In Silico Prediction of Physicochemical Properties of Environmental Chemicals Using Molecular Fingerprints and Machine Learning-Reference-Cited by-同舟云学术

In Silico Prediction of Physicochemical Properties of Environmental Chemicals Using Molecular Fingerprints and Machine Learning

Published:2017-01-09 Issue:1 Volume:57 Page:36-49
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Zang Qingda¹^ORCID,Mansouri Kamel²^ORCID,Williams Antony J.²,Judson Richard S.²,Allen David G.¹,Casey Warren M.³,Kleinstreuer Nicole C.³^ORCID

Affiliation:

1. Integrated Laboratory Systems, Inc., Research Triangle Park, North Carolina 27709, United States

2. National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States

3. National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina 27709, United States

Funder

National Institute of Environmental Health Sciences

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.6b00625

Reference67 articles.

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2. Chemicals under the Toxic Substances Control Act (TSCA).https://www.epa.gov/chemicals-under-tsca(accessed December 16, 2016) .

3. The exposure data landscape for manufactured chemicals

4. Aggregating Data for Computational Toxicology Applications: The U.S. Environmental Protection Agency (EPA) Aggregated Computational Toxicology Resource (ACToR) System

5. ACToR — Aggregated Computational Toxicology Resource

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