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

Tilman Hinnerichs | Zendy; Robert Hoehndorf | Zendy

Open Access

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

Author(s) -

Tilman Hinnerichs,

Robert Hoehndorf

Publication year - 2021

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btab548

Subject(s) - computer science , ontology , artificial intelligence , machine learning , gene ontology , drug , drug drug interaction , psychology , chemistry , psychiatry , biochemistry , gene , philosophy , gene expression , epistemology

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.

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