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Items: 20

1.
2.

Quantitative structure-activity relationship models for prediction of estrogen receptor binding affinity of structurally diverse chemicals.

Schmieder PK, Ankley G, Mekenyan O, Walker JD, Bradbury S.

Environ Toxicol Chem. 2003 Aug;22(8):1844-54. Review.

PMID:
12924583
3.

Performance of (consensus) kNN QSAR for predicting estrogenic activity in a large diverse set of organic compounds.

Asikainen AH, Ruuskanen J, Tuppurainen KA.

SAR QSAR Environ Res. 2004 Feb;15(1):19-32. Review.

PMID:
15113066
4.

A comparison of model performance for six quantitative structure-activity relationship packages that predict acute toxicity to fish.

Moore DR, Breton RL, MacDonald DB.

Environ Toxicol Chem. 2003 Aug;22(8):1799-809. Review.

PMID:
12924579
5.

Quantitative structure-activity relationship methods: perspectives on drug discovery and toxicology.

Perkins R, Fang H, Tong W, Welsh WJ.

Environ Toxicol Chem. 2003 Aug;22(8):1666-79. Review.

PMID:
12924569
7.
8.

Predictive QSAR modeling workflow, model applicability domains, and virtual screening.

Tropsha A, Golbraikh A.

Curr Pharm Des. 2007;13(34):3494-504. Review.

PMID:
18220786
9.

In silico prediction of harmful effects triggered by drugs and chemicals.

Vedani A, Dobler M, Lill MA.

Toxicol Appl Pharmacol. 2005 Sep 1;207(2 Suppl):398-407. Review.

PMID:
16045954
10.

Comparative molecular field analysis (CoMFA) model using a large diverse set of natural, synthetic and environmental chemicals for binding to the androgen receptor.

Hong H, Fang H, Xie Q, Perkins R, Sheehan DM, Tong W.

SAR QSAR Environ Res. 2003 Oct-Dec;14(5-6):373-88. Review.

PMID:
14758981
11.

Large effects from small exposures. I. Mechanisms for endocrine-disrupting chemicals with estrogenic activity.

Welshons WV, Thayer KA, Judy BM, Taylor JA, Curran EM, vom Saal FS.

Environ Health Perspect. 2003 Jun;111(8):994-1006. Review.

12.

Considerations and recent advances in QSAR models for cytochrome P450-mediated drug metabolism prediction.

Li H, Sun J, Fan X, Sui X, Zhang L, Wang Y, He Z.

J Comput Aided Mol Des. 2008 Nov;22(11):843-55. doi: 10.1007/s10822-008-9225-4. Epub 2008 Jun 24. Review.

PMID:
18574695
13.

Comparison of in silico models for prediction of mutagenicity.

Bakhtyari NG, Raitano G, Benfenati E, Martin T, Young D.

J Environ Sci Health C Environ Carcinog Ecotoxicol Rev. 2013;31(1):45-66. doi: 10.1080/10590501.2013.763576. Review.

PMID:
23534394
15.

State of the art in the application of QSAR techniques for predicting mixture toxicity in environmental risk assessment.

Kim J, Kim S.

SAR QSAR Environ Res. 2015;26(1):41-59. doi: 10.1080/1062936X.2014.984627. Review.

PMID:
25608956
16.

Improving the prediction of drug disposition in the brain.

Lanevskij K, Japertas P, Didziapetris R.

Expert Opin Drug Metab Toxicol. 2013 Apr;9(4):473-86. doi: 10.1517/17425255.2013.754423. Epub 2013 Jan 8. Review.

PMID:
23294027
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18.

Challenges for computational structure-activity modelling for predicting chemical toxicity: future improvements?

Combes RD.

Expert Opin Drug Metab Toxicol. 2011 Sep;7(9):1129-40. doi: 10.1517/17425255.2011.602066. Epub 2011 Jul 15. Review.

PMID:
21756202
19.

Strategies for the generation, validation and application of in silico ADMET models in lead generation and optimization.

Gleeson MP, Montanari D.

Expert Opin Drug Metab Toxicol. 2012 Nov;8(11):1435-46. doi: 10.1517/17425255.2012.711317. Epub 2012 Jul 31. Review.

PMID:
22849616
20.

Chemoinformatic Classification Methods and their Applicability Domain.

Mathea M, Klingspohn W, Baumann K.

Mol Inform. 2016 May;35(5):160-80. doi: 10.1002/minf.201501019. Epub 2016 Feb 19. Review.

PMID:
27492083
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