Wang W, Sedykh A, Zhu H.

ACS Omega. 2017 Jun 30;2(6):2805-2812. doi: 10.1021/acsomega.7b00274. Epub 2017 Jun 19.

19.

2D binary QSAR modeling of LPA3 receptor antagonism.

Fells JI, Tsukahara R, Liu J, Tigyi G, Parrill AL.

J Mol Graph Model. 2010 Jun;28(8):828-33. doi: 10.1016/j.jmgm.2010.03.002. Epub 2010 Mar 7.

20.

Translational Metabolomics of Head Injury: Exploring Dysfunctional Cerebral Metabolism with Ex Vivo NMR Spectroscopy-Based Metabolite Quantification.

Wolahan SM, Hirt D, Glenn TC.

In: Kobeissy FH, editor. Brain Neurotrauma: Molecular, Neuropsychological, and Rehabilitation Aspects. Boca Raton (FL): CRC Press/Taylor & Francis; 2015. Chapter 25.

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