213 related articles for article (PubMed ID: 31524874)
1. In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox.
Bohlen ML; Jeon HP; Kim YJ; Sung B
J Vis Exp; 2019 Aug; (150):. PubMed ID: 31524874
[TBL] [Abstract][Full Text] [Related]
2. Evaluation of the OECD (Q)SAR Application Toolbox for the profiling of estrogen receptor binding affinities.
Mombelli E
SAR QSAR Environ Res; 2012 Jan; 23(1-2):37-57. PubMed ID: 22014213
[TBL] [Abstract][Full Text] [Related]
3. The OECD QSAR Toolbox Starts Its Second Decade.
Schultz TW; Diderich R; Kuseva CD; Mekenyan OG
Methods Mol Biol; 2018; 1800():55-77. PubMed ID: 29934887
[TBL] [Abstract][Full Text] [Related]
4. QSAR modeling and prediction of the endocrine-disrupting potencies of brominated flame retardants.
Papa E; Kovarich S; Gramatica P
Chem Res Toxicol; 2010 May; 23(5):946-54. PubMed ID: 20408563
[TBL] [Abstract][Full Text] [Related]
5. CERAPP: Collaborative Estrogen Receptor Activity Prediction Project.
Mansouri K; Abdelaziz A; Rybacka A; Roncaglioni A; Tropsha A; Varnek A; Zakharov A; Worth A; Richard AM; Grulke CM; Trisciuzzi D; Fourches D; Horvath D; Benfenati E; Muratov E; Wedebye EB; Grisoni F; Mangiatordi GF; Incisivo GM; Hong H; Ng HW; Tetko IV; Balabin I; Kancherla J; Shen J; Burton J; Nicklaus M; Cassotti M; Nikolov NG; Nicolotti O; Andersson PL; Zang Q; Politi R; Beger RD; Todeschini R; Huang R; Farag S; Rosenberg SA; Slavov S; Hu X; Judson RS
Environ Health Perspect; 2016 Jul; 124(7):1023-33. PubMed ID: 26908244
[TBL] [Abstract][Full Text] [Related]
6. Development of QSAR models for predicting the binding affinity of endocrine disrupting chemicals to eight fish estrogen receptor.
He J; Peng T; Yang X; Liu H
Ecotoxicol Environ Saf; 2018 Feb; 148():211-219. PubMed ID: 29055205
[TBL] [Abstract][Full Text] [Related]
7. The importance of molecular structures, endpoints' values, and predictivity parameters in QSAR research: QSAR analysis of a series of estrogen receptor binders.
Li J; Gramatica P
Mol Divers; 2010 Nov; 14(4):687-96. PubMed ID: 19921452
[TBL] [Abstract][Full Text] [Related]
8. Integration of in silico methods and computational systems biology to explore endocrine-disrupting chemical binding with nuclear hormone receptors.
Ruiz P; Sack A; Wampole M; Bobst S; Vracko M
Chemosphere; 2017 Jul; 178():99-109. PubMed ID: 28319747
[TBL] [Abstract][Full Text] [Related]
9. Pesticides as estrogen disruptors: QSAR for selective ERα and ERβ binding of pesticides.
Agatonovic-Kustrin S; Alexander M; Morton DW; Turner JV
Comb Chem High Throughput Screen; 2011 Feb; 14(2):85-92. PubMed ID: 20958252
[TBL] [Abstract][Full Text] [Related]
10. Deep learning driven QSAR model for environmental toxicology: Effects of endocrine disrupting chemicals on human health.
Heo S; Safder U; Yoo C
Environ Pollut; 2019 Oct; 253():29-38. PubMed ID: 31302400
[TBL] [Abstract][Full Text] [Related]
11. Comparing in vivo data and in silico predictions for acute effects assessment of biocidal active substances and metabolites for aquatic organisms.
Blázquez M; Andreu-Sánchez O; Ranero I; Fernández-Cruz ML; Benfenati E
Ecotoxicol Environ Saf; 2020 Dec; 205():111291. PubMed ID: 32956865
[TBL] [Abstract][Full Text] [Related]
12. Prediction of the endocrine disruption profile of pesticides.
Devillers J; Bro E; Millot F
SAR QSAR Environ Res; 2015; 26(10):831-52. PubMed ID: 26548639
[TBL] [Abstract][Full Text] [Related]
13. Development of a general baseline toxicity QSAR model for the fish embryo acute toxicity test.
Klüver N; Vogs C; Altenburger R; Escher BI; Scholz S
Chemosphere; 2016 Dec; 164():164-173. PubMed ID: 27588575
[TBL] [Abstract][Full Text] [Related]
14. Estrogen Receptor Binding Affinity of Food Contact Material Components Estimated by QSAR.
Sosnovcová J; Rucki M; Bendová H
Cent Eur J Public Health; 2016 Sep; 24(3):241-244. PubMed ID: 27743518
[TBL] [Abstract][Full Text] [Related]
15. Consensus QSAR modeling of toxicity of pharmaceuticals to different aquatic organisms: Ranking and prioritization of the DrugBank database compounds.
Khan K; Benfenati E; Roy K
Ecotoxicol Environ Saf; 2019 Jan; 168():287-297. PubMed ID: 30390527
[TBL] [Abstract][Full Text] [Related]
16. Quantitative Structure-Activity Relationships of Aquatic Narcosis: A Review.
Adhikari C; Mishra BK
Curr Comput Aided Drug Des; 2018; 14(1):7-28. PubMed ID: 28699497
[TBL] [Abstract][Full Text] [Related]
17. Evaluation of the OECD QSAR toolbox automatic workflow for the prediction of the acute toxicity of organic chemicals to fathead minnow.
Mombelli E; Pandard P
Regul Toxicol Pharmacol; 2021 Jun; 122():104893. PubMed ID: 33587933
[TBL] [Abstract][Full Text] [Related]
18. Recent advances in the molecular modeling of estrogen receptor-mediated toxicity.
Tsakovska I; Pajeva I; Alov P; Worth A
Adv Protein Chem Struct Biol; 2011; 85():217-51. PubMed ID: 21920325
[TBL] [Abstract][Full Text] [Related]
19. Computational prediction models for assessing endocrine disrupting potential of chemicals.
Sakkiah S; Guo W; Pan B; Kusko R; Tong W; Hong H
J Environ Sci Health C Environ Carcinog Ecotoxicol Rev; 2018; 36(4):192-218. PubMed ID: 30633647
[TBL] [Abstract][Full Text] [Related]
20. Imatinib: Major photocatalytic degradation pathways in aqueous media and the relative toxicity of its transformation products.
Secrétan PH; Karoui M; Sadou Yayé H; Levi Y; Tortolano L; Solgadi A; Yagoubi N; Do B
Sci Total Environ; 2019 Mar; 655():547-556. PubMed ID: 30476834
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
