Machine Learning in Predictive Toxicology: Recent Applications and Future Directions for Classification Models

In recent times, machine learning has become increasingly prominent in predictive toxicology as it has shifted from studies toward studies. Currently, methods together with other computational methods such as quantitative structure-activity relationship modeling and absorption, distribution, metabol...

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Bibliographic Details
Published in:	Chemical research in toxicology Vol. 34; no. 2; p. 217
Main Authors:	Wang, Marcus W H, Goodman, Jonathan M, Allen, Timothy E H
Format:	Journal Article
Language:	English
Published:	United States 15.02.2021
ISSN:	1520-5010, 1520-5010
Online Access:	Get more information
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Summary:	In recent times, machine learning has become increasingly prominent in predictive toxicology as it has shifted from studies toward studies. Currently, methods together with other computational methods such as quantitative structure-activity relationship modeling and absorption, distribution, metabolism, and excretion calculations are being used. An overview of machine learning and its applications in predictive toxicology is presented here, including support vector machines (SVMs), random forest (RF) and decision trees (DTs), neural networks, regression models, naïve Bayes, -nearest neighbors, and ensemble learning. The recent successes of these machine learning methods in predictive toxicology are summarized, and a comparison of some models used in predictive toxicology is presented. In predictive toxicology, SVMs, RF, and DTs are the dominant machine learning methods due to the characteristics of the data available. Lastly, this review describes the current challenges facing the use of machine learning in predictive toxicology and offers insights into the possible areas of improvement in the field.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 ObjectType-Review-3 content type line 23
ISSN:	1520-5010 1520-5010
DOI:	10.1021/acs.chemrestox.0c00316