Guidance for good practice in the application of machine learning in development of toxicological quantitative structure-activity relationships (QSARs)

Recent years have seen a substantial growth in the adoption of machine learning approaches for the purposes of quantitative structure-activity relationship (QSAR) development. Such a trend has coincided with desire to see a shifting in the focus of methodology employed within chemical safety assessm...

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Bibliographic Details
Published in:	PloS one Vol. 18; no. 5; p. e0282924
Main Authors:	Belfield, Samuel J., Cronin, Mark T.D., Enoch, Steven J., Firman, James W.
Format:	Journal Article
Language:	English
Published:	United States Public Library of Science 10.05.2023
Subjects:	Algorithms Analysis Animals Best practice Biocompatibility Biology and Life Sciences Computer and Information Sciences Datasets Decision trees Learning algorithms Machine Learning Medicine and Health Sciences Mitigation Neural networks Optimization Pattern recognition Physical Sciences Principles Quantitative Structure-Activity Relationship Regression analysis Reproducibility of Results Research and Analysis Methods Software Statistical analysis Statistical methods Structure-activity relationships Toxicity Toxicological interactions Toxicology Uncertainty United Kingdom United States > US
ISSN:	1932-6203, 1932-6203
Online Access:	Get full text
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