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Molecular Fingerprint-Derived Similarity Measures for Toxicological Read-Across: Recommendations for Optimal Use

Mellor, CL, Marchese Robinson, RL, Benigni, R, Ebbrell, DJ, Enoch, SJ, Firman, JW, Madden, JC, Pawar, G, Yang, C and Cronin, MTD Molecular Fingerprint-Derived Similarity Measures for Toxicological Read-Across: Recommendations for Optimal Use. Regulatory Toxicology and Pharmacology. ISSN 0273-2300 (Accepted)

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Abstract

Computational approaches are increasingly used to predict toxicity, in part due to pressures to find alternatives to animal testing. Read-across is the “new paradigm” which aims to predict toxicity by identifying similar, data rich, source compounds. This assumes that similar molecules tend to exhibit similar activities, i.e. molecular similarity is integral to read-across. Various molecular fingerprints and similarity measures may be used to calculate molecular similarity. This study investigated the value and concordance of the Tanimoto similarity values calculated using six widely used fingerprints within six toxicological datasets. There was considerable variability in the similarity values calculated from the various molecular fingerprints for diverse compounds, although they were reasonably concordant for homologous series acting via a common mechanism. The results suggest generic fingerprint-derived similarities are likely to be optimally predictive for local datasets, i.e. following sub-categorisation. Thus, for read-across, generic fingerprint-derived similarities are likely to be most predictive after chemicals are placed into categories (or groups), then similarity is calculated within those categories, rather than for a whole chemically diverse dataset.

Item Type: Article
Uncontrolled Keywords: 1115 Pharmacology And Pharmaceutical Sciences
Subjects: R Medicine > RM Therapeutics. Pharmacology
Divisions: Pharmacy & Biomolecular Sciences
Publisher: Elsevier
Date Deposited: 16 Nov 2018 11:58
Last Modified: 17 Nov 2018 19:15
URI: http://researchonline.ljmu.ac.uk/id/eprint/9666

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