DTI-Voodoo: machine learning over interaction networks and ontology-based background knowledge predicts drug–target interactions

Tilman Hinnerichs, Robert Hoehndorf

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Motivation In silico drug–target interaction (DTI) prediction is important for drug discovery and drug repurposing. Approaches to predict DTIs can proceed indirectly, top-down, using phenotypic effects of drugs to identify potential drug targets, or they can be direct, bottom-up and use molecular information to directly predict binding affinities. Both approaches can be combined with information about interaction networks. Results We developed DTI-Voodoo as a computational method that combines molecular features and ontology-encoded phenotypic effects of drugs with protein–protein interaction networks, and uses a graph convolutional neural network to predict DTIs. We demonstrate that drug effect features can exploit information in the interaction network whereas molecular features do not. DTI-Voodoo is designed to predict candidate drugs for a given protein; we use this formulation to show that common DTI datasets contain intrinsic biases with major effects on performance evaluation and comparison of DTI prediction methods. Using a modified evaluation scheme, we demonstrate that DTI-Voodoo improves significantly over state of the art DTI prediction methods.
Original languageEnglish (US)
StatePublished - Jul 28 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-08-05
Acknowledged KAUST grant number(s): URF/1/3790-01-01 and URF/1/4355-01-01
Acknowledgements: This work was supported by funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR)
under Award No. URF/1/3790-01-01 and URF/1/4355-01-01.

ASJC Scopus subject areas

  • Biochemistry
  • Computational Theory and Mathematics
  • Computational Mathematics
  • Molecular Biology
  • Statistics and Probability
  • Computer Science Applications


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