Prediction of Metabolic Transformations using Cross Venn-ABERS Predictors

Staffan Arvidsson, Ola Spjuth, Lars Carlsson, Paolo Toccaceli

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Prediction of drug metabolism is an important topic in the drug discovery process, and we here present a study using probabilistic predictions applying Cross Venn-ABERS Predictors (CVAPs) on data for site-of-metabolism. We used a dataset of 73599 biotransformations, applied SMIRKS to define biotransformations of interest and constructed five datasets where chemical structures were represented using signatures descriptors. The results show that CVAP produces well-calibrated predictions for all datasets with good predictive capability, making CVAP an interesting method for further exploration in drug discovery applications.
Original languageEnglish
Title of host publicationProceedings of Machine Learning Research
Number of pages14
Publication statusPublished - 31 May 2017


  • Venn-ABERS
  • Cross Venn-ABERS
  • Site-of-Metabolism
  • Drug discovery
  • Machine Learning
  • Support Vector Machine
  • Signatures descriptor
  • QSAR

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