Abstract
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 potentials. 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 affects 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.
Availability DTI-Voodoo source code and data necessary to reproduce results are freely available at https://github.com/THinnerichs/DTI-VOODOO.
Contact tilman.hinnerichs{at}kaust.edu.sa
Supplementary information Supplementary data are available at https://github.com/THinnerichs/DTI-VOODOO.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
In this updated version we provide more in depth analysis of the results provided by DTI-Voodoo. We therefor analyze the effects of different interaction types on the models performance and add an enrichment analysis for newly predicted DTI pairs in section 4.3.