CERAPP: Collaborative Estrogen Receptor Activity Prediction Project | Zendy

Kamel Mansouri | Zendy; Ahmed Abdelaziz | Zendy; Aleksandra Rybacka | Zendy; Alessandra Roncaglioni | Zendy; Alexander Tropsha | Zendy; Alexandre Varnek | Zendy; Alexey Zakharov | Zendy; Andrew Worth | Zendy; Ann M. Richard | Zendy; Chris Grulke | Zendy; Daniela Trisciuzzi | Zendy; Denis Fourches | Zendy; Dragos Horvath | Zendy; Emilio Benfenati | Zendy; Eugene Muratov | Zendy; Eva Bay Wedebye | Zendy; Francesca Grisoni | Zendy; Giuseppe Felice Mangiatordi | Zendy; Giuseppina Maria Incisivo | Zendy; Huixiao Hong | Zendy; Hui Wen Ng | Zendy; Igor V. Tetko | Zendy; Ilya A. Balabin | Zendy; Jayaram Kancherla | Zendy; Jie Shen | Zendy; J. S. Burton | Zendy; Marc C. Nicklaus | Zendy; Matteo Cassotti | Zendy; Nikolai Georgiev Nikolov | Zendy; Orazio Nicolotti | Zendy; Patrik L. Andersson | Zendy; Qingda Zang | Zendy; Regina Politi | Zendy; Richard D. Beger | Zendy; Roberto Todeschini | Zendy; Ruili Huang | Zendy; Sherif Farag | Zendy; Sine Abildgaard Rosenberg | Zendy; Svetoslav Slavov | Zendy; Xin Hu | Zendy; Richard Judson | Zendy

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CERAPP: Collaborative Estrogen Receptor Activity Prediction Project

Author(s) -

Kamel Mansouri,

Ahmed Abdelaziz,

Aleksandra Rybacka,

Alessandra Roncaglioni,

Alexander Tropsha,

Alexandre Varnek,

Alexey Zakharov,

Andrew Worth,

Ann M. Richard,

Chris Grulke,

Daniela Trisciuzzi,

Denis Fourches,

Dragos Horvath,

Emilio Benfenati,

Eugene Muratov,

Eva Bay Wedebye,

Francesca Grisoni,

Giuseppe Felice Mangiatordi,

Giuseppina Maria Incisivo,

Huixiao Hong,

Hui Wen Ng,

Igor V. Tetko,

Ilya A. Balabin,

Jayaram Kancherla,

Jie Shen,

J. S. Burton,

Marc C. Nicklaus,

Matteo Cassotti,

Nikolai Georgiev Nikolov,

Orazio Nicolotti,

Patrik L. Andersson,

Qingda Zang,

Regina Politi,

Richard D. Beger,

Roberto Todeschini,

Ruili Huang,

Sherif Farag,

Sine Abildgaard Rosenberg,

Svetoslav Slavov,

Xin Hu,

Richard Judson

Publication year - 2016

Publication title -

environmental health perspectives

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.257

H-Index - 282

eISSN - 1552-9924

pISSN - 0091-6765

DOI - 10.1289/ehp.1510267

Subject(s) - categorical variable , endocrine disruptor , oestrogen receptor , weighting , computer science , applicability domain , estrogen receptor , quantitative structure–activity relationship , predictive modelling , machine learning , computational biology , estrogen receptor alpha , data mining , endocrine system , hormone , biology , medicine , endocrinology , breast cancer , radiology , cancer

Humans are exposed to thousands of man-made chemicals in the environment. Some chemicals mimic natural endocrine hormones and, thus, have the potential to be endocrine disruptors. Most of these chemicals have never been tested for their ability to interact with the estrogen receptor (ER). Risk assessors need tools to prioritize chemicals for evaluation in costly in vivo tests, for instance, within the U.S. EPA Endocrine Disruptor Screening Program.

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