Chemical toxicity prediction for major classes of industrial chemicals: Is it possible to develop universal models covering cosmetics, drugs, and pesticides?-Reference-Cited by-同舟云学术

Chemical toxicity prediction for major classes of industrial chemicals: Is it possible to develop universal models covering cosmetics, drugs, and pesticides?

Published:2018-02 Issue: Volume:112 Page:526-534
ISSN:0278-6915
Container-title:Food and Chemical Toxicology
language:en
Short-container-title:Food and Chemical Toxicology

Author:

Alves Vinicius M.,Muratov Eugene N.^ORCID,Zakharov Alexey,Muratov Nail N.,Andrade Carolina H.,Tropsha Alexander

Funder

NIH

EPA

European Union

FAPEG

CNPq

CAPES

National Center for Advancing Translational Sciences, National Institutes of Health

Publisher

Elsevier BV

Subject

Toxicology,General Medicine,Food Science

Reference57 articles.

1. QSAR models of human data can enrich or replace LLNA testing for human skin sensitization;Alves;Green Chem.,2016

2. Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds;Alves;Toxicol. Appl. Pharmacol.,2015

3. Predicting chemically-induced skin reactions. Part II: QSAR models of skin permeability and the relationships between skin permeability and skin sensitization;Alves;Toxicol. Appl. Pharmacol.,2015

4. Predictive models for carcinogenicity and mutagenicity: frameworks, state-of-the-art, and perspectives;Benfenati;J. Environ. Sci. Health. C. Environ. Carcinog. Ecotoxicol. Rev.,2009

5. Multiscale modelling approaches for assessing cosmetic ingredients safety;Bois;Toxicology,2016

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