Comparing Machine Learning Models for Aromatase (P450 19A1)-Reference-Cited by-同舟云学术

Comparing Machine Learning Models for Aromatase (P450 19A1)

Published:2020-11-19 Issue:23 Volume:54 Page:15546-15555
ISSN:0013-936X
Container-title:Environmental Science & Technology
language:en
Short-container-title:Environ. Sci. Technol.

Author:

Zorn Kimberley M.¹,Foil Daniel H.¹,Lane Thomas R.¹,Hillwalker Wendy²,Feifarek David J.²,Jones Frank²,Klaren William D.²,Brinkman Ashley M.²,Ekins Sean¹^ORCID

Affiliation:

1. Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States

2. Global Product Safety, SC Johnson and Son, Inc., Racine, Wisconsin 53404, United States

Funder

National Institute of Environmental Health Sciences

National Institute of General Medical Sciences

Publisher

American Chemical Society (ACS)

Subject

Environmental Chemistry,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.est.0c05771

Reference65 articles.

1. EPA, U.S. EPA Endocrine Disruptor Screening Program Tier 1 Battery of Assays. https://www.epa.gov/endocrine-disruption/endocrine-disruptor-screening-program-tier-1-battery-assays.

2. Integrated Model of Chemical Perturbations of a Biological Pathway Using 18In VitroHigh-Throughput Screening Assays for the Estrogen Receptor

3. Screening Chemicals for Estrogen Receptor Bioactivity Using a Computational Model

4. Development and Validation of a Computational Model for Androgen Receptor Activity

5. OPERA models for predicting physicochemical properties and environmental fate endpoints

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