Banerjee, A, Kar, S, Roy, K, Patlewicz, G, Charest, N, Benfenati, E and Cronin, M 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. ISSN 1040-8444 (Accepted)

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Abstract

This article aims to provide a comprehensive, critical yet readable review of general interest to the chemistry community on molecular similarity as applied to chemical informatics and predictive modeling with a special focus on read-across (RA) and read-across structure-activity relationships (RASAR). Molecular similarity-based computational tools, such as Quantitative Structure-Activity Relationship (QSAR) and RA, are routinely used to fill the data gaps for a wide range of properties including toxicity endpoints for regulatory purposes. This review will explore the background of RA starting from how structural information has been used through to how other similarity contexts such as physicochemical, ADME properties, and biological aspects are being characterized. More recent developments of its integration with QSAR have resulted in the emergence of novel models such as ToxRead, Generalized Read-Across (GenRA), and quantitative RASAR (q-RASAR). Conventional QSAR techniques have been excluded from this review except where necessary for context.

Item Type:	Article
Uncontrolled Keywords:	1115 Pharmacology and Pharmaceutical Sciences; Toxicology
Subjects:	Q Science > QD Chemistry R Medicine > RA Public aspects of medicine > RA1190 Toxicology. Poisions
Divisions:	Pharmacy & Biomolecular Sciences
Publisher:	Taylor and Francis Group
SWORD Depositor:	A Symplectic
Date Deposited:	12 Aug 2024 10:41
Last Modified:	12 Aug 2024 10:41
DOI or ID number:	10.1080/10408444.2024.2386260
URI:	https://researchonline.ljmu.ac.uk/id/eprint/23907

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