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Items: 1 to 20 of 296

1.

Predicting acute aquatic toxicity of structurally diverse chemicals in fish using artificial intelligence approaches.

Singh KP, Gupta S, Rai P.

Ecotoxicol Environ Saf. 2013 Sep;95:221-33. doi: 10.1016/j.ecoenv.2013.05.017.

PMID:
23764236
2.

Predicting carcinogenicity of diverse chemicals using probabilistic neural network modeling approaches.

Singh KP, Gupta S, Rai P.

Toxicol Appl Pharmacol. 2013 Oct 15;272(2):465-75. doi: 10.1016/j.taap.2013.06.029.

PMID:
23856075
3.

Modeling the binding affinity of structurally diverse industrial chemicals to carbon using the artificial intelligence approaches.

Gupta S, Basant N, Rai P, Singh KP.

Environ Sci Pollut Res Int. 2015 Nov;22(22):17810-27. doi: 10.1007/s11356-015-4965-x.

PMID:
26160122
4.

QSTR modeling for qualitative and quantitative toxicity predictions of diverse chemical pesticides in honey bee for regulatory purposes.

Singh KP, Gupta S, Basant N, Mohan D.

Chem Res Toxicol. 2014 Sep 15;27(9):1504-15. doi: 10.1021/tx500100m.

PMID:
25167463
5.

Using fragment chemistry data mining and probabilistic neural networks in screening chemicals for acute toxicity to the fathead minnow.

Niculescu SP, Atkinson A, Hammond G, Lewis M.

SAR QSAR Environ Res. 2004 Aug;15(4):293-309.

PMID:
15370419
7.

Predicting adsorptive removal of chlorophenol from aqueous solution using artificial intelligence based modeling approaches.

Singh KP, Gupta S, Ojha P, Rai P.

Environ Sci Pollut Res Int. 2013 Apr;20(4):2271-87. doi: 10.1007/s11356-012-1102-y.

PMID:
22851225
8.

Multispecies QSAR modeling for predicting the aquatic toxicity of diverse organic chemicals for regulatory toxicology.

Singh KP, Gupta S, Kumar A, Mohan D.

Chem Res Toxicol. 2014 May 19;27(5):741-53. doi: 10.1021/tx400371w.

PMID:
24738471
9.

Predicting the acute neurotoxicity of diverse organic solvents using probabilistic neural networks based QSTR modeling approaches.

Basant N, Gupta S, Singh KP.

Neurotoxicology. 2016 Mar;53:45-52. doi: 10.1016/j.neuro.2015.12.013.

PMID:
26721664
11.

Prediction of fathead minnow acute toxicity of organic compounds from molecular structure.

Eldred DV, Weikel CL, Jurs PC, Kaiser KL.

Chem Res Toxicol. 1999 Jul;12(7):670-8.

PMID:
10409408
12.

In silico prediction of toxicity of non-congeneric industrial chemicals using ensemble learning based modeling approaches.

Singh KP, Gupta S.

Toxicol Appl Pharmacol. 2014 Mar 15;275(3):198-212. doi: 10.1016/j.taap.2014.01.006.

PMID:
24463095
14.

Linear and nonlinear modeling approaches for urban air quality prediction.

Singh KP, Gupta S, Kumar A, Shukla SP.

Sci Total Environ. 2012 Jun 1;426:244-55. doi: 10.1016/j.scitotenv.2012.03.076.

PMID:
22542239
15.
16.

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
17.

QSAR/QSPR studies using probabilistic neural networks and generalized regression neural networks.

Mosier PD, Jurs PC.

J Chem Inf Comput Sci. 2002 Nov-Dec;42(6):1460-70.

PMID:
12444744
18.

Tuning neural and fuzzy-neural networks for toxicity modeling.

Mazzatorta P, Benfenati E, Neagu CD, Gini G.

J Chem Inf Comput Sci. 2003 Mar-Apr;43(2):513-8.

PMID:
12653515
20.

Validation of a QSAR model for acute toxicity.

Pavan M, Netzeva TI, Worth AP.

SAR QSAR Environ Res. 2006 Apr;17(2):147-71.

PMID:
16644555
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