Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

Ancuceanu, Robert; Hovanet, Marilena Viorica; Anghel, Adriana Iuliana; Furtunescu, Florentina; Neagu, Monica; Constantin, Carolina; Dinu, Mihaela

doi:10.3390/ijms21062114

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Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

Robert Ancuceanu

¹,

Marilena Viorica Hovanet

^1,*,

Adriana Iuliana Anghel

¹,

Florentina Furtunescu

²,

Monica Neagu

^3,4,5

Carolina Constantin

^3,4 and

Mihaela Dinu

Faculty of Pharmacy, Carol Davila University of Medicine and Pharmacy, 020956 Bucharest, Romania

Faculty of Medicine, Carol Davila University of Medicine and Pharmacy, 020956 Bucharest, Romania

Immunology Laboratory, Victor Babes National Institute of Pathology, 050096 Bucharest, Romania

⁴

Department of Pathology, Colentina University Hospital, 020125 Bucharest, Romania

⁵

Faculty of Biology, University of Bucharest, 76201 Bucharest, Romania

Author to whom correspondence should be addressed.

Int. J. Mol. Sci. 2020, 21(6), 2114; https://doi.org/10.3390/ijms21062114

Received: 12 February 2020 / Revised: 13 March 2020 / Accepted: 17 March 2020 / Published: 19 March 2020

(This article belongs to the Section Molecular Informatics)

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Abstract

Drug-induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized and candidate drugs, and predicting hepatotoxicity from the chemical structure of a substance remains a task worth pursuing. Such an approach is coherent with the current tendency for replacing non-clinical tests with in vitro or in silico alternatives. In 2016, a group of researchers from the FDA published an improved annotated list of drugs with respect to their DILI risk, constituting “the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans” (DILIrank). This paper is one of the few attempting to predict liver toxicity using the DILIrank dataset. Molecular descriptors were computed with the Dragon 7.0 software, and a variety of feature selection and machine learning algorithms were implemented in the R computing environment. Nested (double) cross-validation was used to externally validate the models selected. A total of 78 models with reasonable performance were selected and stacked through several approaches, including the building of multiple meta-models. The performance of the stacked models was slightly superior to other models published. The models were applied in a virtual screening exercise on over 100,000 compounds from the ZINC database and about 20% of them were predicted to be non-hepatotoxic. View Full-Text

Keywords: DILIrank; DILI; drug hepatotoxicity; QSAR; nested cross-validation; virtual screening; in silico

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Ancuceanu, R.; Hovanet, M.V.; Anghel, A.I.; Furtunescu, F.; Neagu, M.; Constantin, C.; Dinu, M. Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset. Int. J. Mol. Sci. 2020, 21, 2114. https://doi.org/10.3390/ijms21062114

AMA Style

Ancuceanu R, Hovanet MV, Anghel AI, Furtunescu F, Neagu M, Constantin C, Dinu M. Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset. International Journal of Molecular Sciences. 2020; 21(6):2114. https://doi.org/10.3390/ijms21062114

Chicago/Turabian Style

Ancuceanu, Robert, Marilena V. Hovanet, Adriana I. Anghel, Florentina Furtunescu, Monica Neagu, Carolina Constantin, and Mihaela Dinu. 2020. "Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset" International Journal of Molecular Sciences 21, no. 6: 2114. https://doi.org/10.3390/ijms21062114

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Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

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