CADRE-SS, an in Silico Tool for Predicting Skin Sensitization Potential Based on Modeling of Molecular Interactions-Reference-Cited by-同舟云学术

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CADRE-SS, an in Silico Tool for Predicting Skin Sensitization Potential Based on Modeling of Molecular Interactions

Published:2015-12-21 Issue:1 Volume:29 Page:58-64
ISSN:0893-228X
Container-title:Chemical Research in Toxicology
language:en
Short-container-title:Chem. Res. Toxicol.

Author:

Kostal Jakub¹²,Voutchkova-Kostal Adelina³

Affiliation:

1. Computational Biology Institute, The George Washington University, 45085 University Drive Suite 305, Ashburn, Virginia 20147, United States

2. DOT Consulting LLC, 113 South Columbus Street Suite 100, Alexandria, Virginia 22314, United States

3. Department of Chemistry, The George Washington University, 800 22nd Street Northwest, Washington, D.C. 20052, United States

Funder

Division of Industrial Innovation and Partnerships

Publisher

American Chemical Society (ACS)

Subject

Toxicology,General Medicine

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.chemrestox.5b00392

Reference44 articles.

1. Allergic contact dermatitis: epidemiology, molecular mechanisms, in vitro methods and regulatory aspects

2. Guiding principles for the implementation of non-animal safety assessment approaches for cosmetics: Skin sensitisation

3. Assessing skin sensitization hazard in mice and men using non-animal test methods

4. Computer models versus reality: How well do in silico models currently predict the sensitization potential of a substance

5. Skin Sensitization: Reaction Mechanistic Applicability Domains for Structure−Activity Relationships

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1. Molecular similarity in chemical informatics and predictive toxicity modeling: from quantitative read-across (q-RA) to quantitative read-across structure–activity relationship (q-RASAR) with the application of machine learning;Critical Reviews in Toxicology;2024-09-03

2. Quantum-Mechanics Calculations Elucidate Skin-Sensitizing Pharmaceutical Compounds;Chemical Research in Toxicology;2024-07-29

3. Development of a hierarchical support vector regression-based in silico model for the prediction of the cysteine depletion in DPRA;Toxicology;2024-03

4. Low-cost quantum mechanical descriptors for data efficient skin sensitization QSAR models;Current Research in Toxicology;2024

5. In Silico Design Vectoring for Safer Pesticides;ACS Symposium Series;2023-10-02

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