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Results: 1 to 20 of 139

Similar articles for PubMed (Select 18817552)

1.
2.

A novel method for mining highly imbalanced high-throughput screening data in PubChem.

Li Q, Wang Y, Bryant SH.

Bioinformatics. 2009 Dec 15;25(24):3310-6. doi: 10.1093/bioinformatics/btp589. Epub 2009 Oct 13.

3.

Data mining PubChem using a support vector machine with the Signature molecular descriptor: classification of factor XIa inhibitors.

Weis DC, Visco DP Jr, Faulon JL.

J Mol Graph Model. 2008 Nov;27(4):466-75. doi: 10.1016/j.jmgm.2008.08.004. Epub 2008 Aug 27.

PMID:
18829357
4.

An overview of the PubChem BioAssay resource.

Wang Y, Bolton E, Dracheva S, Karapetyan K, Shoemaker BA, Suzek TO, Wang J, Xiao J, Zhang J, Bryant SH.

Nucleic Acids Res. 2010 Jan;38(Database issue):D255-66. doi: 10.1093/nar/gkp965. Epub 2009 Nov 19.

5.

Data mining a small molecule drug screening representative subset from NIH PubChem.

Xie XQ, Chen JZ.

J Chem Inf Model. 2008 Mar;48(3):465-75. doi: 10.1021/ci700193u. Epub 2008 Feb 27.

PMID:
18302356
6.

The Text-mining based PubChem Bioassay neighboring analysis.

Han L, Suzek TO, Wang Y, Bryant SH.

BMC Bioinformatics. 2010 Nov 8;11:549. doi: 10.1186/1471-2105-11-549.

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.

Benchmarking ligand-based virtual High-Throughput Screening with the PubChem database.

Butkiewicz M, Lowe EW Jr, Mueller R, Mendenhall JL, Teixeira PL, Weaver CD, Meiler J.

Molecules. 2013 Jan 8;18(1):735-56. doi: 10.3390/molecules18010735.

9.

PubChem's BioAssay Database.

Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Zhou Z, Han L, Karapetyan K, Dracheva S, Shoemaker BA, Bolton E, Gindulyte A, Bryant SH.

Nucleic Acids Res. 2012 Jan;40(Database issue):D400-12. doi: 10.1093/nar/gkr1132. Epub 2011 Dec 2.

10.

PubChem BioAssay: 2014 update.

Wang Y, Suzek T, Zhang J, Wang J, He S, Cheng T, Shoemaker BA, Gindulyte A, Bryant SH.

Nucleic Acids Res. 2014 Jan;42(Database issue):D1075-82. doi: 10.1093/nar/gkt978. Epub 2013 Nov 5.

11.

Designing focused chemical libraries enriched in protein-protein interaction inhibitors using machine-learning methods.

Reynès C, Host H, Camproux AC, Laconde G, Leroux F, Mazars A, Deprez B, Fahraeus R, Villoutreix BO, Sperandio O.

PLoS Comput Biol. 2010 Mar 5;6(3):e1000695. doi: 10.1371/journal.pcbi.1000695.

12.

QSAR classification model for antibacterial compounds and its use in virtual screening.

Singh N, Chaudhury S, Liu R, AbdulHameed MD, Tawa G, Wallqvist A.

J Chem Inf Model. 2012 Oct 22;52(10):2559-69. doi: 10.1021/ci300336v. Epub 2012 Oct 8.

PMID:
23013546
13.

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
14.

Profile-QSAR: a novel meta-QSAR method that combines activities across the kinase family to accurately predict affinity, selectivity, and cellular activity.

Martin E, Mukherjee P, Sullivan D, Jansen J.

J Chem Inf Model. 2011 Aug 22;51(8):1942-56. doi: 10.1021/ci1005004. Epub 2011 Jul 19.

PMID:
21667971
15.

Combinatorial support vector machines approach for virtual screening of selective multi-target serotonin reuptake inhibitors from large compound libraries.

Shi Z, Ma XH, Qin C, Jia J, Jiang YY, Tan CY, Chen YZ.

J Mol Graph Model. 2012 Feb;32:49-66. doi: 10.1016/j.jmgm.2011.09.002. Epub 2011 Oct 5.

PMID:
22064367
16.

Knowledge-based virtual screening: application to the MDM4/p53 protein-protein interaction.

Jacoby E, Boettcher A, Mayr LM, Brown N, Jenkins JL, Kallen J, Engeloch C, Schopfer U, Furet P, Masuya K, Lisztwan J.

Methods Mol Biol. 2009;575:173-94. doi: 10.1007/978-1-60761-274-2_7.

PMID:
19727615
17.

Using machine learning methods to predict experimental high-throughput screening data.

Mballo C, Makarenkov V.

Comb Chem High Throughput Screen. 2010 Jun;13(5):430-41.

PMID:
20236062
18.
19.

Decision tree models for data mining in hit discovery.

Hammann F, Drewe J.

Expert Opin Drug Discov. 2012 Apr;7(4):341-52. doi: 10.1517/17460441.2012.668182. Epub 2012 Feb 29. Review.

PMID:
22458505
20.

Differentiation of AmpC beta-lactamase binders vs. decoys using classification kNN QSAR modeling and application of the QSAR classifier to virtual screening.

Hsieh JH, Wang XS, Teotico D, Golbraikh A, Tropsha A.

J Comput Aided Mol Des. 2008 Sep;22(9):593-609. doi: 10.1007/s10822-008-9199-2. Epub 2008 Mar 13.

PMID:
18338225
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