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Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors
In recent years, a number of machine learning models for the prediction of the skin sensitization potential of small organic molecules have been reported and become available. These models generally perform well within their applicability domains but, as a result of the use of molecular fingerprints...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8402010/ https://www.ncbi.nlm.nih.gov/pubmed/34451887 http://dx.doi.org/10.3390/ph14080790 |
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author | Wilm, Anke Garcia de Lomana, Marina Stork, Conrad Mathai, Neann Hirte, Steffen Norinder, Ulf Kühnl, Jochen Kirchmair, Johannes |
author_facet | Wilm, Anke Garcia de Lomana, Marina Stork, Conrad Mathai, Neann Hirte, Steffen Norinder, Ulf Kühnl, Jochen Kirchmair, Johannes |
author_sort | Wilm, Anke |
collection | PubMed |
description | In recent years, a number of machine learning models for the prediction of the skin sensitization potential of small organic molecules have been reported and become available. These models generally perform well within their applicability domains but, as a result of the use of molecular fingerprints and other non-intuitive descriptors, the interpretability of the existing models is limited. The aim of this work is to develop a strategy to replace the non-intuitive features by predicted outcomes of bioassays. We show that such replacement is indeed possible and that as few as ten interpretable, predicted bioactivities are sufficient to reach competitive performance. On a holdout data set of 257 compounds, the best model (“Skin Doctor CP:Bio”) obtained an efficiency of 0.82 and an MCC of 0.52 (at the significance level of 0.20). Skin Doctor CP:Bio is available free of charge for academic research. The modeling strategies explored in this work are easily transferable and could be adopted for the development of more interpretable machine learning models for the prediction of the bioactivity and toxicity of small organic compounds. |
format | Online Article Text |
id | pubmed-8402010 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-84020102021-08-29 Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors Wilm, Anke Garcia de Lomana, Marina Stork, Conrad Mathai, Neann Hirte, Steffen Norinder, Ulf Kühnl, Jochen Kirchmair, Johannes Pharmaceuticals (Basel) Article In recent years, a number of machine learning models for the prediction of the skin sensitization potential of small organic molecules have been reported and become available. These models generally perform well within their applicability domains but, as a result of the use of molecular fingerprints and other non-intuitive descriptors, the interpretability of the existing models is limited. The aim of this work is to develop a strategy to replace the non-intuitive features by predicted outcomes of bioassays. We show that such replacement is indeed possible and that as few as ten interpretable, predicted bioactivities are sufficient to reach competitive performance. On a holdout data set of 257 compounds, the best model (“Skin Doctor CP:Bio”) obtained an efficiency of 0.82 and an MCC of 0.52 (at the significance level of 0.20). Skin Doctor CP:Bio is available free of charge for academic research. The modeling strategies explored in this work are easily transferable and could be adopted for the development of more interpretable machine learning models for the prediction of the bioactivity and toxicity of small organic compounds. MDPI 2021-08-11 /pmc/articles/PMC8402010/ /pubmed/34451887 http://dx.doi.org/10.3390/ph14080790 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wilm, Anke Garcia de Lomana, Marina Stork, Conrad Mathai, Neann Hirte, Steffen Norinder, Ulf Kühnl, Jochen Kirchmair, Johannes Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors |
title | Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors |
title_full | Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors |
title_fullStr | Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors |
title_full_unstemmed | Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors |
title_short | Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors |
title_sort | predicting the skin sensitization potential of small molecules with machine learning models trained on biologically meaningful descriptors |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8402010/ https://www.ncbi.nlm.nih.gov/pubmed/34451887 http://dx.doi.org/10.3390/ph14080790 |
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