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Results: 1 to 20 of 127

1.

Oversampling to overcome overfitting: exploring the relationship between data set composition, molecular descriptors, and predictive modeling methods.

Chang CY, Hsu MT, Esposito EX, Tseng YJ.

J Chem Inf Model. 2013 Apr 22;53(4):958-71. doi: 10.1021/ci4000536. Epub 2013 Mar 15.

PMID:
23464929
[PubMed - indexed for MEDLINE]
2.

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
[PubMed - indexed for MEDLINE]
3.

Predictive toxicology modeling: protocols for exploring hERG classification and Tetrahymena pyriformis end point predictions.

Su BH, Tu YS, Esposito EX, Tseng YJ.

J Chem Inf Model. 2012 Jun 25;52(6):1660-73. doi: 10.1021/ci300060b. Epub 2012 Jun 8.

PMID:
22642982
[PubMed - indexed for MEDLINE]
4.

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
[PubMed - indexed for MEDLINE]
5.

Predictions of BuChE inhibitors using support vector machine and naive Bayesian classification techniques in drug discovery.

Fang J, Yang R, Gao L, Zhou D, Yang S, Liu AL, Du GH.

J Chem Inf Model. 2013 Nov 25;53(11):3009-20. doi: 10.1021/ci400331p. Epub 2013 Nov 6.

PMID:
24144102
[PubMed - indexed for MEDLINE]
6.

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
[PubMed - in process]
7.

Fusing dual-event data sets for Mycobacterium tuberculosis machine learning models and their evaluation.

Ekins S, Freundlich JS, Reynolds RC.

J Chem Inf Model. 2013 Nov 25;53(11):3054-63. doi: 10.1021/ci400480s. Epub 2013 Oct 30.

PMID:
24144044
[PubMed - indexed for MEDLINE]
8.

Prediction of compounds with closely related activity profiles using weighted support vector machine linear combinations.

Heikamp K, Bajorath J.

J Chem Inf Model. 2013 Apr 22;53(4):791-801. doi: 10.1021/ci400090t. Epub 2013 Apr 8.

PMID:
23517241
[PubMed - indexed for MEDLINE]
9.

Predicting drug-induced hepatotoxicity using QSAR and toxicogenomics approaches.

Low Y, Uehara T, Minowa Y, Yamada H, Ohno Y, Urushidani T, Sedykh A, Muratov E, Kuz'min V, Fourches D, Zhu H, Rusyn I, Tropsha A.

Chem Res Toxicol. 2011 Aug 15;24(8):1251-62. doi: 10.1021/tx200148a. Epub 2011 Jul 21.

PMID:
21699217
[PubMed - indexed for MEDLINE]
10.

Prediction of skin sensitization with a particle swarm optimized support vector machine.

Yuan H, Huang J, Cao C.

Int J Mol Sci. 2009 Jul 17;10(7):3237-54. doi: 10.3390/ijms10073237.

PMID:
19742136
[PubMed - indexed for MEDLINE]
Free PMC Article
11.

Application of support vector machine to three-dimensional shape-based virtual screening using comprehensive three-dimensional molecular shape overlay with known inhibitors.

Sato T, Yuki H, Takaya D, Sasaki S, Tanaka A, Honma T.

J Chem Inf Model. 2012 Apr 23;52(4):1015-26. doi: 10.1021/ci200562p. Epub 2012 Mar 27.

PMID:
22424085
[PubMed - indexed for MEDLINE]
12.

PXR ligand classification model with SFED-weighted WHIM and CoMMA descriptors.

Ma SL, Joung JY, Lee S, Cho KH, No KT.

SAR QSAR Environ Res. 2012 Jul;23(5-6):485-504. doi: 10.1080/1062936X.2012.665385. Epub 2012 May 16.

PMID:
22591167
[PubMed - indexed for MEDLINE]
13.

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
[PubMed - indexed for MEDLINE]
14.

Predicting human liver microsomal stability with machine learning techniques.

Sakiyama Y, Yuki H, Moriya T, Hattori K, Suzuki M, Shimada K, Honma T.

J Mol Graph Model. 2008 Feb;26(6):907-15. Epub 2007 Jun 27.

PMID:
17683964
[PubMed - indexed for MEDLINE]
15.

Effect of training data size and noise level on support vector machines virtual screening of genotoxic compounds from large compound libraries.

Kumar P, Ma X, Liu X, Jia J, Bucong H, Xue Y, Li ZR, Yang SY, Wei YQ, Chen YZ.

J Comput Aided Mol Des. 2011 May;25(5):455-67. doi: 10.1007/s10822-011-9431-3. Epub 2011 May 10.

PMID:
21556903
[PubMed - indexed for MEDLINE]
16.

Use of cell viability assay data improves the prediction accuracy of conventional quantitative structure-activity relationship models of animal carcinogenicity.

Zhu H, Rusyn I, Richard A, Tropsha A.

Environ Health Perspect. 2008 Apr;116(4):506-13. doi: 10.1289/ehp.10573.

PMID:
18414635
[PubMed - indexed for MEDLINE]
Free PMC Article
17.

A classification study of human β₃-adrenergic receptor agonists using BCUT descriptors.

Hao M, Li Y, Wang Y, Zhang S.

Mol Divers. 2011 Nov;15(4):877-87. doi: 10.1007/s11030-011-9321-6. Epub 2011 May 31.

PMID:
21626288
[PubMed - indexed for MEDLINE]
18.

Predictive models for cytochrome p450 isozymes based on quantitative high throughput screening data.

Sun H, Veith H, Xia M, Austin CP, Huang R.

J Chem Inf Model. 2011 Oct 24;51(10):2474-81. doi: 10.1021/ci200311w. Epub 2011 Sep 26.

PMID:
21905670
[PubMed - indexed for MEDLINE]
Free PMC Article
19.

QSAR investigation of NaV1.7 active compounds using the SVM/Signature approach and the Bioclipse Modeling platform.

Norinder U, Ek ME.

Bioorg Med Chem Lett. 2013 Jan 1;23(1):261-3. doi: 10.1016/j.bmcl.2012.10.102. Epub 2012 Oct 31.

PMID:
23177785
[PubMed - indexed for MEDLINE]
20.

Quantitative structure-activity relationship models for ready biodegradability of chemicals.

Mansouri K, Ringsted T, Ballabio D, Todeschini R, Consonni V.

J Chem Inf Model. 2013 Apr 22;53(4):867-78. doi: 10.1021/ci4000213. Epub 2013 Mar 27.

PMID:
23469921
[PubMed - indexed for MEDLINE]

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