In silicoestimation of chemical aquatic toxicity on crustaceans using chemical category methods-Reference-Cited by-同舟云学术

In silicoestimation of chemical aquatic toxicity on crustaceans using chemical category methods

Published:2018 Issue:9 Volume:20 Page:1234-1243
ISSN:2050-7887
Container-title:Environmental Science: Processes & Impacts
language:en
Short-container-title:Environ. Sci.: Processes Impacts

Author:

Cao Qianqian¹²³⁴⁵,Liu Lin¹²³⁴⁵,Yang Hongbin¹²³⁴⁵,Cai Yingchun¹²³⁴⁵,Li Weihua¹²³⁴⁵,Liu Guixia¹²³⁴⁵^ORCID,Lee Philip W.¹²³⁴⁵,Tang Yun¹²³⁴⁵^ORCID

Affiliation:

1. Shanghai Key Laboratory of New Drug Design

2. School of Pharmacy

3. East China University of Science and Technology

4. Shanghai 200237

5. China

Abstract

A series ofin silicomodels were developed to estimate chemical acute aquatic toxicity on crustaceans using machine learning methods combined with molecular fingerprints.

Funder

National Natural Science Foundation of China

Publisher

Royal Society of Chemistry (RSC)

Subject

Management, Monitoring, Policy and Law,Public Health, Environmental and Occupational Health,Environmental Chemistry,General Medicine

Link

http://pubs.rsc.org/en/content/articlepdf/2018/EM/C8EM00220G

Reference62 articles.

1. Chemical Abstracts Service Database, http://www.cas.org/

2. The Challenge of Micropollutants in Aquatic Systems

3. Monte Carlo–based quantitative structure–activity relationship models for toxicity of organic chemicals to Daphnia magna

4. Acute aquatic toxicity of organic solvents modeled by QSARs

5. Quantitative Read-Across for Predicting the Acute Fish Toxicity of Organic Compounds

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