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-Reference-Cited by-同舟云学术

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

Published:2024-09-03 Issue: Volume: Page:1-26
ISSN:1040-8444
Container-title:Critical Reviews in Toxicology
language:en
Short-container-title:Critical Reviews in Toxicology

Author:

Banerjee Arkaprava¹^ORCID,Kar Supratik²^ORCID,Roy Kunal¹^ORCID,Patlewicz Grace³^ORCID,Charest Nathaniel³^ORCID,Benfenati Emilio⁴^ORCID,Cronin Mark T. D.⁵^ORCID

Affiliation:

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

2. Department of Chemistry and Physics, Chemometrics & Molecular Modeling Laboratory, Kean University, Union, NJ, USA

3. Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Research Triangle Park, NC, USA

4. Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy

5. School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK

Funder

Life Science Research Board (DRDO), New Delhi

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10408444.2024.2386260

Reference177 articles.

1. The use of systems biology in chemical risk assessment

2. Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations?

3. Toward Good Read-Across Practice (GRAP) guidance

4. Key read across framework components and biology based improvements

5. Quantitative predictions from chemical read-across and their confidence measures

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. The application of chemical similarity measures in an unconventional modeling framework c-RASAR along with dimensionality reduction techniques to a representative hepatotoxicity dataset;Scientific Reports;2024-09-06