Discovering protein drug targets using knowledge graph embeddings | Zendy

Sameh K. Mohamed | Zendy; Vít Nováček | Zendy; Aayah Nounu | Zendy

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Discovering protein drug targets using knowledge graph embeddings

Author(s) -

Sameh K. Mohamed,

Vít Nováček,

Aayah Nounu

Publication year - 2019

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/btz600

Subject(s) - computer science , machine learning , knowledge graph , embedding , graph , benchmark (surveying) , artificial intelligence , drug repositioning , drug discovery , data mining , theoretical computer science , drug , bioinformatics , psychology , geodesy , psychiatry , biology , geography

Computational approaches for predicting drug-target interactions (DTIs) can provide valuable insights into the drug mechanism of action. DTI predictions can help to quickly identify new promising (on-target) or unintended (off-target) effects of drugs. However, existing models face several challenges. Many can only process a limited number of drugs and/or have poor proteome coverage. The current approaches also often suffer from high false positive prediction rates.

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