To Örebro University

oru.seÖrebro University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors
Center for Bioinformatics (ZBH), Department of Informatics, Universität Hamburg, Hamburg, Germany; HITeC e.V., Hamburg, Germany.
Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of Vienna, Vienna, Austria.
Center for Bioinformatics (ZBH), Department of Informatics, Universität Hamburg, Hamburg, Germany.
Computational Biology Unit (CBU), Department of Chemistry, University of Bergen, Bergen, Norway.
Show others and affiliations
2021 (English)In: Pharmaceuticals, E-ISSN 1424-8247, Vol. 14, no 8, article id 790Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
MDPI, 2021. Vol. 14, no 8, article id 790
Keywords [en]
Bioactivity descriptors, conformal prediction, in silico prediction, machine learning, random forest, skin sensitization, toxicity prediction
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:oru:diva-93959DOI: 10.3390/ph14080790ISI: 000689893400001PubMedID: 34451887Scopus ID: 2-s2.0-85113182766OAI: oai:DiVA.org:oru-93959DiVA, id: diva2:1589332
Note

Funding agencies:

Trond Mohn Foundation BFS2017TMT01

German Research Foundation (DFG)KI 2085/1-1  

Beiersdorf AG through HITeC e.V.

Available from: 2021-08-31 Created: 2021-08-31 Last updated: 2024-01-16Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textPubMedScopus

Authority records

Norinder, Ulf

Search in DiVA

By author/editor
Norinder, Ulf
By organisation
School of Science and Technology
In the same journal
Pharmaceuticals
Bioinformatics (Computational Biology)

Search outside of DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 14 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf