High Throughput Read-Across for Screening a Large Inventory of Related Structures by Balancing Artificial Intelligence/Machine Learning and Human Knowledge-Reference-Cited by-同舟云学术

High Throughput Read-Across for Screening a Large Inventory of Related Structures by Balancing Artificial Intelligence/Machine Learning and Human Knowledge

Published:2023-07-03 Issue:7 Volume:36 Page:1081-1106
ISSN:0893-228X
Container-title:Chemical Research in Toxicology
language:en
Short-container-title:Chem. Res. Toxicol.

Author:

Yang Chihae¹^ORCID,Rathman James F.²³^ORCID,Mostrag Aleksandra²,Ribeiro João Vinícius²,Hobocienski Bryan²,Magdziarz Tomasz¹,Kulkarni Sunil⁴,Barton-Maclaren Tara⁴

Affiliation:

1. MN-AM, 90411 Nürnberg, Germany

2. MN-AM, Columbus, Ohio 43215, United States

3. Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio 43210, United States

4. Existing Substances Risk Assessment Bureau, Health Canada, Ottawa, ON K1A 0K9, Canada

Publisher

American Chemical Society (ACS)

Subject

Toxicology,General Medicine

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.chemrestox.3c00062

Reference88 articles.

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2. Evaluation of the applicability of existing (Q)SAR models for predicting the genotoxicity of pesticides and similarity analysis related with genotoxicity of pesticides for facilitating of grouping and read across: An EFSA funded project

3. European Chemical Agency (ECHA) Read-Across Assessment Framework (RAAF). ECHA-17-R-01-EN. 2017, https://echa.europa.eu/documents/10162/13628/raaf_en.pdf/614e5d61-891d-4154-8a47-87efebd1851a (last accessed Jan 2023).

4. Paving the way for application of next generation risk assessment to safety decision-making for cosmetic ingredients

5. New Approach Methodologies in Regulatory Science. April 2016. https://echa.europa.eu/documents/10162/22816069/scientific_ws_proceedings_en.pdf (last accessed Jan 2023).

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