Hinweise zu Qualitätskriterien von Zeitschriften
Open-Access-Richtlinie der Helmholtz-Gemeinschaft 2016
CC Licencences
Metriken für Publikationen
Startseite
Login
English
Neuer EVA Antrag
Erweiterte Suche
Durchblättern nach ...
Publikationen im Überblick
HGF Fortschrittsbericht
OA Publikationen
Neue Publikation eintragen
Neue Publikation holen aus...
Fehlende Publikation melden
Ansprechpartner
Hilfe
Bibliometrische Indikatoren
Text
Endnote (RIS)
BibTeX
Verlagsversion
Anhang
DOI
PMC
 |
Open Access Gold |
|
möglich sobald bei der ZB eingereicht worden ist.
CERAPP: Collaborative estrogen receptor activity prediction project.
Environ. Health Perspect. 124, 1023-1033 (2016)
BACKGROUND: 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 EPA Endocrine Disruptor Screening Program (EDSP). OBJECTIVES: Here, we describe a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) and demonstrate the efficacy of using predictive computational models trained on high-throughput screening data to evaluate thousands of chemicals for ER-related activity and prioritize them for further testing. METHODS: CERAPP combined multiple models developed in collaboration among 17 groups in the United States and Europe to predict ER activity of a common set of 32,464 chemical structures. Quantitative structure-activity relationship models and docking approaches were employed, mostly using a common training set of 1677 chemical structures provided by US EPA, to build a total of 40 categorical and 8 continuous models for binding, agonist, and antagonist ER activity. All predictions were evaluated on a set of 7,522 chemicals curated from the literature. To overcome the limitations of single models, a consensus was built by weighting models on scores based on their evaluated accuracies. RESULTS: Individual model scores ranged from 0.69 to 0.85, showing high prediction reliabilities. Out of the 32,464 chemicals, the consensus model predicted 4,001 chemicals (12.3%) as high priority actives and 6,742 potential actives (20.8%) to be considered for further testing. CONCLUSION: This project demonstrated the possibility to screen large libraries of chemicals using a consensus of different in silico approaches. This concept will be applied in future projects related to other endpoints.
Impact Factor
Scopus SNIP
Web of Science
Times Cited
Times Cited
Scopus
Cited By
Cited By
Altmetric
8.443
2.217
165
188
Anmerkungen
Besondere Publikation
Auf Hompepage verbergern
Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Wissenschaftlicher Artikel
Schlagwörter
Endocrine-disrupting Chemicals; Computational Toxicology Resource; Quantitative Structure-activity; Throughput Screening Assays; Relationship Qsdar Models; Environmental Chemicals; Decision Forest; Partition-coefficients; Binary Classification; Biological-activity
Sprache
englisch
Veröffentlichungsjahr
2016
HGF-Berichtsjahr
2016
ISSN (print) / ISBN
0091-6765
e-ISSN
1552-9924
Zeitschrift
Environmental Health Perspectives
Quellenangaben
Band: 124,
Heft: 7,
Seiten: 1023-1033
Verlag
Research Triangle Park
Verlagsort
NC [u.a.]
Begutachtungsstatus
Peer reviewed
Institut(e)
Institute of Structural Biology (STB)
POF Topic(s)
30203 - Molecular Targets and Therapies
Forschungsfeld(er)
Enabling and Novel Technologies
PSP-Element(e)
G-503000-003
WOS ID
WOS:000380749300025
Scopus ID
84977138622
PubMed ID
26908244
Erfassungsdatum
2016-02-27