Read-across-based intelligent learning: development of a global q-RASAR model for the efficient quantitative predictions of skin sensitization potential of diverse organic chemicals-Reference-Cited by-同舟云学术

Read-across-based intelligent learning: development of a global q-RASAR model for the efficient quantitative predictions of skin sensitization potential of diverse organic chemicals

Published:2023 Issue:10 Volume:25 Page:1626-1644
ISSN:2050-7887
Container-title:Environmental Science: Processes & Impacts
language:en
Short-container-title:Environ. Sci.: Processes Impacts

Author:

Banerjee Arkaprava¹^ORCID,Roy Kunal¹^ORCID

Affiliation:

1. Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India

Abstract

This computational work presents a novel similarity-based q-RASAR approach for the development of a global model that can quantitatively and efficiently identify the skin sensitization potential of hazardous chemicals.

Funder

Defence Research and Development Organisation

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/2023/EM/D3EM00322A

Reference69 articles.

1. In silico research in drug discovery

2. In Silico Toxicology – Non-Testing Methods

3. In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways

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