CERAPP: Collaborative Estrogen Receptor Activity Prediction Project (Q28830914)

15

0 references

Alexander Tropsha

5

0 references

Denis Fourches

12

0 references

Jayaram Kancherla

Jayaram Kancherla

24

0 references

Kamel Mansouri

Kamel Mansouri

1

0 references

Eva B Wedebye

Eva Bay Wedebye

16

0 references

Andrew Worth

Andrew Worth

8

0 references

Igor V. Tetko

Igor V Tetko

22

0 references

Roberto Todeschini

Roberto Todeschini

35

0 references

Christopher M Grulke

Christopher M Grulke

10

0 references

Francesca Grisoni

Francesca Grisoni

Giuseppe Felice Mangiatordi

17

0 references

Giuseppe F Mangiatordi

18

0 references

Nikolai G Nikolov

Nikolai G Nikolov

29

0 references

Emilio Benfenati

14

Emilio Benfenati

0 references

Dragos Horvath

13

Dragos Horvath

0 references

Antony John Williams

1 reference

ORCID Public Data File 2021

Richard S. Judson

41

Richard S Judson

0 references

Ann M Richard

9

Ann M Richard

0 references

Huixiao Hong

Huixiao Hong

20

0 references

Marc C Nicklaus

Marc Nicklaus

27

0 references

Daniela Trisciuzzi

11

Daniela Trisciuzzi

0 references

Orazio Nicolotti

30

Orazio Nicolotti

0 references

author name string

Ahmed Abdelaziz

2

0 references

Aleksandra Rybacka

3

0 references

Alessandra Roncaglioni

4

0 references

Alexandre Varnek

6

0 references

Alexey Zakharov

7

0 references

Giuseppina M Incisivo

19

0 references

Hui W Ng

21

0 references

Ilya Balabin

23

0 references

Jie Shen

25

0 references

Julien Burton

26

0 references

Matteo Cassotti

28

0 references

Patrik L Andersson

31

0 references

Qingda Zang

32

0 references

Regina Politi

33

0 references

Richard D Beger

34

0 references

Ruili Huang

36

0 references

Sherif Farag

37

0 references

Sine A Rosenberg

38

0 references

Svetoslav Slavov

39

0 references

Xin Hu

Environmental Health Perspectives

40

0 references

language of work or name

English

0 references

publication date

July 2016

0 references

published in

0 references

volume

124

0 references

issue

7

0 references

page(s)

1023-33

0 references

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Competitive molecular docking approach for predicting estrogen receptor subtype α agonists and antagonists

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The toxicity data landscape for environmental chemicals

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The use of metabolising systems for in vitro testing of endocrine disruptors

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3 June 2018

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Decision forest analysis of 61 single nucleotide polymorphisms in a case-control study of esophageal cancer; a novel method

3 June 2018

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3 June 2018

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Developing 13C NMR quantitative spectrometric data-activity relationship (QSDAR) models of steroid binding to the corticosteroid binding globulin

3 June 2018

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3 June 2018

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3 June 2018

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Research needs for the risk assessment of health and environmental effects of endocrine disruptors: a report of the U.S. EPA-sponsored workshop

3 June 2018

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Binary classification of a large collection of environmental chemicals from estrogen receptor assays by quantitative structure-activity relationship and machine learning methods.

3 June 2018

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26 November 2018

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Comparison of different approaches to define the applicability domain of QSAR models.

26 November 2018

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Estimating toxicity-related biological pathway altering doses for high-throughput chemical risk assessment

26 November 2018

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Advancing exposure characterization for chemical evaluation and risk assessment.

26 November 2018

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Computational toxicology as implemented by the U.S. EPA: providing high throughput decision support tools for screening and assessing chemical exposure, hazard and risk.

26 November 2018

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In silico toxicology in drug discovery - concepts based on three-dimensional models.

26 November 2018

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Pharmacophore and QSAR modeling of estrogen receptor beta ligands and subsequent validation and in silico search for new hits.

26 November 2018

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Binary classification models for endocrine disrupter effects mediated through the estrogen receptor

26 November 2018

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ACToR — Aggregated Computational Toxicology Resource

26 November 2018

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26 November 2018

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An in silico ensemble method for lead discovery: decision forest

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The structure-activity relationship in amides inhibiting photosynthesis

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