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Open AccessArticle

Skin Doctor: Machine Learning Models for Skin Sensitization Prediction that Provide Estimates and Indicators of Prediction Reliability

1
Center for Bioinformatics, Universität Hamburg, 20146 Hamburg, Germany
2
HITeC e.V, 22527 Hamburg, Germany
3
Department of Chemistry, University of Bergen, 5020 Bergen, Norway
4
Computational Biology Unit (CBU), University of Bergen, 5020 Bergen, Norway
5
Front End Innovation, Beiersdorf AG, 20253 Hamburg, Germany
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2019, 20(19), 4833; https://doi.org/10.3390/ijms20194833
Received: 27 August 2019 / Revised: 17 September 2019 / Accepted: 18 September 2019 / Published: 28 September 2019
(This article belongs to the Special Issue QSAR and Chemoinformatics Tools for Modeling)
The ability to predict the skin sensitization potential of small organic molecules is of high importance to the development and safe application of cosmetics, drugs and pesticides. One of the most widely accepted methods for predicting this hazard is the local lymph node assay (LLNA). The goal of this work was to develop in silico models for the prediction of the skin sensitization potential of small molecules that go beyond the state of the art, with larger LLNA data sets and, most importantly, a robust and intuitive definition of the applicability domain, paired with additional indicators of the reliability of predictions. We explored a large variety of molecular descriptors and fingerprints in combination with random forest and support vector machine classifiers. The most suitable models were tested on holdout data, on which they yielded competitive performance (Matthews correlation coefficients up to 0.52; accuracies up to 0.76; areas under the receiver operating characteristic curves up to 0.83). The most favorable models are available via a public web service that, in addition to predictions, provides assessments of the applicability domain and indicators of the reliability of the individual predictions. View Full-Text
Keywords: skin sensitization potential; prediction; in silico models; machine learning; local lymph node assay (LLNA); cosmetics; drugs; pesticides; chemical space; applicability domain skin sensitization potential; prediction; in silico models; machine learning; local lymph node assay (LLNA); cosmetics; drugs; pesticides; chemical space; applicability domain
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MDPI and ACS Style

Wilm, A.; Stork, C.; Bauer, C.; Schepky, A.; Kühnl, J.; Kirchmair, J. Skin Doctor: Machine Learning Models for Skin Sensitization Prediction that Provide Estimates and Indicators of Prediction Reliability. Int. J. Mol. Sci. 2019, 20, 4833. https://doi.org/10.3390/ijms20194833

AMA Style

Wilm A, Stork C, Bauer C, Schepky A, Kühnl J, Kirchmair J. Skin Doctor: Machine Learning Models for Skin Sensitization Prediction that Provide Estimates and Indicators of Prediction Reliability. International Journal of Molecular Sciences. 2019; 20(19):4833. https://doi.org/10.3390/ijms20194833

Chicago/Turabian Style

Wilm, Anke; Stork, Conrad; Bauer, Christoph; Schepky, Andreas; Kühnl, Jochen; Kirchmair, Johannes. 2019. "Skin Doctor: Machine Learning Models for Skin Sensitization Prediction that Provide Estimates and Indicators of Prediction Reliability" Int. J. Mol. Sci. 20, no. 19: 4833. https://doi.org/10.3390/ijms20194833

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