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Machine Learning Methods in Computational Toxicology

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Computational Toxicology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1800))

Abstract

Various methods of machine learning, supervised and unsupervised, linear and nonlinear, classification and regression, in combination with various types of molecular descriptors, both “handcrafted” and “data-driven,” are considered in the context of their use in computational toxicology. The use of multiple linear regression, variants of naïve Bayes classifier, k-nearest neighbors, support vector machine, decision trees, ensemble learning, random forest, several types of neural networks, and deep learning is the focus of attention of this review. The role of fragment descriptors, graph mining, and graph kernels is highlighted. The application of unsupervised methods, such as Kohonen’s self-organizing maps and related approaches, which allow for combining predictions with data analysis and visualization, is also considered. The necessity of applying a wide range of machine learning methods in computational toxicology is underlined.

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Authors and Affiliations

  1. Faculty of Physics, M.V. Lomonosov Moscow State University, Moscow, Russian Federation

    Igor I. Baskin

  2. Butlerov Institute of Chemistry, Kazan Federal University, Kazan, Russian Federation

    Igor I. Baskin

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  1. Igor I. Baskin
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  1. Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, Bari, Italy

    Orazio Nicolotti

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Baskin, I.I. (2018). Machine Learning Methods in Computational Toxicology. In: Nicolotti, O. (eds) Computational Toxicology. Methods in Molecular Biology, vol 1800. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7899-1_5

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  • DOI: https://doi.org/10.1007/978-1-4939-7899-1_5

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