Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors

Anke Wilm, Marina Garcia de Lomana, Conrad Stork, Neann Mathai, Steffen Hirte, Ulf Norinder, Jochen Kühnl, Johannes Kirchmair

Publications: Contribution to journalArticlePeer Reviewed

Abstract

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.
Original languageEnglish
Article number790
Number of pages21
JournalPharmaceuticals
Volume14
Issue number8
DOIs
Publication statusPublished - Aug 2021

Austrian Fields of Science 2012

  • 106005 Bioinformatics
  • 301207 Pharmaceutical chemistry

Keywords

  • APPLICABILITY DOMAIN
  • BINARY
  • CONFORMAL PREDICTION
  • HAZARD
  • LYMPH-NODE ASSAY
  • PART
  • POTENCY
  • STRATEGY
  • bioactivity descriptors
  • conformal prediction
  • in silico prediction
  • machine learning
  • random forest
  • skin sensitization
  • toxicity prediction
  • Skin sensitization
  • Random forest
  • Toxicity prediction
  • In silico prediction
  • Machine learning
  • Bioactivity descriptors
  • Conformal prediction

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