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

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 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 fe

SUBMITTER: Hinnerichs T 

PROVIDER: S-EPMC8665763 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature