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Original Articles

PLS-QSAR of the adult and developmental toxicity of chemicals to Hydra attenuata

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Pages 705-712 | Published online: 29 Oct 2010

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Read on this site (7)

S.B. Gunturi & N. Ramamurthi. (2014) A novel approach to generate robust classification models to predict developmental toxicity from imbalanced datasets. SAR and QSAR in Environmental Research 25:9, pages 711-727.
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J.P. Doucet, A. Doucet-Panaye & J. Devillers. (2013) Structure–activity relationship study of trifluoromethylketones: inhibitors of insect juvenile hormone esterase. SAR and QSAR in Environmental Research 24:6, pages 481-499.
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L. Wang, X.H. Liu, D. Wu, M.Z. Xu, T. Sun, B.S. Cui & Z.F. Yang. (2009) Modelling the depuration rates of polychlorinated biphenyls in Oncorhynchus mykiss with quantum chemical descriptors. SAR and QSAR in Environmental Research 20:1-2, pages 91-101.
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J. F. Niu, Z. F. Yang, Z. Y. Shen & L. L. Wang. (2006) QSPRs for the prediction of photodegradation half-life of PCBs in n-hexane. SAR and QSAR in Environmental Research 17:2, pages 173-182.
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J. Devillers. (2005) A new strategy for using supervised artificial neural networks in QSAR. SAR and QSAR in Environmental Research 16:5, pages 433-442.
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J. Devillers. (2004) Linear versus nonlinear QSAR modeling of the toxicity of phenol derivatives to Tetrahymena pyriformis . SAR and QSAR in Environmental Research 15:4, pages 237-249.
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Articles from other publishers (14)

Xiao Li, Ning Li, Zhenyu Sui, Kaishun Bi & Zuojing Li. (2017) An Investigation on the Quantitative Structure-Activity Relationships of the Anti-Inflammatory Activity of Diterpenoid Alkaloids. Molecules 22:3, pages 363.
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Saeed Yousefinejad & Bahram Hemmateenejad. (2015) Chemometrics tools in QSAR/QSPR studies: A historical perspective. Chemometrics and Intelligent Laboratory Systems 149, pages 177-204.
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James Devillers. 2014. In Silico Bees. In Silico Bees 135 152 .
James Devillers & Hugo Devillers. 2014. In Silico Bees. In Silico Bees 1 26 .
Annick Doucet-Panaye & Jean-Pierre Doucet. 2013. Juvenile Hormones and Juvenoids. Juvenile Hormones and Juvenoids 241 266 .
Raghuraman Venkatapathy & Nina Ching Y. Wang. 2013. Computational Toxicology. Computational Toxicology 305 340 .
James Devillers. 2013. Computational Toxicology. Computational Toxicology 3 27 .
Christina Grindon, Robert Combes, Mark T.D. Cronin, David W. Roberts & John F. Garrod. (2008) Integrated Decision-tree Testing Strategies for Developmental and Reproductive Toxicity with Respect to the Requirements of the EU REACH Legislation. Alternatives to Laboratory Animals 36:1_suppl, pages 123-138.
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Maykel Cruz-MonteagudoHumberto González-DíazFernanda BorgesElena Rosa DominguezM. Natália D.S. Cordeiro. (2008) 3D-MEDNEs: An Alternative “in Silico” Technique for Chemical Research in Toxicology. 2. Quantitative Proteome−Toxicity Relationships (QPTR) based on Mass Spectrum Spiral Entropy. Chemical Research in Toxicology 21:3, pages 619-632.
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Christina Grindon, Robert Combes, Mark T.D. Cronin, David W. Roberts & John F. Garrod. (2008) Integrated Decision-tree Testing Strategies for Developmental and Reproductive Toxicity with Respect to the Requirements of the EU REACH Legislation. Alternatives to Laboratory Animals 36:1, pages 65-80.
Crossref
Xue LI, Lei FANG, Jun HUANG & Gang YU. (2008) Photolysis of mono-through deca-chlorinated biphenyls by ultraviolet irradiation in n-hexane and quantitative structure-property relationship analysis. Journal of Environmental Sciences 20:6, pages 753-759.
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Mark T. D. Cronin & Andrew P. Worth. (2008) (Q)SARs for Predicting Effects Relating to Reproductive Toxicity. QSAR & Combinatorial Science 27:1, pages 91-100.
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J Devillers, J.C Doré, M Tisseur, S Cluzeau & G Maurin. (2004) Modelling the flight activity of Apis mellifera at the hive entrance. Computers and Electronics in Agriculture 42:2, pages 87-109.
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James Devillers. 2003. Nature-insprired Methods in Chemometrics: Genetic Algorithms and Artificial Neural Networks. Nature-insprired Methods in Chemometrics: Genetic Algorithms and Artificial Neural Networks 323 339 .

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